“Electric vehicle charging station in combination with an ......“Electric vehicle charging station in combination with an external battery and PV system energy management simulator”

Master thesis to obtain the degree of

Master of Science

Renewable Energy Management

“Electric vehicle charging station in combination with an

external

battery and PV system energy management simulator”

Gregorio Vázquez Patrón

Matrikelnr: 10318207

Supervising professor: Prof. Dr. Kerstin Wydra

Second supervisor: Dipl.-Ing. Konrad Uebel

Defense date: 05.06.2019

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Acknowledgements

I would like to thank all persons who supported me during the development of this project.

Prof. Dr. Wydra for always being an exceptional academical representant, permanently

extending her support. Konrad Uebel and Daniel Tittel, from Freiberg Institut GmbH, who

collaborated on the technical aspect of this thesis, particularly in the achievement of a

conceptual structure for this work. Especially, I would like to thank Dr. Jose Luis Espinosa

Mendoza for his remarkable support in the developing of the software. Without his

contribution and constant advising, this project wouldn’t have been achieved.

At the same time, I would like to dedicate this work to everyone who, emotionally, was

present. My mom, who even being far apart, always kept unconditional love and attention.

My sister, who always offered me a hand while being down. My brother, who always stood

behind advising me. To my late dad, who during the process of this thesis, unfortunately,

left this world due to cancer. Without your perpetual advice to leave Mexico and come to

Germany to continue my professional life, this achievement wouldn’t have happened. May

God have you on His side.

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Contents

I List of Tables ................................................................................................................ - 5 -

II List of Figures ............................................................................................................. - 6 -

III Nomenclatures ........................................................................................................... - 9 -

1 Abstract ................................................................................................................. - 10 -

2 Introduction ........................................................................................................... - 12 -

2.1 Energy, transportation and the world .............................................................. - 12 -

2.2 The sun as a power source ............................................................................. - 16 -

2.3 Energy storage ................................................................................................ - 19 -

2.4 Related work ................................................................................................... - 21 -

2.5 Research questions and objectives ................................................................. - 22 -

2.6 Research methodology and assumptions ....................................................... - 23 -

3 Development ......................................................................................................... - 26 -

3.1 General scheme .............................................................................................. - 26 -

3.2 Parameters ...................................................................................................... - 28 -

3.3 PV implementation .......................................................................................... - 29 -

3.4 PV Exemplary data collection .......................................................................... - 31 -

3.5 AC and DC structure ....................................................................................... - 34 -

3.6 Charging Station - RC Circuit - subsystem ...................................................... - 37 -

3.7 Signal builder ................................................................................................... - 41 -

3.8 EVs Battery Routine subsystem ...................................................................... - 42 -

3.9 Buffer Routine subsystem ............................................................................... - 45 -

3.10 PV-Grid-Buffer Ratio subsystem ..................................................................... - 50 -

3.11 Script ............................................................................................................... - 52 -

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4 Exemplary scenarios ............................................................................................ - 53 -

4.1 7,12-13,16 Scenario ........................................................................................ - 53 -

4.2 7-19 Scenario .................................................................................................. - 56 -

4.3 7-16 Scenario .................................................................................................. - 59 -

5 Scenarios’ financial run ....................................................................................... - 62 -

6 Further development and appliances ................................................................. - 66 -

6.1 Carsharing ....................................................................................................... - 67 -

6.2 V2G ................................................................................................................. - 68 -

7 Conclusions .......................................................................................................... - 71 -

8 Bibliography .......................................................................................................... - 73 -

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I List of Tables

Table 1 Pros and cons of Li-Ion battery technology. ..................................................... - 20 -

Table 2 Subsystems and parameterized variables. ....................................................... - 28 -

Table 3 Parameters input in the model's workspace for the 7,12-13,16 scenario. ........ - 54 -

Table 4 Total energy flow in the charging station under the working day’s scenario. .... - 56 -

Table 5 Parameters inputted in the model's workspace for the 7-19 scenario. ............. - 56 -

Table 6 Total energy flow in the charging station under the day’s trip scenario. ........... - 58 -

Table 7 Parameters inputted in the model's workspace for the 7-16 scenario. ............. - 59 -

Table 8 Total energy flow in the charging station under the commute trip-scenario. .... - 61 -

Table 9 Budget sheet with a 9.8 kWh buffer battery system. ........................................ - 63 -

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II List of Figures

Figure 1 Climate balance for small vehicles (after driven for 150,000 km) (Kroher, 2018) ... -

14 -

Figure 2 Electric and hybrid vehicles german market distribution. Electric vehicles (red),

hybrid plug-in vehicles (blue). ................................................................................. - 15 -

Figure 3 Global EV sales throughout the last years (Irle, 2019) .................................... - 15 -

Figure 4. Percentage renewable energy in net electricity consumption for Germany. (Energy,

https://www.bmwi.de, 2017) ................................................................................... - 17 -

Figure 5 Li-Ion historical prices and forecast market. (BNEF, 2018) ............................. - 19 -

Figure 7 Energy flow for each potential situational scenario. ........................................ - 27 -

Figure 9 Normalized power per minute during a year (2011) of data collection ............ - 30 -

Figure 10 PV_System Subsystem ................................................................................. - 30 -

Figure 11 Power generated by the PV data in a year's representative day. .................. - 32 -

Figure 12 Power generated by the PV data in a summer season's representative day. - 33 -

Figure 13 Power generated by the PV data in a winter season’s representative day. .. - 33 -

Figure 14 Simulation of demand profile of EV charger with Continuous Power-Continuous

Voltage option with AC design. (Francesco Marra, 2012) ...................................... - 35 -

Figure 15 Simple RC-Circuit schematic (July Thomas, 2018) ....................................... - 38 -

Figure 16 RC-Circuit model represented in the model. ................................................. - 39 -

Figure 17 Current scope, where in the first steps of the simulation the EV's battery is

requiring power -docked- and the power is exercised in the EV's battery with a delay of

5 mins. The expectation of the power curve was to simulate a real-life powering ratio,

but the obstacle was the 5-min steps pacing, which can’t reproduce the same real

behavior a charging station could have, that’s the main reason why the current/power

behaves linearly in the simulation. .......................................................................... - 40 -

Figure 18 Signal builder block representing a 0-7; 16-24 docking behavior. The expectation

was to represent in 5 mins steps the docking behavior an EV could have in a house-

based charging station. The result is displayed in 5 mins steps. Unfortunately, at the

same time, the model’s output could confuse the user. The author recommends to pay

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close attention to the excel format so that, during the input parameters configuration,

interpretation mistakes could be avoided. .............................................................. - 42 -

Figure 19 EV's Battery Routine Subsystem in the Simulink model................................ - 44 -

Figure 20 Scope from the power integrator. The integrator is a fundamental aspect of the

charging/discharging of both the EV’s battery and the buffer’s battery. It converts the

power signal into energy, which is the unit the project was based on. This scope of that

function just shows how energy is represented in the Simulink’s environment, although

it doesn’t have any further relevance in the interpretation of the eventual results. . - 45 -

Figure 21 The battery buffer routine's subsystem in the Simulink model. ..................... - 47 -

Figure 22 Scope from the buffer battery energy. On the second quarter of the curve, the

energy flowing into the buffer is curve, since the PV system starts receiving solar

irradiance and as the sun transfer, the irradiance becomes higher, which translates into

an exponential behavior. After the EV is docked into the charging station system – last

quarter of the curve- the behavior of the function is linear since the buffer discharges in

a constant matter towards the EV’s battery. The results reassemble the expectation that

was intended for this particular module of the model: to successfully represent the basic

power behavior the buffer battery would have in function of the EV’s battery and the PV

system. ................................................................................................................... - 49 -

Figure 23 The PV-Grid-Buffer Ratio's subsystem in the Simulink model. ...................... - 51 -

Figure 24 EV’s battery's energy representative throughout 24 hrs of a working day with a

small break-scenario. ............................................................................................. - 54 -

Figure 25 Buffer battery's energy representative throughout 24 hrs of a working day with a

small break-scenario. ............................................................................................. - 55 -


small break-scenario, where positive values is energy produced by the PV system flown

into the buffer battery while the negative values are flown into the grid. ................ - 55 -

Figure 27 EV’s battery's energy representative throughout 24 hrs of a day’s trip-scenario. . -

57 -

Figure 28 Buffer battery's energy representative throughout 24 hrs of a day’s trip-scenario.

................................................................................................................................ - 57 -

Figure 29 Buffer battery's energy representative throughout 24hrs of a day's trip-scenario,

where positive values is energy produced by the PV system flown into the buffer battery

while the negative values are flown into the grid. ................................................... - 58 -

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Figure 30 EV’s battery's energy representative throughout 24 hrs of a normal commute trip-

scenario. ................................................................................................................. - 60 -

Figure 31 Buffer battery's energy representative throughout 24 hrs of a normal commute

trip-scenario. ........................................................................................................... - 60 -

Figure 32 Buffer battery's energy representative throughout 24hrs of a normal commute trip-

scenario, where positive values is energy produced by the PV system flown into the

buffer battery while the negative values are flown into the grid. ............................. - 61 -

Figure 34 Schematic representation of the concept (Dennis van der Meer, 2016) ....... - 69 -

Figure 35."Carsharing increases its popularity" Number of registered carsharing users in

Germany (in 1,000s) (Carsharing, 2017) ................................................................ - 70 -

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III Nomenclatures

EV Electric Vehicle

EVSE Electric Vehicle Supply Equipment

SOC State of charge

V2G Vehicle-to-Grid

GHG Greenhouse Gas

MW Megawatt

Wh Watt hour

kWh Kilowatt hour

EEG Renewable Energy Law

t Real world time in min

np Normalized Power

kWp Kilowatt peak

T Simulation time in sec

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1 Abstract

Due to the challenges of the current global environmental situation, the world agenda to

reduce the levels of CO2emissions permeates all economy sectors that contribute to these

emissions. The transportation sector is influenced by a market that demands alternative

vehicles which generate less emissions than the current motorized versions. In the last

decade, the energy storage technology has been immensely improved and full-electric

vehicles are nowadays an increasing fragment of some countries’ vehicle fleet. The main

obstacle, as of now, is the adaptation of the infrastructure for the growing electric vehicle

fleet and the minimization of fossil fueled based energy generation to supply those.

