A BRIEF INTRODUCTION INTO EV CHARGING INFRASTRUCTURE

RESEARCH

Name: Drs. Ir. JR Helmus Colloquium: Computational science lab Date: 2016-02-19

Contents of this presentation

1. Introduction about myself2. Context of electric vehicles 3. The IDO-LAAD project4. Introduction in the dataset5. A rollout-strategy assessment (J.C. Spoelstra, J. Helmus, 2015)6. Taxonomy of EV users charging behavior (J. Helmus, vd hoed 2015)7. Future research – Vehicle to grid solutions

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@JRHelmus / 35 yrs/ Father / Fiat X1-9 / AmersfoortNL Executive board PDMA / Lateral thinker

@HvA – lecturer innovation management / Senior researcher e-mobility

@UvA CSL – PhD research on Agent based model for optimizing

EV charging infrastructure

IT architecture for Basel II compliancy in non-ERP systems

European Central bank set out Basel II (from 2019 Basel III) legislation to govern the system stability of European Banks.

This requires• Banks to implement specific information in IT systems in order to comply with Basel II reporting• Banks to setup a credit risk evaluation framework for loans and loan portfolios. • Banks to report quarterly / monthly / yearly on a growing amount of reports

For large banks with large adaptive financial ERP systems this is a matter of performing a large IT project. For small banks the Basel II compliancy is a more difficult due to a scattered IT landscape.

How to setup a IT architecture that enables consistent reporting on non-ERP systems within the whole organization in order to be Basel II complient?

Modelling heat and mass transfer of milk powder in a shallow vibrating fluidized bed Develop a multiscale model that enables both R&D as well as process engineers to calculate process conditions and equipment requirements for the last phase of the milk drying process.

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Meso scale –rheology in CISTR

Macro scale –total fluidized bed

Macro model with parameters that enable process engineers to calculate optimal equipment specification and priceMeso model with parameters that enable R&D engineers to optimize rheology and drying process within the bedMicro model with parameters that enable R&D engineers to do product and process innovation (eg. new powder type)

The Netherlands is a frontrunner in E-mobility• Forced by EU emission regulations and clean air policies. • For example, Amsterdam aims to have an emission free city center in 2025• Vision is that charging infrastructure might not be the main barrier to switch to EV from ICE • Broader vision on sustanable energy transition in the netherlands (energie akkoord, 2015)

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Main problem to solve: optimization of charging infrastructure usage and rollout

Typical Problems in EV charging infra field

• Investments in charging infrastructure is expensive (as in ~4-6k per charging point, 20-40k Fast charger)

• The business case for charging point operators is barely positive. Market takeover could be far away.

• Charging infrastructure rollout strategies are either slow (demand-driven) or uncertain (forecast driven). (Funke et al., 2015)

• Energy sales prices are regulated and capped (.028¢) whereas energy costs prices fluctuate over the day (on/off peak) due to energy production

• Energy grid capacity costs are relatively high compared to sales prices, whereas full power is almost never used currently

• New business models for smart EV charging in combination with energy transition (V2X) are needed (Bohnsack et al, 2015)

The University of Applied Sciences Amsterdam

The UASA Urban Technology research program supports stakeholders within the charging infrastructure ecosystem by developing and monitoring rollout strategies

Monitoring performance Predictive analytics Modelling & simulation

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Total beforecleansing

Connection timerepair

Physicly impossiblecharge sessions

Unknown data Double records Short time Double provider Net usable records

Causes of ~35% data removal per error type

source: website urban technology

From Helmus vd. Hoed, (2015)

2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 122012 2013 2014

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CentrumNieuw-WestNoordOostWestWestpoortZuidZuidoost

Maand/Jaar

kWh

Amount of kWh charged per month per city district

The 167 Tesla taxis merely live in the Amterdam Nieuw-West area, where they charge overnight.

