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LMS Imagine.Lab AMESim

Solution for Electric vehicle From the design to the integration

Table of Content

Industry Context

LMS Solutions for Electric vehicle

Why LMS Imagine.Lab AMESim?

Conclusion

2 copyright LMS International - 2010

Industry context

Attributes balancing

Performance Fuel Eco (CO2) /

Range Emissions Drivability

Thermal

&

Vehicle Energy

Management

Transmission

&

Vehicle Energy

Management

Engine Integration

&

Vehicle Energy

Management

3 copyright LMS International – 2010

Comfort Safety

Electrics

&

Vehicle Energy

Management

Table of Content

Industry Context



Conclusion


LMS Imagine.Lab AMESim Solution for Electric Vehicle

Aux.

Power S.

Auxilliary

loads

Aux.

Control

auxiliary subsystem

Vehicle

controller Power

converter

Electric

motor

Mechanical

transmission

Brake

accelerator

Electric Propulsion Subsystem

Mechanical link

Electric link

Control link

HV

Battery

Battery

charging

Energy

management

unit

Energy Source subsystem

Solution Overview Set of generic libraries


The scalability of AMESim

Various complexity scale for various development objective

Modeling

focus

Development

objective

Pre-sizing,

control strategy

development

System sizing,

control

Component

optimization

Quasi-static:

map models

Low frequency:

prevailing behaviors

High frequency:

detailed dynamics

Physical Modeling: From Functional Specification to Calibration.



LV

Battery

Auxilliary

loads

Aux.

Control

auxiliary subsystem

Vehicle

controller Power

converter

Electric

motor

Mechanical

transmission

Brake

accelerator


Mechanical link

Electric link

Control link

HV

Battery

Battery

charging

Energy

management

unit


Electric propulsion subsystem The scalability modeling approach of AMESim

Pre-sizing applications with quasi-static

components

Energy management, strategy development and validation

Model focus :

No or low dynamics

Simple sizing parameters

Map models

Benefits:

Very fast simulations

Accurate energy assessments

0 Hz

1 Hz


Performance/driving range

…with 14V network

…with cooling

…first confort assesments

Electric Vehicle: Tabulated motors and batteries

Motor (=motor + converter + control)

Max torque =f( U, w)

Losses = f( T, w)

Battery

E = f( SOC,T )

R = f( SOC,T)

Benefits:

Simulation of the whole system (vehicle)

Early design phase (sizing of components)

Integration phase (check of control laws)


Electric car model for performance/driving range

Battery SOC

Motor losses

Motor torque

Vehicle speed

Focus on performance / range estimation

Mechanical and electric losses.

Regenarative braking

Energy management


Electric car model for performance/driving range incl. 14V Boardnet

14V Battery SOC

14V Power consumption

Focus on performance/consumptions



Energy management

Detailed 14V Boardnet


Electric car model for performance/driving range incl. Cooling of

Electric components

Coolant temperature

Focus on performance/consumptions


Regenerative braking

Energy management

Detailed 14V Boardnet

Temperature dependency


Electric car model for performance/driving range/comfort

Transmission + car body

Rotary velocity (pitch) of car body

Performance/consumptions



Energy management

Focus on driving comfort

Dynamic up to 20 Hz (transmission +

car body)



Sizing and control applications with slow transients components

Component optimization, environment interaction

Model focus :

Low dynamics

Simple physical parameters

Thermal coupling

Benefits:

Fast simulations

Transient influence for control laws

and energy consumption

Thermal sizing

0 Hz

1 Hz

100 Hz


Motor control design

… with detailled cooling

… and influence on comfort

Electric motor: Permanent Magnet synchronous Machine

3 phase synchronous machine with permanent

magnet (no damper)

Linear models (no saturation effect)

Temperature dependancy

Estimation of dynamic behavior of electric machines


Level 2 : converters average models

Average model

Switching dynamic (ESC)

3 phase

inverter

phase

voltage

3 phase

inverter

phase

current Purpose :

Inverter losses computation

Energy management, optimization of machine control, cooling sizing…

Time simulated ~ 0.1s to 1000s


Losses hypotheses

Conduction losses: Due to forward drop voltage of diodes and transistors

Switching losses: Due to the fact diodes and transistors do not switch state in zero time.

