An Accessible Predesign Calculation Tool to Support EV … Accessible... · 2015-01-31 · An Accessible Predesign Calculation Tool to Support EV Components Definition Topic: Electric

EVS28 International Electric Vehicle Symposium and Exhibition 1

Goyang, Korea, May 3-6, 2015

An Accessible Predesign Calculation Tool to Support EV

Components Definition

Topic: Electric Vehicle, Presentation preference: Lecture session

Roche, Sabrià, Mammetti

Marina Roche Arroyos, Applus IDIADA group, [email protected]

Dídac Sabrià, Applus IDIADA group, [email protected]

Marco Mammetti, Applus IDIADA group, [email protected]

Abstract

The new freedoms in design that electric powertrains provide lead to a wide variety of configurations to

consider when developing an electric vehicle (EV) from scratch. Furthermore, the strong relation of the

battery size with vehicle weight, range and performances leads to a set of interrelated dependencies that

can result in many design loops to fulfil the targets and regulations simultaneously.

The paper presents a tool that integrates the main relations regarding vehicle targets, market and

regulations constraints and plots them as restrictions for vehicle development. As a result, the tool depicts

a set of feasible vehicle configurations that could fulfil the targets. Furthermore, to better assist selection,

it also provides a sensitivity analysis of the performances and the user can introduce a cost function

depending on vehicle weight and battery size. The tool is aimed at providing an overview of the possible

solutions and guide for component selection in the early predesign phase in which vehicle characteristics

and even powertrain architecture are unknown.

Finally, the tool is evaluated by modelling one of its solutions for passenger car for three different

architectures in the simulation software vemSim. Furthermore, for one of the architectures, two control

strategies were simulated, leading to a total of four simulations. The results of the simulations are

compared to the solution of the predesign tool to evaluate the level of fidelity and the deviations in the final

result that can appear depending on the architecture, components and control strategy.

Keywords: design, energy consumption, definition, simulation, performances

1 Introduction

Nowadays, transportation faces higher energy

costs and continuously increasing restrictive

emission targets, aiming at 95 g/km CO2 by the

year 2020. This implies the necessity of a

significant change in road vehicle propulsion

technologies. This necessity is especially patent

in dense urban areas with high traffic volumes,

polluted atmosphere and high noise levels. Fully

electric vehicles (EVs) offer the potential to be

locally emission free and recover energy through

regeneration while meeting the individual mobility

demand of passenger cars as well as fleet vehicles.

In the recent years IDIADA has participated in

several EV research and development projects,


namely ELVA, Electric Race Car, More Zero,

IMPROVE and Puma Mind (European

Cofounded projects), eTruck and VeLoW

(locally cofounded projects), and iShare,

iTorque, eValuate and vemSim (internally

funded projects) as well projects for clients.

Based on the knowledge obtained in these

projects, IDIADA has developed an interactive

tool aimed at assisting EV design in the very first

development steps.

The main advantage of this tool is that it

interconnects calculations that typically involve

different departments in a loop; for example, the

target range and battery chemistry influence the

battery size, which affects the package and

vehicle weight, and thus the consumption and

range in an iterative loop. The tool was intended

to provide the user a first insight from a design

point of view and sufficient orientative

information on the vehicle requirements to fulfil

the performance targets regardless of the

powertrain architecture. A first approximation of

a vehicle solution which fulfils all these targets

and the market and regulations constraints is key

to speeding up the development process and

select the best powertrain architecture for

packaging, performances and cost.

The paper is structured in two main parts: the

first one describes the interactive tool that

models the constraints for the vehicle

development and shows a result example.

Afterwards, the same example vehicle is further

designed for three different architectures by

modelling each configuration in the in-hose

developed simulation software vemSim. The

required vehicle specifications are re-calculated

in more detail for each architecture of EV.

Finally, the results from the pre-design tool and

the detailed simulation models are compared to

show that the pre-design tool can provide a good

guideline for component selection when the

package and the architecture is still to be

decided.

2 Pre-design Tool

The pre-design tool was aimed at defining the

problem of EV development from an integrated

point of view. Commonly, a powertrain

definition with the eye on consumption and

performance cannot meet the dynamic targets or

may not be feasible for packaging. This situation

leads to a loop of iterations among departments

that modify the vehicle characteristics and affect

other department targets until a compromise is

reached.

The aim of the tool, despite its simplicity, is to

provide sufficient information on the vehicle

requirements to fulfil different targets

simultaneously in order to reduce the iterations

among departments. This solution can be used as a

specification to select the components and start the

packaging saving many design loops.

The process to find a solution is focused on the

design and package point of view: what volume

should I keep for the battery? What is my weight

target? How much the battery will weight? Where

are the powertrain components located? The main

targets and regulations to be fulfilled were

translated to weight and volume targets, leading to

a solution in terms of these two variables that

fulfils the requirements of different departments

and regulations simultaneously.

The pre-design tool was developed in Matlab.