More than 135 reports were developed for stakeholders to support and monitor effective EV infrastructure rolloutFor example new modalities such as Tesla taxis are closely monitored

Source: CHIEF database

The dataset ~2M transactions from 50K, ~5k charging stations users for this research is unique in the world

Parameter Example Explanation

Charge point address

Admiralengracht 44

Adress of the charge point

Charge point operator

Nuon Owner of the charge point

Charging service provider

Essent Owner of the used charging card

Charge point city

Amsterdam

Charge point postal code

1057EW ZIP code of the area of the charge point

Volume 0,86 Charged energy [kWh]

Connection time

0:14:23 Time the car was connected

Start Date 18-04-2012 Date the session started

End Date 18-04-2012 Date the session ended

Start Time 23:20:55 Time the session started

End Time 23:35:18 Time the session ended

Charging time

0:14:23 Time the car is actually charging

RFID 60DF4D78 RFID code of a charging card

Charge volume

Charge point address

Connection time

RFID

The data is anonimized de RFID tag is not coupled to personal information in this dataset. De SQL server database is behind VPN and firewall en HvA has exclusive right to publish on scientific results.

Participating geographic areas- Amsterdam /Rotterdam /the Hague /Utrecht- Metropool Regio Amsterdam (from Haarlem to Amersfoort)- Evnet in whole of Netherlands

Deeper insight- Specific charging cards for Taxi, Car2Go, Entrepreneurs, RDW,

and more to come- Implemented interventions in G4 (Parking) and EVNET (price)

Timing spectrum- From real-time to monthly from first charging point 2012 to 5 min ago

From vd. Hoed, Helmus, Bardok (2014)

De data van Amsterdam is exceptional

4933 charging sessions in een test environment

In current state of literature there is no such dataset for scientific usage in dense metropolean areas public charging infrastructure.

10 months data 10 EV drivers

No EV data available – GPS location of non EV users

36 mln km’s / 28,000 vehicles 0 EV’s

The IDOLAAD subsidy program (4 years) supports this research and requires deliverables as well

Research team - 1 lector- 3 Post doc senior researchers - 2 PhD students - 3 junior researchers (Applied mathematicians)- 3 IT specialists - Computational science lab

Consortium participants (deliver data and cases and work)- 4 largest cities and areas surrounding - 3 charging infrastructure providers (delivering data)- 1 charging point producer - 1 network grid provider- 1 consultancy firm (specialized in EV interventions)

Expectations- Prediction model for local demand (not this PhD study)- Simulation model for optimizing charging infrastructure usage and

rollout (as always ASAP)

Working packages Research team and participants

From IDOLAAD proposal, Hoed, Maase, Helmus (2014)

Program goal: develop insights that enable stakeholders within EV value chain to have a effective rollout and efficient use of charging infrastructure

General methodology of IDOLAAD research

My PhD project at CSL

Goal of this PhD research: Deliver insight and tools for interventions for professionals in the EV value chain that enable optimization of both usage as well as future rollout of charging infrastructure.

Fundamental research question: Which factors determine EV users

charging behavior?

Applied research question: How can EV users charging behavior be integrated

in a spatiotemporal simulation model in order to gain insight in optimization mechanisms for charging infrastructure efficiency?

From PhD proposal, Helmus (2015)

Public Charging infrastructure use in the Netherlands: A rollout-strategy assessment

Research by Amsterdam University of Applied Sciences and ElaadNLLarge Dutch EV charging infrastructure rollout 2009 – 2014Demand-driven and Strategic strategies

How does the rollout-strategy of the public EVnetNL charging points in the Netherlands influence the use of these charging points?

From J.C. Spoelstra, J. Helmus (2015)

Rollout StrategiesLocal/regional governments apply for EV charging pointSubsidised by National Government, performed by EVnetNL

Strategic

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Demand-driven

? ?

From J.C. Spoelstra, J. Helmus (2015)

Historical context of charging point rollout

Strategic

Demand-driven

Unknown

 

Figure 2: Public charging point locations in the Netherlands on January 1st 2015

2012 2012 23 2012 45 2013 15 2013 37 2014 7 2014 29 2014 510

200400600800

100012001400160018002000 Charging point rollout

Demand-driven Strategic

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Figure 1: Public EVnetNL charging point rollout between 2012 and February 2015

• More strategic charging points in January 2012 (480 vs. 54)• Comparable growth in numbers (631 vs. 673)• Demand-driven charging points primarily in highly populated

urbanized areas• Several, primarily strategic, charging points in low populated

rural areas

(J.C. Spoelstra, J. Helmus, 2015)

Charging point use: Energy transfer & Connection times

2012 2012 242012 472013 182013 412014 122014 35 2015 65

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Average energy transfer per transaction