The purpose of this thesis is to replicate, in form of a simulation conducted fundamentally in

Matlab Simulink, a PV system that feeds a buffer-separated battery, in order to supply one,

or more, EVs with electric energy with as low or no energy from the grid as possible.

The first approach was to analyze similar studies and develop a new way to represent a

similar data flow. The simulation consists of four different subsystems, each representing a

different element of a charging station system: a RC circuit, which represents a charging

station; an EV’s battery subsystem, which represents an EV’s battery charging and

discharging patterns; a buffer’s battery subsystem, which creates a separated battery

connected to a PV system and their energy management’s relation; a separated subsystem

that simulates the relation between the feed the PV system inserts into the buffer-EV’s

battery and the amount of energy that is being taken from the grid.

Eventually, comma separated values, also called CSV, constructions – Microsoft excel

datasheets – were made to configure data readings from a specific PV system, to allow the

model to be flexible enough to recreate different locations the PV system could be located

in. A signal builder was constructed in a CVS file, to reconstruct the driving behavior, which

means, at what times and with how much energy a potential EV would dock in the charging

station.

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Exemplary simulations were conducted to test the model. One of them is used to reproduce

a simple financial scenario that could show the financial feasibility of the model under the

stated limitations among the model itself.

The particularity about this work is the different algorithmic - construction - approach to the

construction of the model itself, which in other words, is the data flow concept/map behind

the EVs charging station fundamentals.

It was proven that the exemplary scenario selected to perform the financial feasibility

analysis did not result to be a viable proposal. Principally, the household’s energy demand

factor is not considered in the structure of the model and therefore the author recommends

a further development of the model to provide values that represent market competitive

results.

This study sums up the efforts to provide an alternative system to forecast the energy flow

a buffer-PV system charging station could have, based on Matlab Simulink and Microsoft

Excel. The developed model offers a foundation for a further product’s development that

could be used by final users or energy consultants to predict, with relative precision, if the

acquisition of a buffer battery connected to a PV system would make financial sense. Even

though there are a substantial number of variables that are not considered in the model, the

objective to create an existing system with algorithmical differences was achieved. The

development of different ways to accomplish a similar objective, which is the energy

management in an EV’s charging station, affords a broader research to provide diverse tools

for researchers and developers. At the same time, the results of this study can contribute to

enhance the use of renewables for future grid-independent electric vehicles, considering the

possible risk of an excess of EV’s could represent in the grid’s expansion forecast.

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2 Introduction

2.1 Energy, transportation and the world

Since the beginning of humanity, movement has been a cornerstone to its survival.

Transiting from a nomad way of life, taming and riding wild animals, to the discovery of the

wheel and the motorization of it, humanity has been constantly searching for the most

comfortable, fast and efficient way to move.

In today’s world, people are continuously moving as part of their everyday life.

Transportation has become one of the most important aspects and challenges of the 20th

and 21st century. Since the creation and adaptation of the internal combustion engine into

motorized vehicles, cars have been one of the preferred transportation methods in the first

world countries. Specifically, in Germany, as for May 2018, there are 46.475 Million cars

registered (Statista, 2018).

Parallelly, another subject has increasingly become international priority for the past three

decades as a consequence from last century’s industrial revolution and rapid CO2 emissions

mainly created by the industry, energy production and transportation: the greenhouse gas

effect, which has been concluded by authorities like the NASA to increment the earth’s

temperature above an historical record line (NASA, climate.nasa.gos/evidence, 2018),

which alters most of the atmosphere we live in. It’s presumed that transportation alone

contributes to 14% of the global emissions (as in 2010) (Victor D.G., 2014), which is

considered a fundamental part of the gross volume of GHG total worldwide emissions.

As a response to this increasing threat (NASA, 2018), the vehicular sector has reached new

technological horizons in recent years: hydrogen cells and combustion by butane gas,

among others; but mainly the impulse to the technologies based on electrical energy storage

and the optimization of electric motors for private vehicles has been of particular interest.

This in response to a continuously emerging and growing market that considers, in economic

terms, the use of electric power as a source of fuel, or to the continuing global warming

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where the purchase of an electric vehicle significantly reduces over a long-term the carbon

footprint of its user – as referenced in Figure 1 -. However, the creation of new propulsion

systems for vehicles - such as electric vehicles - still have the consequence of depending

on the production of energy and the technology applied in the EVs is still outside the

acquisitive possibility of many people due to a relatively high price in Europe (Insider, 2018).

Nevertheless, the EVs market has been exponentially growing in countries like Germany in

which the number of EVs on the streets from 2016 to 2017 increased 33% and improved

into a 58% increase from 2017 to 2018, resulting in absolute numbers of 11,006 units, as

shown in

Figure 2 (ADAC, 2019). In Figure 3, a general global sales historic representation is

displayed and clarifies how extent the market has become and how the tendency for the

next years will be. As for now, the Chinese market represents more than all the other

countries EV’s markets. This creates an interesting niche for future energy solutions related

to electric mobility since the demand of energy for future infrastructural adaptations will

exponentially increase in order to supply the EV market increments. On the other hand, the

GHG emitted by the generation of electric power has a significant figure of 40% by the end

of 2018 of the total emissions of this specific sector (Victor D.G., 2014). Comparing this

percentage to the 14% of the total GHG emissions, which is produced by the transport

sector, it is reasonable to think that, from the environmental approach, providing the

increasing EV fleet with enough power to cover the increasing future demand, ends up

charging more load into the electric generation sector that is currently mostly fueled by fossil

sources – in Germany, till the fall 2018, the usage of coal for energy production goes to up

to 35% (BDEW-Schnellstatistikerhebung, 2018) -. Assuming that at the more EV’s there are,

the production of electric power to feed these would end up increasing the emissions of the

energy production sector if simultaneously the transition to 100’% renewable energy for

electricity production is not made (Elektromobilität, 2018). One reasonable technical solution

to lessen the demand load of energy produced by the future amount of EVs running on the

streets would be the further use of renewable energy, as shown in Figure 1; specially the

privately owned systems that generate energy in the household can make a vast difference

in how the grid will behave in the next years as a response to the increasing number of EVs

that are being sold. If the growth rate of EVs on the streets remains exponential, this could

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create a fragile condition for the future grid expectancies, specially towards the inclusion of

renewable energies (Lodberg Høj, 2018).

Figure 1 Climate balance for small vehicles (after driven for 150,000 km) (Kroher, 2018)

The technical obstacles to build or design a system to make the most of residential

renewable energy sources to provide power to the EVs are few. The premise would be to

know how exactly an EV feed system would behave in function of a privately generated

renewable energy through photovoltaic cells in the household, giving it an environmental,

financial and functional sense in providing an EV with energy.

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Figure 2 Electric and hybrid vehicles German market distribution. Electric vehicles (red),

hybrid plug-in vehicles (blue). (VDA, 2019)

Figure 3 Global EV sales throughout the last years (Irle, 2019)

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2.2 The sun as a power source

The constant fusion of our star, the sun, has a continuous production of high gamma

radiation photons because of its nuclear reactions, which are emitted perpetually into space.

These particles carry electromagnetic energy that is currently used in our planet through

photovoltaic systems which use the energy released from photons to create electrical

energy. In recent decades there have been extraordinary advances in the technical field and

solar cells can now reach an efficiency of up to 42% ((NREL), 2018); unfortunately the

technology of high efficiency solar cells is still too expensive, so usually, in the wholesale

energy market, photovoltaic systems are built with solar cells efficiencies of approx. 24%

(Hall, 2019). As renewable energy technology has become increasingly needed as far as in

many countries energy transition to renewables is part of their master plans for energy

conversion until 2030 and related to the achievements they signed during the launching of

Agenda 2030, the ‘Sustainable Development Goals’ (SDG), in 2015/2016. (UNDP, 2019),

photovoltaic energy has become a cornerstone for SDG’s as for its high installation flexibility,

its low environmental impact and its relatively low and still over-time-reducing cost. In the

case of Germany, the "Energiewende" (energy transition to renewable models) has set the

international example of how a country can transform its electricity production focus to less

GHG emissions. Although investment in photovoltaic technology has grown substantially,

the German energy market has been inclined to favor wind energy and biomass produced

as can be seen in Figure 4. Though, Germany is still the world’s largest lignite producer in

the world, with 23% of electricity deriving from lignite and 35.4 % from lignite and hard coal,

corresponding to 37% of Germany’s greenhouse gas emissions (Appunn, 2019).

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Figure 4. Percentage renewable energy in net electricity consumption for Germany.

(Energy, https://www.bmwi.de, 2017)

Thus, presently, 7.2% of the energy produced in Germany is of photovoltaic origin. The

benefits to encourage the population to install residential PV systems or in a larger scale is

the so-called "EEG-Vergütung" - the financial state-regulated incentive for renewable energy

- which provides a financial compensation for each kWh of energy transmitted to the grid.

As of November of 2018, the incentive for the feed in tariff (FIT) is stablished at a price of

0.1017 EUR/kWh1 against an average price of net commercial electric energy between a

consumption of 2,500-5,000 kWh per year - the average of household consumption in

Germany - of 0.2986 EUR/kWh (Energy, www.bmwi.de, 2018). At end of October 2018, the

Federal Network Agency fixed the regression of solar FITs for November, December and

January. The remuneration rates will decrease by 1% each month. In December, the feed-

in tariff for small PV rooftops – up to 100 kW – is between €0.1007 and €0.1159/kWh,

depending on system size, with €0.0801/kWh for other systems. The tariff applied to energy

sold to the grid – mandatory for systems larger than 100 kW, which do not qualify for a FIT

– is €0.1047-0.1199/kWh for rooftop systems, and €0.0841/kWh for other installations.

(Enkhardt, 2018)

1 Value to be applied according to §48 EEG 2017, minus reduction according to §53 (0,4 Cent / kWh). The price is size/effective power dependent.

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The monthly relative reduction of the incentive had two general consequences. The first is

that the residential sector stopped installing PV systems since the break-even point was

extended to furthermore years. The second is that it encouraged, and still encourages, the

use of PV energy for own usage, instead of injecting it into the grid. In these cases, stationary

battery systems, are used to store energy generated by the PV system during the day, while

using that same energy during demand hours when the PV system does not generate any

energy at all. This has become a lucrative business model -based on public funding - in the

last past few years (Finanztip, 2018), which indicates that the trend of photovoltaic solar

energy is still high in terms of installation and is becoming a very competitive market, that

translates into a lower benchmark for solar systems, and that the use of energy produced

has the great advantage of being used for own purpose or direct sales scheme. This gives

the opening to new emerging markets, new business models, as well as autonomy for

locations way apart any electric infrastructure at all. Specifically, the Li-Ion battery

technology has become increasingly cheaper in the last years as shown in Figure 5.