Figure 1 shows the tool GUI in which the user can

select the inputs from sliders or also type in the

value. The dynamic GUI is programmed so the

default, maximum and minimum values for each

field are refreshed automatically when the user

changes the vehicle segment. The result of the

calculation is instantaneously refreshed as the user

moves a slider or modifies an input value. The

main parameters that affect vehicle longitudinal

dynamics [4] and targets are present in the

interface:

- 𝑖: Vehicle segment

- battery type: default chemistries [1].

- εbat: battery vol. energy density (kWh/l)

- ρbat: battery density (kg/l)

- πbat: battery vol. power density (kW/l)

- η: average powertrain efficiency (%)

- Af: aerodynamic frontal area (m2)

- Cx: aerodynamic drag coefficient

- PAux: auxiliaries power consumption (W)

- f: rolling resistance [5] (kg/ton)

- x: motor torque characteristic (see Figure 3)

- Reg: recovered energy in deceleration (%)

- Irot: rotating parts equivalent inertia (%)

- mload: playload mass (kg) (if applies)

- R1−3 : vehicle range targets in up to three

cycles (loaded or uloaded).

- vmax: target vehicle speed (m/s).

- ta: target 0-100 km/h acceleration (s)

- tan(α): gradeability target (%)


Figure 1: Interface with highlighted inputs.

As all the main parameters are interrelated (range

- battery capacity - volume and weight - vehicle

weight – consumption and power required, etc),

the constraints due to the targets, regulations and

segment’s characteristics were translated to

battery volume and vehicle weight requirements

[6]. The principle of linear programming was

used to apply linear inequation constraints. The

purpose is to check whether there is a set of

solutions that fulfil all the requirements and, if

not, which limitations should be modified. If

there is a solution, the feasible solutions region is

a convex polyhedron, which is defined by

intersection of half spaces defined by linear

inequations representing the constraints as shown

in Figure 2. In this case, there is a range of

vehicle mass and battery volume combinations

that could lead to feasible solutions.

Figure 2: Internal tool result with numbered

constraints.

2.1 Deterministic constraints

In this section, the inequations associated to each

constraint in Figure 2 are explained. It can be

observed that the half spaces defined for the

constraints are coloured for better understanding:

the restrictions regarding weight (due to market

practices, structural feasibility or regulations) are

coloured in dark blue, the constraints coming from

performance targets or power limitations in

regulations are coloured in red. Finally, the

restrictions caused by the range targets are

coloured in cyan.

The possibility to dynamically modify the inputs

and obtain a picture of the feasible solutions is

helpful to determining the most restrictive

requirements and for decision-making in vehicle

development. It must be noted that the restrictions

applied by the inputs are deterministic, and all of

them can be expressed in the form of inequations.

The equations describing the 13 constraints are

defined in the following paragraphs in terms of

vehicle mass (𝒎) and/or battery volume (𝑽). The

list of all the abbreviations used can be consulted

at the end of the paper.

1.1.1 Vehicle segment constraints The following equations describe the constraints

due to restrictions coming from the market typical

values for each segment, structural feasibility and

weight restrictions that appear in regulations.

Maximum battery volume: it is restricted by the

possible available space per segment and it must

fulfil the inequation (1).

𝑽 < 𝑉𝑚𝑎𝑥,𝑖 (1)

Minimum battery volume: it must fulfil a

consistent minimum defined per segment as

specified in ineq. (2).

𝑽 > 𝑉𝑚𝑖𝑛,𝑖 (2)

Maximum battery mass: for each vehicle mass

there is a limit battery mass that allows structural

feasibility. The minimum mass requirements

without battery (𝑚𝑊/𝑂) for each segment lead to a

battery volume limit that can be calculated with the

battery density as shown in ineq. (3).

𝑽 <(𝒎−𝑚𝑊/𝑂,𝑚𝑖𝑛,𝑖)

𝜌𝑏𝑎𝑡(3)

Maximum vehicle mass: it must be consistent

with the vehicle segment mass as in ineq. (4).

𝒎 < 𝑚𝑚𝑎𝑥,𝑖 (4) For light duty vehicles, if a payload is selected, the

vehicle mass is also limited by the maximum

Gross Vehicle Weight of 3500 kg as specified in

the regulations [7], and must also fulfil ineq. (5).


𝒎 < 𝑚𝑖𝑛(𝑚𝑚𝑎𝑥,𝑖 , (3500 −𝑚𝑙𝑜𝑎𝑑))(5)

Minimum vehicle mass: it is defined to allow

structural feasibility and leads to ineq. (6).

𝒎 > 𝑚𝑚𝑖𝑛,𝑖 (6)

Quadricycle mass limit: the vehicle mass

without battery for quadricycles is limited in

regulations [8] to 350 kg for light quadricycles

and 400 kg for heavy quadricycles and thus, the

battery volume must fulfil ineq. (7).