Demand-driven Strategic

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Energytransfer distribution

Demand-driven Strategic

kWh

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,1 k

Wh

Figure 5: Average energy transfer (kWh) per transaction per week for the two rollout-strategies, 2012 – Feb 2015

Figure 6: Distribution of charging transactions per 0,1 kWh energy transfer

2012 2012 252012 492013 212013 452014 172014 4105:0007:0009:0011:0013:0015:0017:0019:00

Average connection duration per transaction

Demand-driven Strategic

TimeAver

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Connection time distributionDemand-driven Strategic

Connection time (hours)

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Figure 7: Average connection duration per transaction per week for the two rollout strategies, 2012 – Feb 2015

Figure 8: Distribution of charging transactions per 5 minutes connection duration (0-24 hours)

From J.C. Spoelstra, J. Helmus (2015)

Taxonomy of EV user charging behavior

Aim: develop a set of different meaningful EV user types with specific properties related to charging behavior from the data (1) to be used in an agent based model(2) develop algorithm that continuously scans for new user types/changes over time (new cars, user types, etc) (3) expand taxonomy for more than 1 city (make uniform)(4) rerun algorithm if new data/insight/city is added

(e.g. run in production environment)

This paper will be a successor of the paper presented at EVS28 EVS28 KINTEX, Korea, May 3-6, 2015 Unraveling User Type Characteristics: Towards a Taxonomy for Charging Infrastructure J. Helmus MSc. R. van den Hoed MSc PhD

Dimensions of Charging behavior

Charging behavior has dimensions with measureable parameters of charging behavior:(1) WHEN - start / end times (2) WHERE – area type/ (amount) locations(3) WHAT - kWh / State of Charging (SOC) / duration(4) LONG - activity during life span and time between sessions(5) EV usage determined – max battery capacity / range / (6) PREDICTABILITY – constancy of aforementioned parameters *(7) SENSITIVITY – to external conditions (weather, parking pressure, interventions)

**Relation driver and origin destination matrix(1) one-to-one – one driver one car (residents, commuters, etc)(2a) one-to-several (entrepreneurs in logistics)(2b) one-to-several combined with one-to-one (taxi with home charging as well)(3) many-to-many – car sharing scheme (both private as large)

Full overview of charging behavior parametersSign Residents Commuters Taxis Visitors Car sharing RC chargers

Start time

Weekdays between 5:00 PM and 7:00 PM with low standard deviation

Weekdays between 7:30 AM and 9:00 AM with low to medium standard deviation

Varies, at night, around midday at residential zones, spread over the day in other zones

Mostly between 10:00 AM and 2:00 PM, high standard deviation

All day, standard deviation irrelevant

Both resident and commuter times

End time

Next morning between 7:00 AM and 9:00 AM hours with low standard deviation

Same day around 6 PM with low standard deviation

Varies, in the early morning,

Mostly between 6:00 PM and 22:00 PM, high standard deviation

All day, standard deviation irrelevant

Both resident and commuter times

Duration

Long, around 8 hours with low standard deviation

Long, around 8 hours with low standard deviation

Short, 4 hours Long, 8 hours Very short (around 4 hours), except for charging sessions starting at night

Both resident and commuter aspects

TBSweekdays

Medium around 10 hours, consistent per user with low standard deviation

Medium around 10 hours, consistent per user with low standard deviation

Low (2 to 4 hours) to medium (6-8 hours)

Very long (varying from days to weeks), inconsistent, high standard deviation

Very short (2-4 hours) with high standard deviation

Both resident and commuter

TBSweekends

Medium consistency per user. medium standard deviation

Inconsistent per user. High standard deviation

Low (2 to 4 hours) to medium (6-8 hours)

Very long (varying from days to weeks), inconsistent, high standard deviation

Very short (2-4 hours) with high standard deviation

Inconsistent, medium standard deviation

kWh

Mean 60% of max kWh of car type, ,medium standard deviation

Mean 80% of max kWh of car type, medium standard deviation

Inconsistent per session, high standard deviation, many times max kWh of car battery

Inconsistent per user, high standard deviation across all user types even with same cars

Median around 60% of max battery capacity mean is slightly lower.