In 2016, almost one in every two small PV systems was already installed together with a

battery storage unit in households (Graulich, 2018), and in the coming years, due to funding

programs, the phasing out of the first contrastingly high EEG subsidies for photovoltaic

systems, which has been limited to 20 years, and also as a consequence of the further

reduction in costs, especially for lithium-ion battery storage systems, a noteworthy growth in

stationary battery storage can be expected for private households. The storage monitoring

report of 2017 exposed that the average electricity consumption of solar power storage

systems’ users lies meaningfully above average. The motives for investing in a storage

system were not only the positive participation in the German energy transition, but also

protection against increasingly rising electricity prices was mentioned as a main motivation

for the investment. Additionally, most of the buyers of PV battery storage systems can be

attributed to the cluster of “innovators” or "early adopters", who are viewed as highly

educated, effluent and having a strong bias towards technology and the environment.

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Figure 5 Li-Ion historical prices and forecast market. (BNEF, 2018)

2.3 Energy storage

The energy storage capabilities and different types of battery storage systems are a

cornerstone for the integral understanding of an EV. The technology is currently dominated

by the commercially most used chemistry with the optimal price-efficiency ratio, the lithium

ion cell. Most automobile brands that mass-produce EVs use this chemistry. Nevertheless,

limitations of the lithium battery technology currently make the attractiveness of EVs not to

reach a high-scale market impact:

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Table 1 Pros and cons of Li-Ion battery technology.

Pros Cons

It’s lightweight in contrast to other

battery chemistry technologies

High energy efficiency

Very low self-discharge rate

Versatile forms and fittings, which

allow the technology to be used in a

variety of products.

Li-ion still expensive, representing

almost 30% of the EV’s final price.

(Schwarzer, 2018)

Short lifespan, loosing a

considerable amount of effective

capacity after a relatively short

amount of cycles.

The architecture of the current batteries have been improved in recent years to meet the

demands of the EV’s market; however an improvement in the fundamental chemistry would

be necessary to be able to satisfy a sustainable life cycle which currently the extraction of

lithium, for example , counteracts. Unfortunately, lithium carbonate is a limited resource and

has a considerable environmental and social-laboral impact - along with various materials

used for the production of the battery cell itself -. It has been reported that the extraction of

approx. 500 kg – which are needed for a sport utility EV – emits almost 74% CO

(Bloomberg, 2018) more than an efficient conventional car manufactured in fossil fueled

industries in a place like Germany, what means a negative trade-off in the use of electric

vehicles for the relative decrease of GHG emissions. Nevertheless, if manufacturers

switched their power usage in their plants to only renewables, they could cut emissions up

to 65% less CO – due to the reduction of the extraction and burning of coal to generate

energy -, according to the Transport & Environment Secretary of the U.S.A., which could

almost balance the GHG emissions between extraction, transportation of li-ion and those

produced by the manufacturing industry. The CO balance is becoming an increasingly

decisive topic in the international industrial market.

The influence of CO balance is considered vital for a more sustainable way of industrializing

products, nevertheless, the focus of this work is the state of the art energy managing system

used in the operating industry of EVs. The charge and discharge algorithms of a li-ion

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battery, which take a fundamental role for this work, will further be discussed in function of

the model developed in this thesis, putting the environmental considerations to the side.

2.4 Related work

The economic and technical feasibility of electric vehicles in connection to renewables and

the management of the energy used has been studied by various authors. The baseline

theory published by Fuentes et al. (2017) in using a PV powered E-Bike sharing platform,

concludes that PV is highly efficient in providing CO -free mobility sharing services through

already installed infrastructure available for their applied project. Another related work to the

direct application of PV to generate energy directly provided to charging stations, was

conducted by Tulpule et al. (2013) where it is shown that the recovery of the investment

attributed by the direct purchase of the PV system can be achieved before the end of the

useful life cycle of the components – usually a 20 years life cycle -. These studies were

performed on the background of an economic and geographic margin within the USA.

The most interesting work is related to the charging of a small carsharing system’s batteries

through renewable energies (Jameel et al. 2017), in which the premise is the feeding of an

EVs sharing platform through a combination between grid and PV generated power. The

project is based at the University of Hildesheim and basicly provides the vehicles with PV

on-demand2 power and the intereseting study in function of different docking behaviours. It

displays different curves for every energy fluctuation in all of the system’s components for a

one year period.

Fairley (2013) states the success of EV carsharing platforms in France, specifically from the

customer’s point of view, which provides a wide perspective of what customers expect from

carsharing in a qualitative perspective.

2 As far as solar radiation is available.

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In relation to Vehicle to Grid (V2G), Gough et al. (2017) present a work in which it is

concluded that the degradation of EV batteries tends to be highly risky in certain scenarios

from a financial and functional point of view - due to the extreme degradation of the battery

over time. Thus, the use of V2G as a network stability model for private third parties highly

depends on the market and the selling price of electric power in order to be financially

senseful. This leads to much skepticism as to whether this model could be succesful under

the current design and chemistry of the batteries.

Supporting this, the work of van der Meer et al. (2016) concludes that V2G is not feasible

under the schemes and configurations of the study until the publication of the work, since

the batteries simply negatively exceed the cost-benefit ratio. Furthermore, the authors

emphasize that this model could work in geographic locations where the energy provided by

PV has a surplus, which suggest the concept for other parts of the world in which feed-in

tariffs are competitive and where there is sufficient solar radiation to provide continuous

power supply by the PV system.

2.5 Research questions and objectives

The objective of this study is to create an instrument (further called “model”) to evaluate the

financial feasibility of a hybrid energy EV supply system between grid and photovoltaic

power by simulating an empirical model created using Matlab Simulink in function of different

docking scenarios and PV power historical data. The instrument allows future EV owners to

study the possible acquisition of a PV-Buffer and analyze if such an energy solution would

bring financial benefits.

Slowly, cities are adapting to a model of smart cities in which the management of resources

tends to be automated, such as traffic control systems and the management of thermal and

electrical energy. In this latter area, also called smart grid, the automation of energy

management plays a fundamental role for the correct administration of energy sources,

whether centralized or decentralized. The model of this work depends on the efficiency of

the energy control in order to have a significant technical sense. Creating a link between the

vehicle, the grid and the renewable energy system considered, allows to provide a degree

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of autonomy that keeps the human factor aside and allows an efficient management of

energy. Another cornerstone that provides the grid alleviation for future load demands is the

autonomy or efficient energy management systems in beneath the user itself. This aspect

will take a fundamental role in the future of energy management, specially assessing the

CO reduction agenda, since added demand in the grid attributes to additional fast-reaction

energy generation - like gas turbines - which renewables are not considered to take part of,

because of the lack of financial competitiveness in the energy storage branch.

The main research objective is to create an instrument that displays if the feed of an EV

through a PV system and a buffer battery would be commercially viable considering specific

use scenarios and precise historical solar radiation data of the studied location. Parallelly,

possible uses or applications of the instrument will be discussed throughout this work.

2.6 Research methodology and assumptions

The first step in the approach was the evaluation of existing concepts and models which

were described in the last last chapter, specially focussing in other ways to achieve a model

which could recreate the energy management between a buffer battery, a charging station

and a PV system. The main achievement was to develop an instrument as flexible as

possible to have a relatively wide spectrum of applications. Then a simulation model was

developed in Matlab Simulink, by MathWorks, which is a graphical programming

environment for modeling, simulating and analyzing multidomain dynamical systems. Its

main interface is a graphical block diagramming tool and a customizable set of block

libraries. It offers fitted integration with the rest of the MATLAB environment and can either

run MATLAB or be scripted from it. Simulink is widely used in automatic control and digital

signal processing for multidomain simulation and Model-Based Design.

In the specific model developed in this work, the following components were generated and

connected.

- 24 -

A signal builder that represents the times during a day in which the EV is connected

to the charging station. This signal builder input is given by the comma separated

values – CSVs - previously arranged in an excel datasheet.

The electric power generation of a variable peak power photovoltaic system that can

reproduce any recorded data’s reading of a real PV system during a given time. The

recorded data of the PV system is preprocessed - before the actual run of the

simulation - in Microsoft Office Excel and eventually entered in the model. There is

no restrictions as for how much or how less information is input, since the output of

the model creates an average representative day of the information input, which

means the average power ratio shall be generated first from the information supplied

by the PV system before running the simulation.

The energy management between the PV system, the buffer battery and the grid, in

case the supply of electric power generated by the PV system is not enough to satisfy

the demand of the EV. A routine for each of the elements has been built in

subsystems, which are simplifications of certain parts of the model itself.

A RC circuit, a circuit with both a resistor and a capacitor, generates the power over

time or energy by any of the batteries; either the buffer battery or the EV’s battery.

Previous historical PV systems reading data analysis through datasheets and a block

which generates the power generated over time by the PV system.

Each component of the model will eventually be presented in detail in the next chapters.

As for the limitations of the model, we consider one PV system on a single household, one

buffer battery and one EV as the limitations of the model. Every single unit can be

parameterized, so that every capacity, initial condition or other important value can be

altered to improve the certainty of the created scenario.

The human driving factor is not directly considered in the model. Assumptions are just

created through certain scenarios, e.g. the EV is being docked three times during a day and

the second time it’s being docked the user can assume that between the first and second

docking there has been a consume - energy consumed by driving in between both dockings

- of 4 kWh energy from the EV’s. This information can be configured before running the

simulation to recreate this exact behavior. With this, is important to remark that the model

doesn’t generate any human interaction by itself, but rather given by the user.

- 25 -

At the same time, the initial RC circuit parameters - the capacitor and the resistor - are just

demonstrational, since the battery characteristics data for the EV studied -Renault Zoe Z20

– can differ performance wise as the presented in this study.

A significant limitation of the model are the variable climatological and environmental

conditions. Since the input of the PV system is historical, it’s assumed that the average

climatological and environmental conditions that can influence solar radiation like

cloudiness, temperature or dust on the solar panels will remain the same in the future, which

is an unrealistic supposition. This factor influences the certainty level for future generation

predictions significantly and shall be considered.