𝑽 >(𝒎−𝑚𝑊/𝑂,𝑚𝑎𝑥,𝑖)

𝜌𝑏𝑎𝑡(7)

1.1.2 Power constraints

The power constraints [4] are defined by the

battery power delivery limitations and the vehicle

power requirements to fulfil the targets. The

auxiliary variables defined in eq. (8-13) are

required for the calculations.

𝐹2 =1

2· 𝜌𝑎𝑖𝑟 · 𝐶𝑥 · 𝐴𝑓(8)

𝑘 = 1 + 𝐼𝑟𝑜𝑡 (9)

𝑚𝑒𝑥𝑡𝑟𝑎 = 𝑚𝑑𝑟𝑖𝑣𝑒𝑟 +𝑚𝑙𝑜𝑎𝑑 (10)

𝑚𝑇 = 𝒎+𝑚𝑒𝑥𝑡𝑟𝑎(11)

𝐹0 = 𝑓 · (𝑚𝑇(𝒎)) · 𝑔(12)

𝑚𝑒𝑞 = 𝒎 · 𝑘 +𝑚𝑒𝑥𝑡𝑟𝑎(13)

Maximum speed: the power required to reach

the maximum speed at a minimum reference

grade leads to a power demand depending on the

vehicle mass and payload as shown in eq. (14)

and thus, to a battery volume requirement as

specified in ineq. (15).

𝑃𝑣 = 𝐹2 · 𝑣max3 +𝑚𝑇(𝒎) · 𝑔 · 𝑣𝑚𝑎𝑥 ·

(𝑓 · cos(𝛼𝑟𝑒𝑓) + sin(𝛼𝑟𝑒𝑓))(14)

𝑽 > (𝑃𝑣(𝒎)

𝜂𝑝𝑒𝑎𝑘+𝑃𝐴𝑢𝑥)

𝜋𝑏𝑎𝑡(15)

Gradeability: the gradeability target at a

minimum reference speed mainly affects the

torque requirement which is calculated a

posteriori and does not have an effect at this

stage of the calculations. Even though, the power

required from the battery to achieve the

gradeability depends on the vehicle mass and is

calculated following eq. (16) and entails the

battery volume requirement in ineq. (17).

𝑃𝛼 = 𝐹2 · 𝑣𝑟𝑒𝑓3 +𝑚𝑇(𝒎) · 𝑔 · 𝑣𝑟𝑒𝑓 ·

(𝑓 · cos(𝛼) + sin(𝛼))(16)

𝑽 > (𝑃𝛼(𝒎)



Acceleration time 0-100 km/h: the acceleration

time to 100 km/h or to the speed that the vehicle

allows implies a power demand that depends on

the motor torque characteristic. In an electric

motor the base speed is known as the speed in

which the motor switches from a constant torque

operation to a constant power operation, and leads

to the relation specified in eq. (18) known as motor

characteristic [9].

𝑥 = 𝑣𝑚𝑎𝑥

𝑣𝑏(18)

For a better understanding of this magnitude,

Figure 3 depicts the results of a simulation in

which motors with different motor characteristic

provide the same 0-100 km/h acceleration time.

The relevance of 𝑥 is stated since motors with very

different torque can provide the same acceleration

performance.

Figure 3: Different possible motor characteristic curves

for same 0-100 km/h acceleration time.

For combustion vehicles, acceleration usually has

to be simulated in time steps, but this approach is

not suitable for an instantaneous calculation. For

EVs, as the maximum torque curve can be

simplified in constant torque region and a

decreasing torque region governed by a constant

power limitation, the calculation can be divided in

two steps: one ruled by a constant torque equation

and one ruled by a constant power equation. This

hypothesis allowed the calculation of the power

and torque requirement at vehicle level with a

simplified equation obtained through integration of

Newton’s second law under the assumption of

average drag power as specified in [9].

Consequently, the power needed to reach the

acceleration target is calculated through the eq.

(19-22).

𝐸 = (𝑣𝑏

′2+𝑣𝑎2)

2·𝑡𝑎(19)

𝐹 =2

5· 𝐹2 · 𝑣𝑎

3(20)

𝐺 = 2

3· 𝑣𝑎(21)

𝑃𝑎 = 𝐸 · 𝑚𝑒𝑞(𝒎)+ 𝐹 + 𝐺 · 𝐹0(𝒎)(22)


Where the values of 𝑣𝑎 and 𝑣𝑏′ used depend on

the vehicle characteristics as specified in eq. (23-

24). 𝑣𝑎 is the target speed to which the vehicle

must accelerate and it is 100 km/h unless the

vehicle is limited to a lower speed, and 𝑣′𝑏 is the

base speed unless it is higher than 𝑣𝑎.

𝑣𝑎 = min(𝑣𝑚𝑎𝑥, 100)(23)

𝑣𝑏𝑎𝑠𝑒′ = min(𝑣𝑎, 𝑣𝑏)(24)

Finally, the battery volume constraint due to

acceleration target can be calculated with the

battery characteristics through ineq. (25).