Consistent per user, could vary based on early or later charging session at a specific day

Charge point volatility

Mean low (1-5) for absolute volatility, individual users could deviate

Very low (1-3) absolute volatility

Medium (around 10), except for home charging between shifts

High over all users, medium (around 8) per user

High > 80 charging points used, absolute volatility and relative volatility high as well

Medium (around 10)

Time Ratio

Low, consistent around 20-30%

Medium, per user consistent around 20-30%

High on the short sessions medium on the longer sessions (around 40%)

Low, inconsistent around 20-30%

high, many times 100% mean around 40%

Low, inconsistent around 20-30%

C,L,kWhMedium (~0.5), varies per user

Low to none (0 to 0.2), varies per user

Medium (~0.5), depending on starting time

Low to none, varies per user

High (~0.8), for all users Medium to high (0.5 to 0.8)

C,S,TR

High correlation between, later arrival tends to result in higher time ratio

High correlation between, later arrival tends to result in higher time ratio

Low for all users Low for all users Low for all users Low, due to the combination of both patterns

Pattern type

Valley hill Multiple saw teeth Flatt with possible peaks Valley at daytimes when driving flat at night

Flat pattern with two small valleys for travel time

Some typical examples of connection profiles

1 3 5 7 9 11 13 15 17 19 21 230.002.004.006.008.00

10.0012.0014.0016.0018.00

Office charger**

0 2 4 6 8 10 12 14 16 18 20 220.00

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6.00Car sharing car

1 2 3 4 5 6 7 8 9 1011121314151617181920212223240.00

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8.00Early night charger

1 2 3 4 5 6 7 8 9 1011121314151617181920212223240.00

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7.00 Late night charger

1 2 3 4 5 6 7 8 9 1011121314151617181920212223240.00

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9.00 Visitor*

0 2 4 6 8 10 12 14 16 18 20 2202468

101214161820

Taxi

From J. Helmus, R. van den Hoed ( 2015)

Plots display percentage of total hours connected at specific hour (1-24) - total 100%

Typical charging point connection profiles

Profiel AJ Ernststraat 2014

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Hours of the day

Profiel Abbenesstraat

Plots display total connection hours per hour (1-24) on a single charging point

Connection profiles for charging points are a portfolio of charging sessions of users at a charging station.

Let’s take a short look at the (smart) grid

Time Ratio is a leading factor for V2X applicationsThe time ratio is defined as the charge time divided by the connection time

Power delivery

Net Charging

PV charging

Net Charging

Power deliveryLow time rato

High time ratio

Slack

Note:1. Sessions with time ratio <<100% are best usable for V2X applications2. Sessions with time ratio of 100% are not useful for V2X, these mostly occur at car sharing sessionIn Amsterdam the non-smart charging

points directly start charging after connection3. Slack exists only after charging is finished while connection remains4. To identify max battery capacity the data requires 1 time ratio 100% session and 1 << 100% session

No slack for power delivery/ postponing or slower charging, low V2X potential

Slack for other charging modalities, thus high V2X potential

J. Helmus, V2X congres Amsterdam (2015)

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Note: for this graph a subset of the data was used since not all charging times are present in the data

Predictive V2X technology based on charging behavior reveals sweet spots for different applicationsThe dispersion in the graph is indicative for the predictability of the time ratio.

Time ratio versus kWh charged for Amsterdam

Potential sweet spot for peak shaving

Potential sweet spot for power delivery

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Source: CHIEF database

Sweet spots in the Amsterdam Area can be found using complex algorithms

Currently available data• The avg kWh charged per session to per area per user• The potential left kWh at start of charging per • The Time ratio in combintion with Clustered user types with same high v2X

potential

Curently not available data• The available PV cells per m2 per household to be connected to the V2x solution

needs to be substantial• The hourly used kWh of households within a selected area gathered by the smart

metering systems

And of courseThe local grid must allow V2X technology to be implemented

In order to be a sweet spot for V2X the following requirements should be fulfilled

Note: Of course we are more than willing to collaborate with data providers in European projects

The avg kWh charged per session per user on a charging point reveals several potential V2x clusters

Map of Amsterdam with avg kWh per user

Source: CHIEF database

Future research for future solutions- A way of digging the

goldmine together

• How can future charging behavior and its impact on the public charging infrastructure be simulated based on historical user profiles of Tesla owners?

• Can we develop a model that is able to project the use of charging infrastructure by car sharing programs on other cities based on typical user behavior and local circumstances?

• What could be the effect of smart charging in metropolitan areas such as Amsterdam for different stakeholders within the value chain of electric mobility?

ANY QUESTIONS