Yet another substantial limitation is the representation of a given time period of data

acquisition, specifically the PV historical data, which in this case is one year of data

recollection, into an average day in the simulation. This increases the amount of uncertainty

as for the precision in the results, since working with average values ignores, in this work,

the possibility of changes in the EV’s battery SOC or the changes in power generation from

the PV system itself in a day-to-day basis.

Another noteworthy limitation was the limited knowledge the author had on the software

used, which made the development less efficient, since the learning curve started from an

unideal point. This made the translation of certain theorical concepts into practical

applications in the model more difficult than in other similar works in which the authors had

significantly more experience with Simulink.

It is imperative to state that any kind of losses between equipments are not being considered

in the model, which means losses due to thermal or alternation effects are not being

measured. The model just implements an empirical representation of all values and doesn’t

take a precise technical approach to the topic researched in this work.

- 26 -

3 Development

The development of the Matlab Simulink model was divided into 7 major parts:

Delimitation of the model’s capacities and the parameters that would be included.

PV historical data and signal building datasheets – CSV - management.

Creation of an RC-Circuit.

Creation of the buffer battery routine.

Creation of the EV’s battery routine.

Relational connection between both routines and the RC-Circuit.

Matlab script for output data easy access.

In this chapter, procedures used for the simulation of each of the above-mentioned sections

are described, considering bibliographic sources.

3.1 General scheme

The fundamental purpose of the model is, as previously stated, to make a system in which

an EV’s battery, a buffer battery, a PV module and the grid interact with each other under

scenarios stated by the user, which displays the energy management of all the elements.

The principle of a Matlab Simulink simulation is to provide a signal that is rerouted and

processed by various conditions provided by the developer. The main schematics of the

general conditions are shown in Figure 6.

As part of this work’s framework, it’s considered in the model that the PV system, the buffer

battery and the charging station are completely separated from a possible household circuit.

Parameters simulating a household’s electricity demand during the day is not, at the same

time, considered in the model. Nevertheless, the charging station is considered to be

connected to the grid, in case the battery has not sufficient energy to supply the EV’s battery.

- 27 -

PLUGGED & BUFFER SUFFICIENT

EV PLUGGED & BUFFER INSUFFICIENT

EV UNPLUGGED/FULL

Figure 6 Energy flow for each potential situational scenario. (own illustration)

- 28 -

3.2 Parameters

As an introductory phase to the project, the author selected several variables that would be

important to relate to the model. The listing in Table 2 is separated in subsystems –circles -

on the left side and the variables described on the right side are each parametrized variables

and its respective measuring units:

Table 2 Subsystems and parameterized variables.

All previously variables will be described with detail in their respective subsystem’s chapter.

The access to the variables/parameters and their configuration can be seen in the workshop

•EV's battery pack voltage (V)•EV's battery capacitance (F)•EV's battery resistance (Ohm) •Charging station's max. power (W)

RC Circuit

•Cycle start condition (Wh)•EV's battery max. capacity (Wh)•Price/kWh (EUR/W)

EV's battery

•Peak power (Wp)•Price/W feed in tariff (EUR/W)PV Module

•Buffer capacity (Wh)•Initial buffer energy (Wh)•Inferior battery charge border (Wh)

Buffer battery

- 29 -

file added to the simulation package. It is important to open the workshop file before running

the simulation so that the parameters can be read while running the simulation.

3.3 PV implementation

Metadata reading flexibility for use within the simulation was an important part of the project,

so the implementation of this module had to be based on solar radiation; we chose an excel

spreadsheet, where the values are managed in two columns, the first for time (t) and the

second for normalized power (np) where:

0 𝑛𝑝 1

This constraint allows for the PV system peak power to scalate easily, only manipulating the

block parameter PV_System_Scaling, which for exemplary purposes displays 2,000,000

Wp.3

In Figure 8, the PV_system Subsystem is displayed, which multiplies the np times the

PV_System_Scaling in function of t.

3 Wp shall be the preferred power unit for calculation purposes and will eventually be converted to a more satisfactory dimension for financial evaluations (kWh).

- 30 -

Figure 8 PV_System Subsystem

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 100000 200000 300000 400000 500000

np

t (min)

Figure 7 Normalized power per minute during a year (2011) of data collection

- 31 -

3.4 PV exemplary data collection

The data acquired for the PV system sample was taken from a facility located in Kronberg,

Germany. The system has a total power of 4.51 kWp4 with a 30° inclination with reference

to the horizon and 40° oriented to the southwest.

The period on which the data collection was based, was stablished during the following

dates: 01.01.2011 - 31.12.2012. In Figure 7 5, t (min) is shown in function of np during the

total data acquisition time period.

In Figure 7 we can also observe the seasonal variation of power supply. Considering that

the graph tends to represent data from January, a Gaussean type distribution is observed,

in which in the cold/dark months (January to April, October to December) the scarcity of

solar radiation is reflected in the reduced np generated by the PV system.

In order to convert the data for one year displayed in Figure 7 a CSV spreadsheet was

utilized. The simulations total time period of analysis is one representative day, which means

that the acquired data must be converted into an average day in order to be able to process

it through the model. The CSV in that case would have all np ratios for an average certain

time period. The CSV spreadsheet used for the average PV readings year can be found

annexed digitally to this work.

The total simulation is running with a maximum of 288 steps, each step with a value of 5

mins real time, being the total number of minutes in a day. The aim here is to facilitate the

reading of both databases in referential searches. An example is presented in the following

equation:

𝑇 240 𝑚𝑖𝑛 ∴ real time 240 min/5min

steps48 𝑆𝑡𝑒𝑝𝑠

4 The peak power in the data collection is disposable because p is normalized. 5 Data collection facilitated by M.Sc. Anne Mädlow from the “Technische Universität Bergakadamie Freiberg”. [email protected]

- 32 -

In order to refer from simulation time to real time, the inverse of the equation is to be

calculated.

The above-mentioned data was entered through a spreadsheet block in the Matlab Simulink

model. To averagely reproduce the readings in the model environment, it was necessary to

do a filtering of the information. In the folder >Pre-processing Data>Seasonal PV power

data>Datamanagement_PV_seasoned_year, the user can find an Excel file that filters all

information separated by months if the reading is paced in 5 min laps. After that, the user

can use a representative Month’s representative day, a Season’s representative day or a

Year’s representative day. This data is then updated in the “PV Spreadsheet” of the model

to input the data in the simulation.

Figure 9 Power generated by the PV data in a year's representative day.

- 33 -

Figure 10 Power generated by the PV data in a summer season's representative day.

Figure 11 Power generated by the PV data in a winter season’s representative day.

- 34 -

3.5 AC and DC structure

There are two different load types available to use in an electric circuit/system installation

that are important to consider and differ greatly from each other infrastructurally-wise: AC

and DC. Both types have advantages and disadvantages.

(1) AC: The design of the charging stations is usually configured to be cheaper. The ease

of having charging stations based on AC is the possibility of using the current without using

a transformer and a circuit rectifier, so it tends to have a simpler and faster installation than

its counterpart. EV tend to have both inputs, one with a direct connection to the battery -

which is charged with DC only - and with an on-board AC/DC connection. On the other

hand, the load powers are lower than those provided by a load station that uses DC, which

makes the charge per se relatively slower than with a DC architecture.

(2) In charging stations with DC, the installation costs are higher since a transformer and a

circuit rectifier are used to convert the AC of the grid6 into DC. However, the load power can

be up to 20 times greater than those that can be provided by an AC-based charging station,

making the charging of an EV battery much faster. The complexity of the charging process

is increased, since the charging process must be continuously monitored through a BMS

(Battery Management System).

For the purpose of this work, the model will only represent an AC connection between the

charging station and the EV’s battery, while parallelly a DC connection exists between the

PV system, the buffer battery and the EV’s battery. The reason for this resides in the logic

behind the energy transfer from batteries in general and the long distance in which electrical

energy has to be transported through the grid. AC allows a less robust infrastructure to

transport electricity for long distances, while the architecture of batteries is based in DC,

which allows energy transference without any transformation. EV’s usually have two types

of socket inputs to allow either AC or DC charging. While the AC socket has an inverter

before connecting to EV’s battery, the DC socket connects directly to the battery. Usually,

6 Before being connected to the actual charging station.

- 35 -

the less equipment is used in an electrical circuit, the less resistance and less losses there

are.

In Figure 12 the relationship between an EV’s battery pack SOC and the power induced by

the charger under an AC charging process (shown as P ) are presented. It clearly shows

that the relationship is not linear, as stated before. This part will be approached in more

depth in the charging and discharging part of the simulation.

Figure 12 Simulation of demand profile of EV charger with Continuous Power-Continuous

Voltage option with AC design. (Francesco Marra, 2012)

The technical advantage of having an architecture of DC charging stations is the easy

incorporation of PV systems, since the output also corresponds to the same type of current,

in addition to a slightly higher efficiency of the PV system. In case of having a charging

station, a slight loss of 2-3% (Ionin, 2018) is contemplated due to the inverter's AC / DC

conversion defficiency.

- 36 -

There are other technical considerations when an AC or DC system is involved, nevertheless

it is not considered relevant to include detailed technical aspects for the creation of this

simulation, since the approach to the topic is merely empirical. However, it is important to

mention that, although the power is considered as only a relevant aspect for this work , the

difference between AC and DC charging methods do have a significant financial, electrical

efficiency and timespan impacts in the consideration of the scenarios for a potential charging

station installation.

Likewise, the temperature and the efficiency of the electronic components are excluded as

influential factors in the energy generated by the PV system, taking the metadata in cvs

formats obtained from Figure 7 as the only usable data.

When obtaining the power readings of a specific PV system, the limitations of the simulation

are few, strictly from the generation of power through the system’s point of view. Among the

most important is to consider that weather situations are always changing and, although

climate fluctuations may have similarities year after year in the same place, the possibility

that they are equal are very low. The most accurate approach to dealing with this variability

in climate behavior is the use of probabilistic models such as the one mentioned by Dennis

van der Meer et al.(2016) where a probabilistic model is used for forecasting the climatic

scheme, which is a method to be able to forecast the solar radiation levels of a particular

locality. However, it was considered for this work that the readings made during a set period

of time sufficed, since the model allows the flexibility of entering the reading data from any

geolocated site, so, even though the approximation may not be of probabilistic nature, real

data readings can be used and the model is expected to have a resemblance to real world

conditions.