𝑽 > (𝑃𝑎(𝒎)



Quadricycle power limitation: the

quadricycles maximum continuous power is

defined in regulations [8] as 4 kW for light

quadricycles and 15 kW for heavy quadricycles.

This power limitation delimits a mass that, if

exceeded, the performance targets cannot be

reached. For each power requirement (speed,

gradeability and acceleration) this limit mass is

calculated in eq. (26-28) and leads to the

restriction established in ineq. (29).

𝑚𝑚𝑎𝑥,𝑣 =𝑃𝑚𝑎𝑥,𝑖−𝐹2·𝑣𝑚𝑎𝑥

3

𝑣𝑚𝑎𝑥·(𝑓·cos(𝛼𝑟𝑒𝑓)+sin(𝛼𝑟𝑒𝑓))·𝑔−

𝑚𝑒𝑥𝑡𝑟𝑎(26)

𝑚𝑚𝑎𝑥,𝛼 =𝑃𝑚𝑎𝑥,𝑖−𝐹2·𝑣𝑟𝑒𝑓

3

𝑣𝑟𝑒𝑓3 ·(𝑓·cos(𝛼)+sin(𝛼))·𝑔

−

𝑚𝑒𝑥𝑡𝑟𝑎(27)

𝑚𝑚𝑎𝑥,𝑎 =𝑃𝑚𝑎𝑥,𝑖−𝐹−(𝐸+𝐺·𝑓·𝑔)·𝑚𝑒𝑥𝑡𝑟𝑎

(𝐸·𝑘+𝐺·𝑓·𝑔)(28)

𝒎 < min(𝑚𝑚𝑎𝑥,𝑣, 𝑚𝑚𝑎𝑥,𝛼 ,𝑚𝑚𝑎𝑥,𝑎)(29)

1.1.3 Energy constraints

Range in cycle 1: The vehicle must be

dimensioned to provide a consumption [7, 10]

with which the target range in the specified

regulations or real driving cycle can be satisfied.

The connection among battery volume and

vehicle weight is very noticeable in terms of

energy constraints, because a bigger battery to

increase the range also increases the weight and

thus, consumption.

To properly simulate an accurate consumption, a

step-by-step calculation with a vehicle model is

required. However, this simulation cannot run in

the pre-design tool interface instantaneously and

requires a set of inputs that are not available at

the first development phase to provide a level of accuracy that is also not required.

Therefore, the tool was aimed at overcoming this

obstacle during pre-design. One of the most

remarkable contributions of the tool was the

parameterization of the consumption in a specific

cycle with a single formula that requires just

simple vehicle characteristic inputs. This formula

is based on a backward-looking vehicle model in

which the main consumption sources were

classified in inertia (also considering regeneration),

rolling resistance and aerodynamic resistance

forces. The main assumption that the user has to

make is the average working efficiency of the

powertrain (inverter + motor + transmission), to

which reference information is provided.

Assuming the average efficiency, each

consumption source was divided into two

coefficients: one that depended only on the cycle

selected and one that depended just on the vehicle

characteristics as specified in eq. (30), where 𝐵 is

the energy consumption and 𝑅 the regeneration

factor.

𝐵 =1

· (𝐹0(𝒎) · 𝐶1 + 𝐹2 · 𝐶2 +𝑚𝑒𝑞(𝒎) · 𝐶3) +

· 𝑅 · (𝐹0(𝒎) · 𝐶4 + 𝐹2 · 𝐶5 +𝑚𝑒𝑞(𝒎) · 𝐶6) +

𝑃𝐴𝑢𝑥 · 𝐶7 (30)

Thus, the cycle dependent coefficients are defined

in eq. (31-40) where 𝜒represents a step function.

Since the maximum vehicle speed can be decided

by the user, the cycle’s speed profiles are also

modified consequently [7] under the speed limit to

calculate the cycle coefficients.

𝐶1 = ∫𝑣(𝑡) · 𝜒1(𝑡) · 𝑑𝑡(31)

𝐶2 = ∫𝑣(𝑡)3 · 𝜒1(𝑡) · 𝑑𝑡 (32)

𝐶3 = ∫𝑣(𝑡) ·𝑑𝑣(𝑡)

𝑑𝑡· 𝜒1(𝑡) · 𝑑𝑡 (33)

where𝜒1(𝑡) = {1,

𝑑𝑣(𝑡)

𝑑𝑡≥ 0

0,𝑑𝑣(𝑡)

𝑑𝑡< 0

(34)

𝐶4 = ∫𝑣(𝑡) · 𝜒2(𝑡) · 𝑑𝑡 (35)

𝐶5 = ∫𝑣(𝑡)3 · 𝜒2(𝑡) · 𝑑𝑡 (36)

𝐶6 = ∫𝑣(𝑡) ·𝑑𝑣(𝑡)

𝑑𝑡· 𝜒2(𝑡) · 𝑑𝑡 (37)

where𝜒2(𝑡) = {0,

𝑑𝑣(𝑡)

𝑑𝑡≥ 0

1,𝑑𝑣(𝑡)

𝑑𝑡< 0

(38)

𝐶7 = ∫𝑑𝑡(39)

𝐷 = ∫𝑣(𝑡) · 𝑑𝑡 (40)

The simplification stated in eq. (30-39) allows

quick characterization of the relative importance of

the three main consumption sources in different

cycles. Through the simplified formula, the

consumption is modelled as a function of the


vehicle mass, and with the target range, the

battery volume requirement can be related to the

vehicle mass as specified in ineq. (41).