- 37 -

3.6 Charging Station - RC Circuit - subsystem

One of the model’s purposes is to simulate a charging station that provides power to the

EV’s battery. In the model, the connection to the charging station is strictly only to the EV,

whereas the PV system is connected only to the buffer battery. The grid - simulated as the

charging station - can provide power at any time given in case the buffer battery has not

enough power to provide energy to the EV’s battery. The base for the reproduction of the

charging station is an RC-Circuit. A voltage is added to the integral of the product from a

resistance and a capacitance. The last two parameters are deliverately given so that a power

of 3700 W is reached. Nevertheless, it’s possible to control the maximum power delivered

to the EV by changing the max. power parameter in the workshop. The scheme that

represents the RC-Circuit has the objective to depict a power flow from an hypothetical grid

source, through the charging station and to the EV’s battery only.

One of the important limitations of the EV battery to be dealt with is the EV model per se.

The EV market offers several vehicles with a wide range of battery packs designs. One of

the most sold vehicles in the German market, for example, is the model 2018 Renault Zoe

in its Z90 version (Schneider, 2019), which has a max. battery pack capacity of 41 kWh, so

for practical use, the voltages and maximum capacity of the simulation’s battery pack of this

EV will be represented as such. Some of the battery pack specifications of the Zoe Z90 are

as following:

Total weight is 305 kg.

Total capacity is 45,61 kWh - estimation by knowing the usable capacity.

Available capacity is 41 kWh.

192 cells, each with 63.35 Ah nominal capacity – estimation - and 3.75 V nominal

voltage.

Total cell weight is 180.12 kg - estimation by knowing the total battery weight- (Lima,

2016).

Refering to the past listing, the available effective capacity of 41 kWh and the nominal

voltage of every 192 cells are added to the model parameters to represent the characteristics

of the second generation Zoe’s battery, which is used in the Z90 model.

- 38 -

Focusing on the representation of the battery in the model, in order to create a realistic

simulation, it became necessary to implement a resistor and capacitor circuit (RC-Circuit,

simple schematics shown in Figure 13 ). Taking into account that:

𝑃𝑜𝑤𝑒𝑟 𝑃 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑉 ∗ 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐼

It was obvserved that the power excercised by a charging station into a battery is functional

to the voltage and the current of the charging station itself. This makes it important to

consider the voltage and current within the simulation scheme, since the power is directly

related to these elements. The output of these parameters is delivered by an CSV file,

nevertheless just power will be used through the simulation.

Figure 13 Simple RC-Circuit schematic (July Thomas, 2018)

The last circuit is shown in applied in the simulink model in Figure 14. Through the appliance

of this circuit, the non linear increments of the power in function of the time is recreated. This

means that the power output from the charging station is not instantaneous excercised to

the maximum. A representation of this phenomenon is shown in Figure 15 where between

the minutes 0 and 1 there’s an increasing of the power - or a light gap horizontally. The

recreation of the RC-Circuit allows the model to represent a more realistic approach to the

power used to charge the battery pack of the EV. This phenomenon affects the eventual

results’ analysis of the buffer battery, where in the 5 min-step in which the power increment

- 39 -

takes part the output of the buffer battery energy status displays 0 as a value in that specific

step. This has consequences in the reality of the analysis in function of how many charging-

to-discharging - or vice versa - changes takes place during the whole simulation, where each

change represents 5/288 of uncertainty.

Figure 14 RC-Circuit model represented in the model.

- 40 -

Figure 15 Current scope, where in the first steps of the simulation the EV's battery is

requiring power -docked- and the power is exercised in the EV's battery with a delay of 5

min. The expectation for the power curve was to simulate a real-life powering ratio, but the

obstacle was the 5-min steps pacing, which cannot reproduce the same real behavior a

charging station could have, which is the main reason why the current/power behaves

linearly in the simulation.

- 41 -

3.7 Signal builder

The signal builder is a fundamental element that vastly influences the model results. This

block creates the docking signal, which in other words can be interpreted as the docking

behavior. This signal outputs with 1’s and 0’s the docking status of the EV to the charging

station: 1 for docked/connected and 0 for not docked/connected. A further interpretation of

a 0 is that the EV is currently being driven. To be able to make a parametrical matrix in which

the user can freely modify the docking behaviors an excel importation into the signal builder

is required before running the simulation. The following steps are to be done:

Open the “Signal Time Reader”.xlsx file - included inside the model folders under “Signal

Builder” > modify the third column to the desire docking times> save the file > in the Simulink

model open the “Signal Builder” Block, inside the “RC Circuit” subsystem > file > import from

file > browse the .xlsx file and select>open sheet 1 tab > select imported_signal 2 >

Placement for selected data: replace existing dataset > confirm selection > apply > accept

> save signal builder block.

The general time equivalency inside the simulation is 1 second representing 5 mins of real

time readings, which is the reason why the pacing between timespans are 5 mins inside the

signal builder datasheet. It is observed in the .xlsx file that there are three columns displaying

data. The first number is the second - Simulink time - in which the signal is being read, while

the second displays the real time conversion and the third, as stated before, the docking

status. As a representation of an example, Figure 16 shows a docking behavior of an EV

being connected from 00:00-07:00, then is reconnected from 16:00-24:00. This behavior

can represent a normal working day in which the EV’s user leaves in the morning for the

usual commute to the workplace, comes homes for a midday lunch, leaves again to the

workplace and at the end drives back home after leaving the workplace.

- 42 -

3.8 EVs Battery Routine subsystem

This subsystem represents the energy flow inside the EV’s battery. Taking the power flow

from the RC-Circuit and aggregating it to the subsystem, subsequently integrating the power

signal in function of time, the energy output can be created under the following equation:

𝐸 𝑝 𝑡 𝑑𝑡

Where p is power in Watt [W] and E is energy in Watt hour [Wh].

Figure 16 Signal builder block representing a 0-7; 16-24 docking behavior. The expectation was to

represent in 5 mins steps the docking behavior an EV could have in a house-based charging station.

The result is displayed in 5 mins steps. The author recommends to pay close attention to the excel

format so that, during the input parameters configuration, interpretation mistakes could be avoided.

- 43 -

Additionally, the power is being outputted in a different scalar unit, so in order to adapt it to

the desired units, the signal must be divided by 12 – steps per hour – so that the energy

units are adapted to the rest of the model, since:

60 𝑚𝑖𝑛5 𝑚𝑖𝑛

12

The integral of Figure 16 would exemplarily be displayed as shown in Figure 18, it’s to

observe that the integrator is restarted through an external signal which comes from a block

composed by a counter and a multiport switch. The purpose of these two elements is to be

able to set conditions before running the simulation, e.g. to set a certain SOC of the battery

before running the simulation. The multiport switch has in default three entries that can be

configured for a respective cycle of the simulation. A cycle, in the model, is defined as the

change in the power output from a zero to a non-zero value. A single cycle is completed

after the following process:

1. Signal changes from a zero to a non-zero value – or vice versa –.

2. Signal changes from a non-zero to a zero value – or vice versa -.

If the user requires more cycle entries, the multiport switch must be changed respectively to

adapt the user’s configuration. This represents any level of SOC that remains in the EV’s

battery after a supposed drive. Supposing that the simulation runs for 24 real-time hours, if

the signal builder is configured in such a way that the EV is connected before and after

24:00/00:00, the simulation is to be run once to observe which is the last energy level – or

SOC in kWh units -maintained in the EV’s battery so that the output of the simulation can

me realistic and cyclical. This is a significant restriction of the subsystem and relevant for

the interpretation of the simulation’s results.

The multiport switch is, as shown in Figure 17, connected to a switch which reads if there is

any non-zero values coming from the “Integrator Battery Power” and adds the multiport

switch to start the increments or “charging” behavior at the respective given cycle start

condition, so that the preserved energy in the battery after a supposed drive can be

represented.

- 44 -

The signal is then rerouted through a saturation block which alerts the user if there’s any

values that do not respect the given limitations. Right before sending the final rerouted

signals, a switch has been given to display zero values if the power – or EV’s docking status

remains “1” - is not currently outputting non-zero values. The signal is then directed to the

workspace in CVS form for further analyze.

Figure 17 EV's Battery Routine Subsystem in the Simulink model.

- 45 -

Figure 18 Scope from the power integrator. The integrator is a fundamental aspect of the

charging/discharging of both the EV’s battery and the buffer’s battery. It converts the

power signal into energy, which is the unit the project was based on. The scope of the

function shows how energy is represented in the Simulink’s environment, although it does

not have any further relevance in the interpretation of the eventual results.

3.9 Buffer Routine subsystem

This subsystem intends to represent a buffer battery pack, which is independent from the

EV’s battery, which receives energy from the PV system, store that energy and transfers it

to the EV in case the EV demands energy, e.g. when the SOC is below the 100% of the

effective battery capacity7 - so that it doesn’t reach an unhealthy charging cycle behavior -

of the EV’s battery capacity. The premise that, alone, the PV system located in a northern

7 The effective battery capacity is the limitation of the brute battery capacity so that the battery doesn’t reach any levels that could provoke an unhealthy charging cycle behavior, which, in other words, means that the battery protects “itself” from deteriorating its battery capacity as a result of limit frame charging behaviors.

- 46 -

hemisphere, e.g. Germany, with residential power capacities (around 10-15 kWp) could

never reach an effective power flow that can satisfy the EV’s power demand in a constant

basis, or at least long enough to consider it financially senseful, the subsystem only

considerate that the power flow goes from the PV system through the buffer into the EV’s

battery and not directly - which is not a practical case, since in a real situation the PV system

can flow energy into the household while the battery is charging the EV, but for this work it

was limited into flowing power directly into the grid.

This subsystem, as shown in Figure 19 uses a switch at the start of the signal routing to

define if the buffer should be either charge or discharge. There are three different inputs

linked to the switch which provides the buffer charge - value of the signal for charging is 1

and discharging is 0 -:

The EV’s battery capacity = The EV’s battery maximum capacity

The EV’s battery capacity = 0 kWh

A counter which decides if the level of the buffer is still above the lower limit.

This third condition relays in the feedback the buffer is giving in-between steps. The decision

is based by a switch that reads, first, if the buffer is empty, and secondly, a counter where

the external reset is the lower limit condition of the buffer. The counter has a range of 0-1

and if the momentaneous buffer level is equal or greater than the buffer lower limit, the

counter resets into a 0. For practical understanding, the condition provides information that

flows from the late buffer actual capacity.

- 47 -

Figure 19 The battery buffer routine's subsystem in the Simulink model.

After the charge/discharge condition, the signal is rerouted into two different algorithms, the

charging and the discharging algorithm. The discharging algorithm begins with a counter

that gives a 1-value pulse to a switch, which at the same time provides the actual value of

the buffer energy. The switch reroutes this value to a sum block which adds up the actual

momentaneous value from the buffer and then subtracts the discharging pattern that request

the EV’s battery directly.

In the case of the discharging algorithm, the signal coming from the charging/discharging

switch triggers an integrator, which is integrating input comes from the power provided by

the PV system. The integrator, at the same time, has an external reset input that is triggered

by the charging/discharging switch. If the values provided by this switch changes from 0 to

- 48 -

1, or vice versa, the integrator starts from an inputted initial condition, which in this case is

provided by the last buffer value – which at the same time has an initial simulation condition

that is given in workspace as a parameter-. After that rerouting, the signal has to be

converted into a 5-min stepped simulation, which is achieved through the gain –

multiplication – by a factor of 12 as explained in the last chapter.

Both signals are then inputted into a switch that is in function of the charging/discharging

switch and reroutes both signals, as commanded, into a saturation block that limits all values

into the given buffer limit parameters. The signal continues into a memory block that feeds

a data store writer so that it can be used around the subsystem without further graphic –

connections - disturbance. All values are then saved into a “to workspace”-block to be able

to display them into a CSV file. An exemplary output of the buffer battery scope is shown in

Figure 20.

- 49 -

Figure 20 Scope from the buffer battery energy. On the second quarter of the function, the

energy flowing into the buffer has an exponential behavior, since the PV system starts

receiving solar irradiance and as the sun rises, the irradiance becomes higher, which

translates into an exponential behavior. After the EV is docked into the charging station

system – last quarter of the curve- the behavior of the function is linear since the buffer

discharges in a constant matter towards the EV’s battery. The results reassemble the

expectation that was intended for this particular module of the model: to successfully

represent the basic power behavior the buffer battery would have in function of the EV’s

battery and the PV system.

- 50 -

3.10 PV-grid-buffer ratio subsystem

The purpose of this subsystem is to display where the energy from the PV system is being

delivered at, either to the buffer battery or the grid or at the same time, when the buffer does

not have enough power to supply the EV’s battery, the grid supplies that energy. These

situations are better represented in Figure 6. Exemplarily, if the buffer battery is full at the

time the PV system is producing energy, the energy management system directs the power

flow into the grid. The subsystem is shown in Figure 21. It basically consists of two

segments. The first one connects the buffer discharge signal, which represents the signal in

which the buffer needs to be charged or not. Then a relational operator checks if the signal

is equal to the buffer maximum capacity. If any of both situations give a “1-signal” then,

another switch proceeds to give either a positive or a negative value to the solar power

energy, that is previously filtered through an integrator and multiplied by 12 in order to modify

the signal time frame - as previously applied to some signals. At the end of the signal a “to

Workspace”-block is given to do its respective postprocessing.

The second segment is responsible of the energy that flows from the grid to the EV’s battery

if needed. It starts with the connection of the EV’s battery energy demand-signal which is

filtered by four different conditions and connected to a logical operator. The conditions are

the following:

The buffer’s actual energy is greater than zero.

The buffer’s actual energy is lower than the parametrized buffer lower limit.

The EV’s battery max. capacity is not equal to the actual EV’s battery energy.

The EV’s battery energy demand is not equal to zero.

This logical operator then gives a “1”- or “0”-signal to a switch that filters the discharged grid

energy the EV’s battery requires in case the last conditions are met.

- 51 -

Figure 21 The PV-Grid-Buffer Ratio's subsystem in the Simulink model.

- 52 -

3.11 Script

The purpose of the script in the Matlab environment is to translate all the data managed in

the Simulink model into user-friendlier software-based data.

Through the script named “doingThings.m” -inside the final model folder - the following

variables are translated into a CSV file - or excel sheet -:

The total energy that flows from the PV system to the buffer battery: “PV to Buffer”

The total energy that flows from the PV system to the grid: “PV to Grid”

The total energy that flows from the grid to the EV’s battery. “Grid to EV”

The total energy that flows from the buffer to the EV’s battery. “Buffer to EV”

- 53 -

4 Exemplary scenarios

In order to exemplarily analyze the functionality of the model, three different scenarios have

been input into certain parameters – inside the Matlab Workspace and the signal builder

block -. In all scenarios, following boundary conditions remained unchanged:

The PV production data is an average of a whole year and can be customized at

any time with the excel tool described in its respective chapter.

The energy flow from the EV’s battery has to be adjusted in order to have cyclical

behaviors. In other words, in case the EV’s battery is below the max. capacity at the

end of the simulation’s day, the “EV’s battery first cycle energy” has to be adjusted

to the last value achieved.

The RC circuit configurations of the battery capacitance, resistance and voltage

remained unchanged during the run of the different scenarios as well.

In the counterpart, the behavioral scenarios – signal builder –, the EV’s battery and the

buffer’s parameters have been changed as listed in each respecting following scenario.

4.1 7,12-13,16 Scenario

This scenario represents an average working commute day with a short one-hour break.

The driver would, fundamentally, take the car from the charging station at 7:00 hours and

come back to the charging station at 12:00 hours, to eventually leave the charging station

again from 13:00-16:00 hours to finally dock the EV back to the charging station for the rest

of the night. The selected parameters for this scenario are the following:

- 54 -

Table 3 Parameters input in the model's workspace for the 7,12-13,16 scenario.

Buffer lower energy limit [Wh] 3,000 Wh – 3.0 kWh

Buffer max capacity [Wh] 9,800 Wh – 9.8 kWh

EV’s battery first cycle energy [Wh] 41,000 Wh – 41.0 kWh

EV’s battery second cycle energy [Wh] 36,000 Wh – 36.0 kWh

EV’s battery third cycle energy [Wh] 36,000 Wh – 36.0 kWh

Max. charging station power [W] 3,700 Wh – 3.7 kWh

PV system peak power [Wp] 10,000 Wh – 10.0 kWh

Initial buffer battery energy [Wh] 0 Wh – 0 kWh

EV’s battery max. capacity [Wh] 41,000 Wh – 41.0kWh

Figure 22 EV’s battery's energy representative throughout 24 hrs of a working day with a

small break-scenario.

- 55 -


small break-scenario.


small break-scenario, where positive values is energy produced by the PV system flown

into the buffer battery while the negative values are flown into the grid.

- 56 -

Table 4 Total energy flow in the charging station under the working day’s scenario.

PV to Buffer PV to Grid Grid to EV Buffer to EV

13.23 15.25 0.00 8.63 kWh

4.2 7-19 Scenario

The 7-19 scenario represents a day’s car trip that could happen during a weekend. The EV’s

user drives the EV for several kilometers before coming back to the charging station, so the

main parameters remain the same, but instead, the second cycle has a higher SOC’s

difference value than what the buffer battery can possibly cover. This pushes the model to

display a grid-EV value, which signifies that the EV demanded at some point energy from

the grid, when the buffer battery could not provide enough. The difference in the first cycle

in contrast to the 1st Scenario is the adjustment of the last simulation’s value so that the

results represent a cyclical scenario.

Table 5 Parameters inputted in the model's workspace for the 7-19 scenario.





EV’s battery third cycle energy [Wh] -





.

- 57 -

Figure 25 EV’s battery's energy representative throughout 24 hrs of a day’s trip-scenario.

Figure 26 Buffer battery's energy representative throughout 24 hrs of a day’s trip-scenario.

.

- 58 -

Figure 27 Buffer battery's energy representative throughout 24hrs of a day's trip-scenario,

where positive values is energy produced by the PV system flown into the buffer battery

while the negative values are flown into the grid.

Table 6 Total energy flow in the charging station under the day’s trip scenario.


9.61 19.98 13.88 9.80 kWh

- 59 -

4.3 7-16 Scenario

The 7-16 scenario represents a normal work commuting day that could happen during a

midweek day. The EV’s user drives the EV from home to her/his work station and back, this

situation would be the most suitable for a financial analysis, due to the regularity of its’

appearance throughout the year.

Table 7 Parameters inputted in the model's workspace for the 7-16 scenario.





EV’s battery third cycle energy [Wh] -





- 60 -

Figure 28 EV’s battery's energy representative throughout 24 hrs of a normal commute

trip-scenario.

Figure 29 Buffer battery's energy representative throughout 24 hrs of a normal commute

trip-scenario.

- 61 -

Figure 30 Buffer battery's energy representative throughout 24hrs of a normal commute

trip-scenario, where positive values are energy produced by the PV system flown into the

buffer battery while the negative values are flown into the grid.

Table 8 Total energy flow in the charging station under the commute trip-scenario.


9.61 19.66 2.47 5.86 kWh

The author considers imperative to conclude, in this chapter, that no intentions to represent

real-life scenarios were made and that the parameters selection of each scenario had the

objective to reproduce some of the characteristical behaviors that could be expected in

further development of this work and, specially, to display the capabilities of the software

achieved in this project. However, the first scenario’s energy output will be used as a context

to analyze the next chapter’s content considering that from the 3 test scenarios, the first,

could be the most real-life suitable.

- 62 -

5 Scenarios’ financial run

Initially, we consider two general possible scheme-scenarios in which the model could be

used. One, where the buffer battery transfers energy to both the EV’s battery – when in

demand - and, parallelly, to household devices. The second in which the buffer battery just

supplies the EV’s battery and not household demand – this last scenario fits the model better

since household parameters are not being considered part of the analysis. In Table 9, a

budget sheet displays the possible costs such a system would have. This, to understand the

financial context the direct utilization of the model would have. It’s necessary to assume an

average a family household would require in terms of energy demand to, even though

parallelly, consider both household and EV requirements.

Assuming the following facts, the creation of an average energy demand for a household

with one EV can be projected:

The average mileage a German person drives throughout the year is approx. 12,000

km/year (WiWo, 2015). – versus the approx. 18,000 km/year an EV could reach on

the first scenario considering that the specific signal builder would just apply during

working days and not during weekends -

The average electricity demand a one-family household - 4 persons living in a rural

house- has for a year sums up to approx. 4000 kWh (rw, 2018).