𝑽 >𝑅1

𝐵(𝒎)

𝐷·𝜀𝑏𝑎𝑡·𝑆𝑂𝐶𝑙𝑖𝑚

(41)

Range in cycle 2: this extra range requirement

allows enter a different target for another cycle.

Range in cycle 3: this extra range requirement

allows enter a different target for another cycle.

2.2 Non-deterministic constraints

The constraints specified in the previous

paragraphs can be expressed through physical

equations and the limit the possible feasible

solutions. However, how to select an optimal

solution within the viable region is not ruled by

deterministic equations. The cost objective

function commonly used in linear programming

is not that straight forward for this situation, as it

depends on discrete and non-linear

characteristics such as the powertrain

architecture, the components selected, the usage

or not of high-technology lightweight materials,

the development costs and the vehicle

manufacturing volume and procedure. The

weighting of all these factors in the final cost to

make a final decision require a proper market

study, interaction among departments, internal

company knowledge and direct contact with

TIERs for components quotations.

For better assisting the decision making inside

the viable region, the pre-design tool allows

introducing two cost functions, one that is

dependent on the battery capacity and one that is

dependent of the mass of the vehicle without the

battery, and represents the cost of the rest of the

material. The total cost function is then defined

by equation 42. The specific coefficients that rule

each cost function have to be decided based on

the know-how of the user, the production volume

and the market. Figure 4 shows an example of

application of a cost function.

𝑪 = 𝑓(𝑽 · 𝜀𝑏𝑎𝑡) + 𝑓(𝒎− 𝑽 · 𝜌𝑏𝑎𝑡)(42)

2.3 Sensitivity analysis

In some cases and depending the background of

the user, it is difficult to know in advance which

modifications will improve consumption and,

furthermore, which will affect consumption

more. For this purpose, once the user selects a

point inside the feasible region, a sensitivity analysis appears to show the magnitude in which

different parameters affect consumption for the

first selected cycle as shown in Figure 5. The

sensitivity analysis is a good summary to compare

the influence of very different parameters on

consumption, and to evaluate the most cost-

effective measure to reduce it.

2.4 Pre-design Tool Application Example

In this section, the pre-design tool was used to

estimate the main characteristics of a C segment

passenger car. The main characteristics of the

vehicle are presented in Table 1.

Table 1: Main characteristics of the vehicle

Aerodynamic drag coefficient (Cx) 0.28

Frontal area (m2) 2.19

Tire drag coefficient 0.007

Auxiliaries consumption (W) 450

Battery vol. energy density (Wh/l) 190

Powertrain average efficiency 75%

Amount of recovered energy 80%

The main targets of the vehicle are the ones

presented in Table 2.

Table 2: Main targets of the vehicle

WLTP Range (km) 200

Max Speed (km/h) 140

0-100 km/h (s) 10

The function of the cost of the battery and the cost

of the mass of the vehicle without the battery were

determined based on two studies [11, 12]. As per

discussed in these studies, the battery was

considered to have a linear cost increase with the

capacity and the weight of the vehicle was

considered to have a non-linear trend. The

lightweight design implies less amount of

materials but also more development costs and

more expensive materials, namely aluminium or

carbon fibre. However, an increase of the mass

over due to a low investment in design also causes

a cost increase due to the need to use more

kilograms of steel. Therefore, the function of the

mass-dependent cost has a local minima. Figure 4

shows the feasible region obtained for a C-segment

vehicle with the characteristics of Table 2 and the

targets of Table 2. It can be observed that the

battery size for this case is determined by the

target range (cyan area) and not by the power

required to fulfil the targets (red area), and that the

size requirement increases with the increase of

vehicle weight, because a higher vehicle weight

implies more consumption and, therefore, a bigger

battery for achieving the same range.


Figure 4 Feasible for a C-segment vehicle and cost

function

Figure 4 also shows the cost function for

batteries from 100 to 200 l as a function of the

battery size and the vehicle mass. It can be

observed that the optimal is to select the

minimum battery that allows to achieve the range

(line limiting with the cyan area) and that the

cost function has a local minimum around 1400-

1500 kg. A lower mass would include the cost of

the materials, and a higher mass would increase

the size of the battery and the amount of material

required.

With this information, the pre-design vehicle

selected was a 1500 kg vehicle, which requires

approximately a 140 l battery to fulfil the range

and 84 kW of power. Figure 5 shows the

sensitivity analysis performed to this vehicle.