Besides these considerations, taking into account that a Renault Zoe Z40 with a battery

max. capacity of 41 kWh can have an effective mileage of almost 300 km, the approached

mileage of such an EV would be of 7.31 km/kWh, which results in approximately 4.5

kWh/day.

Using the model, a 10 kW PV-system is reproduced in order to have a daily avg. of energy

generation – under the same localization characteristics used for the last chapter – which

resulted in approx. 30 kWh/day under an average year’s representative day of radiation.

Taking all past considerations into account, a cost estimation for such a system is

exemplarily shown. All costs follow benchmarking prices and are considered in a competitive

range of net prices per concept.

- 63 -

The first cost sheet - as shown in Table 9 - corresponds to a buffer battery size of

9.8 kWh of capacity which is considered to averagely cover the demand of a standard

compact EV.

Table 9 Budget sheet with a 9.8 kWh buffer battery system8.

Considering the last budget sheet, we assume the following calculations. Since the EV has

a potential average mileage of approx. 60 km per day -considering 8.5 kWh of usage during

the day, while the EV’s energy demand is completely satisfied by the PV system in

combination with the buffer and that the fix electricity demand of a 4-persons household in

a rural area could go up to another 11 kWh/day we derive that:

𝐸𝑉 𝑒𝑛𝑒𝑟𝑔𝑦 𝑎𝑝𝑝𝑟𝑜𝑥. 𝑐𝑜𝑛𝑠𝑢𝑚𝑒 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 8.5 𝑘𝑊ℎ𝑑𝑎𝑦

∗ 365 𝑑𝑎𝑦𝑠 3102.50𝑘𝑊ℎ𝑦𝑒𝑎𝑟

And:

8 Price for each concept was retrieved from various internet retailers like www.greenakku.de, www.mg-solar-shop.de and www.esl-emobility.com .

- 64 -

𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑎𝑝𝑝𝑟𝑜𝑥. 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑟𝑜𝑠𝑠 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑑𝑎𝑦 11𝑘𝑊ℎ𝑑𝑎𝑦

∗ 0,25€

𝑘𝑊ℎ∗ 365 𝑑𝑎𝑦𝑠

1,003.75€

𝑦𝑒𝑎𝑟

Considering that the system with inverter - to allow household usage and grid conversion -

has an approx. gross cost of €17,457.90 , and bearing in mind that the simulation only

intends to supply the EV with energy, but not the household demand, we assume in

reference to Table 4:

𝑃𝑉 𝑡𝑜 𝐺𝑟𝑖𝑑 𝑎𝑡 𝑎 𝑝𝑟𝑖𝑐𝑒 𝑜𝑓 0.10€

𝑘𝑊ℎ 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 15.25 𝑘𝑊ℎ ∗ 0.10 1.53

€𝑑𝑎𝑦

∗ 365 𝑑𝑎𝑦𝑠

558.45€

𝑦𝑒𝑎𝑟

𝐸𝑛𝑒𝑟𝑔𝑦 𝑡𝑜𝑡𝑎𝑙 𝑔𝑟𝑜𝑠𝑠 𝑏𝑎𝑙𝑎𝑛𝑐𝑒

𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑎𝑝𝑝𝑟𝑜𝑥. 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑟𝑜𝑠𝑠 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 𝑃𝑉 𝑡𝑜 𝐺𝑟𝑖𝑑 𝑖𝑛 𝑎 𝑦𝑒𝑎𝑟

1,003.75€

𝑦𝑒𝑎𝑟558.45

€𝑦𝑒𝑎𝑟

445.3€

𝑦𝑒𝑎𝑟

Comparing it to a system with no buffer at all, the total gross cost of the system would be

€12,128.00 and the balance would be the following:

𝐸𝑛𝑒𝑟𝑔𝑦 𝑡𝑜𝑡𝑎𝑙 𝑔𝑟𝑜𝑠𝑠 𝑏𝑎𝑙𝑎𝑛𝑐𝑒

𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑎𝑝𝑝𝑟𝑜𝑥. 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑟𝑜𝑠𝑠 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 𝑃𝑉 𝑡𝑜 𝐺𝑟𝑖𝑑 𝑖𝑛 𝑎 𝑦𝑒𝑎𝑟

𝐸𝑉 𝑒𝑛𝑒𝑟𝑔𝑦 𝑑𝑒𝑚𝑎𝑛𝑑

1,003.75€

𝑦𝑒𝑎𝑟24.48𝑘𝑊ℎ ∗ 365 𝑑𝑎𝑦𝑠 ∗ 0.10

€𝑘𝑊ℎ

8.5 𝑘𝑊ℎ

∗ 365 𝑑𝑎𝑦𝑠 ∗ 0.25€/𝑘𝑊ℎ 885.86€

𝑦𝑒𝑎𝑟

- 65 -

Comparing both configurations of the systems under the same boundary conditions, we

would have:

𝑜𝑛𝑙𝑦 𝑃𝑉 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 𝑃𝑉 𝐵𝑢𝑓𝑓𝑒𝑟 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 885.86€

𝑦𝑒𝑎𝑟445.3

€𝑦𝑒𝑎𝑟

440.56€

𝑦𝑒𝑎𝑟

The break-even point of the investment would be:

17,457.90€

440.56€

𝑦𝑒𝑎𝑟

39.60 𝑦𝑒𝑎𝑟𝑠

We can conclude that the direct adaptation of the simulations scheme under this specific

scenario, would not make an attractive investment situation and a further model

implementation of a dynamic household energy demand would be imperative to achieve

real-life financial attractive scenarios.

- 66 -

6 Further development and appliances

The general framework of this project covers a limited application scenario, in which the

model primary focuses on the behavior a buffer battery in function of a PV system would

have. Concluding that a certain sized buffer battery would cover the demand of other

electronics, e.g. in a household, is only theoretical since the demand fluctuates in function

of the household’s specific electricity demand. In order to make the simulation realistic

enough for further appliance in the market, it is necessary to implement the household factor

in the model, preferably aggregating an average consumption per day. On the other hand,

the model works optimally for forecasting an off-grid buffer-PV-EV system. Nevertheless,

the consideration in the model of the household’s electricity demand in function of a time

during a day could be beneficial for the final user since it could give a more precise

estimation as of the possible demand coverage the buffer is capable to supply.

Physically and thermodynamically, the limitations of the model could affect results in a long-

time span. The author judges the degree of the impact this aspect could have in the

simulation as uncertain, but the referred studies usually agree that it is up to 2-3% of the

energy output that could be affected by chemistry and thermodynamic exchanges inside the

battery.

As for this project, the model can only achieve just one-day simulations, the user must

extend the results in order to have a yearly outcome. As mentioned before, the author

suggests that resolving a daily base could resolve, through easy algorithmic scalation, the

necessity of having longer terms of analysis. Unfortunately, representative weather – PV

output - is exclusively an average which outcome into a relevant uncertainty factor for the

results’ interpretation. The author sees this as a limitation of the approach which needs

further research.

Besides the last stated recommendation, for further studies before implementation there are

two main concepts that are gaining acceptance in the modern EV mobility market to which

the model could be applied to:

- 67 -

6.1 Carsharing

A car-sharing system is defined as “¨[…] a service that provides members with access to an

automobile for short-term – usually hourly – use. The shared cars are distributed across a

network of locations within a metropolitan area. […]” [This model of car-sharing is called

point-to-point.]” […] Members can access the vehicles at any time with a reservation and

are charged by either time or distance […]” (Shared-Use-Mobility-Center, 2018). Users

benefit from this type of system by avoiding to own a vehicle and having to worry about

vehicle expenses such as insurance, spare parts, state controls and fuel, among others.

Around the world, several models of carsharing are currently operating. One of the most

representative examples in Germany is DriveNow. The carsharing platform has shown great

success reflected in its continuous expansion across the country. Some other examples are

Flinkster and Car2go. Among these, operators provide about 1,170,000 users nationwide

with carsharing services, either in station-based mode - where at the beginning and end of

the trip, the vehicle has to be located in a geolocated space by the provider -, or based on

non-station mode - where the vehicle can be parked anywhere within a virtual perimeter no

matter where specifically. An almost exponential trend can be observed in Figure 32, as a

consequence of the popularity of the carsharing market. It shows that this business model

is a serious rival to the traditional model of privatization, where an own vehicle is acquired.

However, vehicles oriented on carsharing platforms are usually internal combustion ones.

New is the case of DriveNow with the integration of the electric BMW i3 model that is found

as an optional vehicle within some cities in Germany. On the other hand, the vehicle fleet

comprised by the i3 models is still relatively small compared to the fleet of internal

combustion vehicles. Unfortunately, carsharing companies do not publish their internal

reports of results to be able to do a detailed analysis of their electric fleet.

- 68 -

6.2 V2G

A possible usage of the model could be used under a ‘pick-demand-hours’ scheme:

The prices of energy fluctuate during the day, which is administrated through the so called

Intraday-Market (Bundesregierung, 2014). As a thinkable scenario, for a company providing

car sharing services – like Car2Go for example – could have the following resources under

its ownership: an electric vehicle, an electric vehicle supply equipment (EVSE) and a

privately-owned PV system located on urban areas. During the period of the timeline with

the greatest energy demand in the city, the company could have the EV connected to the

EVSE. The PV system could generate enough power to power the EV. The energy

management platform would allow the EVSE to decide which would be more efficient from

the commercial point of view: supply the EV battery with the generated electricity from the

PV system or use the remaining energy of the EV to sell it to the market. The network is

schematically presented in Figure 31.

The main current discussion with the conceptual vehicle to grid is the acceleration in the

degradation of the effective capacity a battery can have. Since the battery cycles happen

more frequently than in a normal cycling situation - the battery is discharged even when the

EV is not in effective use – the battery degradation costs mostly challenge the possible

economic feasibility V2G can originate according to Gough R. et al.(2017). Eventually, the

only possible scenario in which V2G can represent a sustainable/optimal financial

investment alternative is one in which the battery technology costs have a considerable

reduction in the next years. The tool developed in this project can recreate a precise

panorama if the discharging algorithm is structured in the model. In other words, the

fundamentals for such implementation are founded in this study. Such algorithm should

contemplate the dynamic of the intra-day market – for trading possibilities – and at the same

time the possibility of aggregating the variable of peak-hours electricity demand of populated

buildings in order to create a live-dynamic energy management platform in which the EV

represents an energy storage that can distribute energy in an immediate frame, making an

attractive market for carsharing platforms, where these are able to provide immediate energy

flows in case of high demand peak-hours. This could represent a considerable watershed in

the future of energy production, after renewable energy can’t currently be stored for non-

- 69 -

immediate usage. V2G is considered, still, one possible scheme to alleviate this energy

challenge.

Figure 31 Schematic representation of the concept. (Dennis van der Meer, 2016)

- 70 -

Figure 32."Carsharing increases its popularity" Number of registered carsharing users in

Germany (in 1,000s) (Carsharing, 2017)

- 71 -

7 Conclusions

This study contributes to the development of an alternative system to forecast the energy

flow a buffer-PV system charging station could have, based on Matlab Simulink and

Microsoft Excel. This model offers a foundation for a further product’s development that

could be used by final users or energy consultants to predict, with relative precision, if the

acquisition of a buffer battery connected to a PV system would be economically viable. Even

though there are a substantial number of variables that are not being considered in the

model, the objective to create an existing system with algorithmically differences was

achieved. The model was tested in different scenarios that recreated the expected results.

The development of diverse ways to accomplish a similar objective, which is the energy

management in an EV’s charging station, enables a wide exploration spectrum and provides

diverse tools for researchers to continue further necessary investigations in the energy

management science. This model can assist further developers or even final users to

analyze how financially viable the acquisition of a PV system with/or a buffer battery could

be. At the same time, the results of this study can enhance the use of renewables for future

grid-independent electric vehicles, considering the possible risk of an excess of EV’s could

have in the grid’s expansion forecast.

Some of the most frequent obstacles in developing the model were the conceptualization of

creating an algorithm based on an expected behavior, in other words, converting an

expectation into a desired behavior in Matlab Simulink. A concrete example was the use of

a script to be able to present an output of accumulated energy values in each of the

subsystems of the model in a way in which less block construction would be needed. The

author opted for programming lines in Matlab to present the values that are transferred from

the PV system to the buffer, the buffer to the EV, the grid to the EV and from the PV system

to the grid. This significantly reduced the time invested that could have been used to develop

construction in the model itself.

However, the number of limitations acquired during the development of the model

considerably affects the level of certainty in the results: One is the stepping pace. Since the

simulation only runs by representative one-day calculations at a specific 5 min-steps and

- 72 -

outputs exclusively one-day results, the user is limited to use 5 min-steps records of the PV

system database to input information into the model. The model can be, nevertheless,

configured to adapt to new pacing orders if the developer/user requires to do so. Besides

this, the author remarks, for further development, that the simulation has to be improved,

presentational wise, so that the interface could be more user friendly and better understood

by academics or users in general that do not have expertise with the platform.

The possession of a previously recorded generation database of a certain PV system

necessary to run the simulation or, even more relevant, the uncertainty in the driving

behaviors that are used to provide a signal builder in the simulation, are some of the aspects

the author recommends to be further investigated, since there is a considerably amount of

vagueness in the validity of the output values the present simulation provides.

Perhaps the most relevant factor that affected the practical usage of the model – in terms of

financial viability - was the no consideration of the household energy demand in the work

frame of the model’s environment. In the time-framework of this master thesis this issue

could not be included in the study.In addition to this factor, the signal builder that represents

the behavior of the EV when connecting to the charging station, also has the potential to be

substantially improved to represent in a more accurate way the different iterations that an

EV can have. Different factors could be added to the improvement of the signal builder, such

as the location of the charging station, if the vehicle is part of a carsharing platform’s fleet, if

the charging station is public, the possibility to perform V2G exchange, among others.

Although the present work develops a good foundation for the energy management of an

EV charging station in conjunction with a PV system and a buffer battery, there are various

options for improvement. The author recommends to focus on the previously mentioned

aspects for further development of a more user-adapted model.

- 73 -

8 Bibliography

(NREL), N. R. (2018). https://www.nrel.gov/.

ADAC. (26.02.2019). www.adac.de. Obtained from https://www.adac.de/rund-ums-

fahrzeug/e-mobilitaet/kaufen/neue-elektroautos/

Appunn, K. (07.02.2019). https://www.cleanenergywire.org. Obtained from

https://www.cleanenergywire.org/factsheets/coal-germany

Bloomberg. (16.10.2018). www.industryweek.com. Obtained from

https://www.industryweek.com/technology-and-iiot/lithium-batteries-dirty-secret-

manufacturing-them-leaves-massive-carbon

Bundesregierung. (5.8.2014). www.bundesregierung.de. Obtained from

https://www.bundesregierung.de/Content/DE/Artikel/2014/08/2014-08-04-so-

funktioniert-der%20strommarkt.html

Carsharing, B. (21.02.2017). www.carsharing.de. Obtained from

https://de.statista.com/infografik/8187/carsharing-nutzer-in-deutschland/

Dennis van der Meer, G. R. (2016). Energy Management System with PV Power Forecast

to Optimally Charge EVs at the Workplace. IEEE TRANSACTIONS ON

INDUSTRIAL INFORMATICS, 10.

Energy, G. F. (2017). https://www.bmwi.de. Obtained from

https://www.bmwi.de/Redaktion/EN/Dossier/renewable-energy.html

Energy, G. F. (15.10.2018). www.bmwi.de. Obtained from

https://www.bmwi.de/Redaktion/DE/Artikel/Energie/strompreise-bestandteile.html

Enkhardt, S. (30.11.2018). PV Magazine. Obtained from www.pv-magazine.com:

https://www.pv-magazine.com/2018/11/30/germany-added-182-mw-of-new-pv-

capacity-in-october/

- 74 -

EPEX SPOT. (15.10.2018). www.epexspot.com. Obtained from

https://www.epexspot.com/en/extras/download-center

Fairley, P. (2013). Car sharing could be the EV's killer app. Inst. Electr. Electron. Eng. -

Spectrum, 50 (9), 14–15.

Finanztip. (21.08.2018). www.finanztip.de. Obtained from

https://www.finanztip.de/photovoltaik/stromspeicher/

Francesco Marra, G. Y. (2012). Demand Profile Study of Battery Electric Vehicle under

Different Charging Options. Denmark: Danmarks Tekniske Universitet.

GOUGH, R. e. (2017). Vehicle-to-grid feasibility: A techno-economic analysis of EV-based

energy storage. Applied Energy, 192.

Graulich, B. H. (2018). Use and financial feasibility of photovoltaic-battery systems in

combination with electricity savings. Freiburg: BMBF.

Hall, M. (17.1.2019). www.pv-magazine.com. Obtained from https://www.pv-

magazine.com/2019/01/17/perc-cell-efficiency-records-tumble/

Insider, B. (11.01.2018). www.Businessinsider.de. Obtained from

https://www.businessinsider.de/electric-vehicle-cost-mistake-2018-1?r=US&IR=T

Ionin, V. (01.11.2018). help.aurorasolar.com. Obtained from

https://help.aurorasolar.com/hc/en-us/articles/235994088-System-Loss-Diagram

July Thomas, V. S. (17.11.2018). www.brillian.org. Obtained from https://brilliant.org/wiki/rc-

circuits-dc/

Kroher, T. (20.03.2018). ADAC. Obtained from https://www.adac.de/der-

adac/motorwelt/reportagen-berichte/auto-innovation/studie-oekobilanz-pkw-

antriebe-2018/

Lima, P. (9.10.2016). www.pushevs.com. Obtained from

https://pushevs.com/2016/10/09/renault-zoe-ze-40-battery-details/

Lodberg Høj, J. C. (2018). V2G—An Economic Gamechanger in E-Mobility? World Electric

Vehicle Journal, 7.

- 75 -

Manuel Fuentes, J. F.-A.-M. (2017). Feasibility Study of a Building-Integrated PV Manager

to Power a Last-Mile Electric Vehicle Sharing System. International Journal of

Photoenergy, 12 pages.

Mohsan Jameel, O. F.-S. (2017). Empirical Study of Using Renewable. En V. W. al.,

Advances and New Trends in Environmental Informatics (págs. 197-207).

Switzerland : Springer International Publishing .

NASA. (24.5.2018). climate.nasa.gos/evidence. Obtained from

https://climate.nasa.gov/evidence/

NASA. (10.10.2018). NASA's global climate change . Obtained from

https://climate.nasa.gov/effects/

Rebecca Gough, C. D. (2017). Vehicle-to-grid feasibility: A technoeconomic analysis of EV-

based energy storage. Applied Energy, 192, pp. 12-23.

rw, d.-t.-o. (25.12.2018). www.t-online.de. Obtained from https://www.t-online.de/heim-

garten/energie/id_78249250/stromverbrauch-im-haushalt-wie-viel-ist-noch-normal-

.html

Schneider, L. (15.01.2019). www.ingenieur.de. Obtained from

https://www.ingenieur.de/technik/fachbereiche/e-mobilitaet/die-10-beliebtesten-

elektroautos-deutschen/

Schwarzer, C. M. (28.03.2018). Heise. Obtained from www.heise.de:

https://www.heise.de/autos/artikel/Frischzellenkur-Batterie-Aufarbeitung-bei-

Nissan-4006130.html

Shared-Use-Mobility-Center. (25.09.2018). Obtained from

http://sharedusemobilitycenter.org/what-is-shared-mobility/

Statista. (2018). de.statista.com. Obtained from

https://de.statista.com/statistik/daten/studie/12131/umfrage/pkw-bestand-in-

deutschland/

Tulpule, P. J. (2013). Economic and environmental impacts of a PV powered workplace

parking garage charging station. Applied Energy 108, 323–332.

- 76 -

UNDP. (15.1.2019). www.undp.org. Obtained from

http://www.undp.org/content/undp/en/home/sustainable-development-goals.html

Victor D.G., D. Z.-H. (2014). Introductory Chapter. In: Climate Change 2014: Mitigation of

Climate Change. Contribution of Working Group III to the Fifth Assessment Report

of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge

University Press.

WiWo. (17.03.2015). www.wiwo.de. Obtained from

https://www.wiwo.de/technologie/mobilitaet/fahrverhalten-so-viele-kilometer-fahren-

die-deutschen-im-jahr/11511178.html

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