Figure 4: Sensitivity analysis in WLTP

It is remarkable, that for EV, an increase of 5%

on the average powertrain efficiency has more

than 5% positive impact on the total consumption.

This phenomenon occurs because when the

powertrain is more efficient, not only the vehicle

consumes less in traction mode (5% less) but also

regenerates more when braking (5% more),

leading to an impact higher than the relative

improvement.

3 Vehicle configurations With the pre-design configuration hint, it was

possible to implement the idea in three different

powertrain architectures and to simulate more

accurate consumption and component

requirements for each case. The simulations were

performed with vemSim, a vehicle longitudinal

dynamics simulation software developed at

IDIADA which is implemented in

Matlab/Simulink environment. The common

characteristics for the simulated configurations

were the ones shown in Table 3.

Table 3: Common characteristics for the simulation

Base vehicle weight 1250 kg

Motor power 85 kW

Motor torque curve characteristic 3

The three powertrain architectures considered

were:

- Longitudinal with one motor, reducer and

differential (Figure 5).

- Two motors connected directly to the front

wheels with two reducers (Figure 7)

- Four motors to wheel with four reducers

with the possibility to adjust the torque split

between the front and rear axle (Figure 8)

The main unknown system is the battery, which

will be different for each case to fulfil the same

range target because of the different weight and

average efficiency of each vehicle configuration.

The internal tool was useful to provide an accurate

hint of the size, but the accurate simulation is used

to check the viability of the predesign for the

specific powertrain architecture.

The motor torque curve characteristic was taken of

3 for all the motors and the motors were

considered of the same power with an accumulated

power of 85 kW for the cases with more than one

motor. The simulations were performed

considering a characteristic electric motor

efficiency map that was re-scaled to fit for the

different-sized motors of the three powertrain

architecture cases.

The weight of the powertrain components is also

different for each configuration, depending on the

amount and size of the motors, reducers and


differential. The mass values were taken from an

analysis of the available components for electric

vehicles currently in the market.

3.1 First configuration

The first case to study was the conventional

architecture for a vehicle, a car with a

longitudinal motor that provides traction to front

wheels but in this case, the IC engine was

changed for an electric motor. To transmit the

power to the wheels, the vehicle used a reducer

and a differential. The configuration simulated is

shown in Figure 5.

Figure 5: Configuration 1 example

Figure 6 shows the efficiency points in which the

motor works for this configuration. It can be

observed that the cycle does not require to use

the maximum power or torque that the vehicle

can provide. Therefore, the torque required is

low compare with the torque available by the

motor and it works very often in low efficiency

points.

Figure 6: Operative points distribution for one

longitudinal motor

3.2 Second configuration

The second case to study was a different

architecture that may be implanted in electric

vehicle thanks to the new possibilities of the

electric traction. The concept of one motor for

traction power can be substituted by the concept of

one motor for every traction wheel. For this case, a

front wheel drive (FWD), car was simulated with

two motor in the front of the car. A reducer was

used to adjust the required torque to the wheels.

Figure 7 shows the simulated vehicle model.


Both motors have half the power of the motor for

the first configuration, but the torque split among

them was 50% for each motor, the operative points

distribution obtained was very similar to the one

depicted in Figure 6, the motors worked very often

at low efficiency points.

3.3 Third configuration

The third case was the same concept of the second

case but with four tractive wheels, simulating an

all-wheel drive (AWD) vehicle. Figure 8 shows the

architecture simulated. This AWD configuration

allows implementing a more efficient operating

strategy of the motors to work in the most efficient

operative points without compromising the

performance. For that, a controller was designed to

calculate in which case is more efficient to work

with two motors or four motors depending on the

motors efficiency map.



The results that would be obtained without

operating strategy by just dividing the target

torque for the four motors would be very similar

to the ones obtained for the first and second

configuration, the motor would mainly work at

low efficiency points.

However, if an operating strategy is implemented

to calculate when it is better to operate only with

two motors and when to split the torque between

front and rear motors, high average efficiency

improvements can be achieved as shown in

Figure 9.

Figure 9: Operative points distribution for AWD

configuration for the front motors (blue) and rear

motors (yellow)

3.4 Simulation results

With the powertrain defined, was possible to

make an approach of the battery capacity

required for each specific case and define the

final performance of the vehicle. The objective

was to design a vehicle which was able to

achieve the range of 200 km following the

WLTP cycle and compare the results obtained

for different architectures with the results obtained

with the pre-design tool before the architecture

was selected.

Figure 10 shows the final weight for each

configuration. The use of four motors implies a

significant increase of the powertrain weight due

to the two extra motors and transmissions. The two

motors configuration implies only a small increase

in weight respect to the longitudinal configuration

because the differential is removed. A difference in

weight can also be observed for the AWD

configuration depending on the operating strategy

of the motors, because the battery size can be

reduced is such strategy is implemented. It can be

observed that the pre-design tool hint was close for

the longitudinal motor configuration but that is

was not that accurate for the AWD configuration.

The predesign tool is aimed at giving a fist hint on

the vehicle characteristics, but many different

implementations can appear when further

developing the configuration for different

powertrain architectures.

Figure 10: Total weight for each configuration

Figure 11 shows that a configuration with four

motors allows including an operating strategy to

optimize the powertrain average efficiency, which

in this case increases from 79% to 82%. The

average efficiency of the powertrain is also

improve with the two-motor configuration,

because the differential and the pinion crown used

to change the direction of the torque delivery from

longitudinal to transversal is no longer required.


Figure 7: Efficiency of the powertrain for each

configuration

Figure 13 shows the kilometric consumption,

which is an effect of the vehicle weight, the

powertrain average efficiency among other

sources. Figure 13 shows that, for this case, even

though the AWD configuration implies a higher

vehicle mass, the kilometric consumption and

thus, the battery size, can be reduced if an

efficiency optimization strategy is implemented.

Figure 8: Consumption for each configuration.

Finally, Figure 15 shows the battery capacity

required for each configuration. The configuration

with four motors to wheel and an optimized

operating strategy requires less battery capacity

than the other configurations, what could

compensate the extra-cost required by the more

complex powertrain.

Figure 9: Battery capacity for each configuration

Conclusion In this paper, a pre-design tool to assist the electric

vehicle development process was presented. The

advantage of the tool is its capability to represent

the different development constraints that are

affected by regulations and decision-making of

different departments in one figure that can be

instantaneously refreshed in an interactive

interface.

The tool makes use of the equations that are used

to perform longitudinal dynamic simulations.

However, these equations were studied to obtain

simple approximations to avoid performing step-

by-step simulations and produce instantaneous

results. The main advantage of this tool is that it

interconnects calculations that typically involve

different departments in a loop to reduce the

iterations in the pre-design phase. The tool was

intended to provide the user a first insight from a

design point of view and sufficient information on

the vehicle requirements to fulfil the performance

targets regardless of the powertrain architecture.

The main equations and assumptions introduced in

the tool to obtain these targets are explained in this

paper. Finally, the usage of the tool is illustrated

through one example that is then further developed

in three different powertrain architectures and

simulated in the software vemSim to assess the

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0,74

0,76

0,78

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level of accuracy of the estimation that the pre-

design tool provides.

Acknowledgments

This study was developed within the framework

of an internal project in Applus IDIADA. The

author would like to express his gratitude to the

vemSim software team

References 1. ELVA Project Webpage, http://www.elva-

project.eu/

2. Dávila, A., Romero, E., Roche, M., Mammetti,

M. et al., "The ELVA Project's EV Design

Support Tool," SAE Technical Paper 2014-01-

1967, 2014, doi:10.4271/2014-01-1967.

3. Dávila, A., Romero, E., Gutiérrez, J., Mammetti,

et al., “ELVA Project – Innovative Approaches

for Electric Vehicle Design,” FISITA 2014 World

Automotive Congress, Maastricht, The

Netherlands, F2014-MVC-032.

4. BOSCH; Automotive Handbook, 2nd edition,

1986

5. UN/ECE Regulation No. 1222/2009, “Labelling

of tyres with respect to fuel efficiency and other

essential parameters”.

6. Sánchez Ruelas, J.G., Stechert, C., Vietor, T.,

Schindler, T., “Requirements Management and

Uncertainty Analysis for Future Vehicle

Architectures,” FISITA 2012 World Automotive

Congress, Beijing, China, F2012-E02-006.

7. Regulation No 83 of the Economic Commission

for Europe of the United Nations (UN/ECE),

“Uniform provisions concerning the approval of

vehicles with regard to the emission of pollutants

according to engine fuel requirements”.

8. Directive 2002/24/EC relating to the type

approval of two or three wheel motor vehicles

9. Ehsani, M., Gao, Y., Gay, S. E., Texas A&M

University, Ali Emadi, Illinois Institute of

Technology, “Modern Electric, Hybrid Electric,

and Fuel Cell Vehicles, Fundamentals, Theory,

and Design,” 2005 by CRC Press LLC. p.108.

10. ISO 2416:1992; Passenger cars – Mass

distribution; 1992

11. Redelbach, M., Klötzke, M., Friedrich, H. E.,

“Impact of lightweight design on energy

consumption and cost effectiveness of alternative

powertrain concepts”, EEVC, Brussels 2012.

12. Mckinsey & Company, “Lightweight, heavy

impact”.

Authors

Ms Marina Roche holds Mechanical

and Industrial Engineering degrees

both with honours. In 2012 she joined

IDIADA for research in vehicle

modelling, powertrain NVH, Wavelet

post-processing and software

development. Previously researched

on modelling the combustion of

gaseous fuels in IC engine in the

University of Zaragoza.

Mr Dídac Sabrià holds an Industrial

Engineering degree and a diploma in

Automotive Engineering from the

Universitat Politècnica de Catalunya

(UPC). He joined IDIADA in 2014 as

powertrain integration engineer for

research and development in vehicle

modelling and software development.

He has also researched on two-

wheeler fuel cell vehicles in UPC.

Mr Mammetti holds PhD in

Mechanics and an Aeronautical

degree. Since 2009 he worked in

IDIADA as powertrain integration

Product Manager. Recent activities

include powertrain conversions for

consumption reduction or to full

electric. In 2006 he was responsible of

powertrain integration in Maserati

and, before he worked in Ferrari as

responsible for engine performance

and calibration and innovation.

Abbreviations 𝛼 Gradeability target (º)

𝛼𝑟𝑒𝑓 Reference grade for speed target (º)

𝜒1(𝑡) True when positive or null acceleration in a

cycle

𝜒2(𝑡) True when negative acceleration in a cycle

𝜀𝑏𝑎𝑡 Battery volumetric energy density (kWh/l)

𝜂𝑝𝑒𝑎𝑘 Peak powertrain efficiency (%)

𝜋𝑏𝑎𝑡 Battery volumetric power density (kW/l)

𝜌𝑎𝑖𝑟 Air density (kg/m3)

𝜌𝑏𝑎𝑡 Battery density (kg/l)

𝐴𝑓 Aerodynamic frontal area (m2)

𝐵 Consumption in a specific cycle (kWh)

𝐶1−7 Coefficients to compute the contribution of

different consumption sources in a cycle

𝐶𝑥 Aerodynamic drag coefficient

𝐷 Distance overtook during a cycle (m)

𝐸 Coefficient to compute the inertia

contribution to the acceleration power

requirements (kW/kg)

𝐹 Coefficient to compute the drag contribution

to the acceleration power requirements (kW)


𝐹0 Coast Down coefficient to compute the

constant resistance forces (N)

𝐹2 Coast Down coefficient to compute the

square-speed dependent resistance forces

(N/(m2/s

2))

𝐺 Coefficient to compute the constant

resistance forces contribution to

acceleration power (m/s)

𝑓 Tire rolling resistance (kg/t)

𝑔 Gravity constant (m/s2)

𝐼𝑟𝑜𝑡 Relation of rotating parts equivalent inertia

to vehicle mass (%)

𝑖 Segment selected

𝑘 Vehicle inertia coefficient

𝒎 Vehicle mass (kg)

𝑚𝑑𝑟𝑖𝑣𝑒𝑟 Driver mass (kg)

𝑚𝑒𝑞 Equivalent total mass considering inertia

(kg)

𝑚𝑒𝑥𝑡𝑟𝑎 Extra mass applied (driver + load) (kg)

𝑚𝑙𝑜𝑎𝑑 Playload mass (kg)

𝑚𝑚𝑎𝑥,𝑖 Upper vehicle mass limit per segment (kg)

𝑚𝑚𝑖𝑛,𝑖 Lower vehicle mass limit per segment (kg)

𝑚𝑚𝑎𝑥,𝑎 Quadricycles: acceleration target mass

limit (kg)

𝑚𝑚𝑎𝑥,𝛼 Quadricycles: gradeability target mass

limit (kg)

𝑚𝑚𝑎𝑥,𝑣 Quadricycles: speed target mass limit (kg)

𝑚𝑇 Total mass (vehicle + driver + load) (kg)

𝑚𝑊/𝑂,𝑚𝑖𝑛,𝑖 Lower limit to vehicle mass without

battery (kg)

𝑚𝑊/𝑂,𝑚𝑖𝑛,𝑖 Quadricycles: upper limit to vehicle mass

without battery (kg)

𝑃𝐴𝑢𝑥 Auxiliaries power consumption (kW)

𝑃𝑎 Acceleration power requirement (kW)

𝑃𝛼 Gradeability power requirement (kW)

𝑃𝑚𝑎𝑥,𝑖 Quadricycles: upper power limit

𝑃𝑣 Speed power requirement (kW)

𝑅1−3 Vehicle range targets for three cycles (m)

𝑅 Relation of recovered to recoverable

energy in deceleration (%)

𝑆𝑂𝐶𝑙𝑖𝑚 Battery State Of Charge lower limit (%)

𝑡 Time (s)

𝑡𝑎 Acceleration time target (s)

𝑽 Battery volume (l)

𝑉𝑚𝑎𝑥,𝑖 Upper limit to battery volume (l)

𝑉𝑚𝑖𝑛,𝑖 Lower limit to battery volume (l)

𝑣(𝑡) Speed profile in a cycle (m/s)

𝑣𝑎 Speed to achieve in acceleration target

(m/s)

𝑣𝑏 Transition speed from constant torque to

constant power in an electric motor (m/s)

𝑣𝑏′ Corrected base speed for acceleration (m/s)

𝑣𝑚𝑎𝑥 Target vehicle speed (m/s)

𝑣𝑟𝑒𝑓 Reference speed for gradeability target

(m/s)

𝑥 Motor torque curve characteristic

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