Parameterization Linear characteristics can be extracted from semiconductors data sheet

Advanced characteristics can be extracted from measurements

Linear Advanced

Advanced Linear

Diode and transistor drop voltage vs current

Switching energy lost vs current

IGBT and

diode

data sheet

Measurement or detailed model

Linear

parameters

Advanced

functions

or data

files

Simulation

Losses

Average

voltage

Control

strategies


p-salient 3 phase synchronous motor torque control

Input :

Torque command

Parameters:

Motor’s parameters

Maximum RMS current

PWM strategy

Time constants to regulate the currents

Outputs:

PWM signals command

for inverter transistors

(duty cycles)

Sensors:

DC voltage

Phase currents

Rotary velocity

Objective :

Considering the inverter voltage capability and the maximum RMS currents (thermal motor limitation),

Match the torque command as much as possible

Find the minimum currents operating point (=> maximum efficiency)


p-salient 3 phase synchronous motor torque control

Torque control unit Compute the command current

in park’s frame (id iq) of the

minimum current functioning

point that match the torque

command. It’s the ideal control

considering the motor model

assumptions.

Current control loop

Compute the voltages in park’s frame

to regulate the currents. The

disturbance due to rotary acceleration

are removed so the control loop can be

seen as a perfect first order lag.

Park’s transformation The park’s frame is a coordinate

system attached to the rotor.

3 phase sinusoidal currents (id iq)

and voltages (vd vq) are constant

in the parks frame. Models take the

electric pulsation as input.

PWM signals generation Compute the duty cycle of the

transistors command signal.

Strategies can improve the

maximum modulation depth and

reduce switching losses.

Maximum RMS voltage The maximum voltage that can be

supplied by the inverter depend on the

battery voltage and on the PWM strategy.

The time constant for currents regulation

can lead to exceed the maximum voltage

during transients. So a safety margin is

added.


Electric car with slow transient models

Focus on machine control for

performances and losses:

Influence of PI time constant

Electric car with slow transient models for comfort

Focus on motor control for comfort:

Influence of filtering torque time constant

Tau = 0.01 Tau = 0.5 Tau = 0.18

Electric car with slow transient models for cooling sizing

Focus on motor and converter

cooling


High frequency applications with fast transients components

Power quality, fast transient

Model focus :

High dynamics

Physical parameters

Benefits:

Accurate transient analysis

Control validation

0 Hz

1 Hz

10 Hz

10 kHz


… switching effects

Level 2 : Model of converter with switching


Assemblies of semiconductors to represent conversion functionalities

(AC/DC, DC/DC,DC/AC, AC/AC)

Ensures ease-of-use and computation efficiency

Semiconductors are modeled as resistors

with low resistance (Ron) when conducting and

high resistance (Roff) when blocking

Electric car with with switching consideration

Focus on control:

Validation of inverter’s close control strategies

Sizing of filtering capacitor

Optimization of noise on the torque or on the battery’s

current


LV

Battery

Auxilliary

loads

Aux.

Control

auxiliary subsystem

Vehicle

controller Power

converter

Electric

motor

Mecha.

transmission

Brake

accelerator


Mechanical link

Electric link

Control link

HV

Battery

Battery

charging

Energy

management

unit


Energy source subsystem The scalability modeling approach of AMESim

Estimation of voltage level and state of charge of the battery in function of the inlet I & T

low

high

Model features:

The equivalent electrical circuit is the serial

association of:

variable voltage source

variable resistance

They are function of the State Of Charge (SOC)

and temperature.

Battery cooling: case organization

Individual modeling of the battery cells

Variable cooling air flow

Electrical series connection

Bank with 3 rows of 6 cells:

Use of simple EMD model for battery cell

Use of THPN library for air flow thermal exchange

Cool air flow Hot air flow

Battery case

Battery cooling: model and results

AMESim model:

Transient and static analysis on the case, air and cells temperature

Battery voltage,…

Air flow step

3D representation of the battery

case:

Case temperature

Air temperature

Cells temperature

Battery cooling: integration in full vehicle model

Energy source subsystem The scalability modeling approach of AMESim

low

high

Accurate prediction of the voltage response of the battery in function of the inlet I & T

Model features:

All parameters can depend on State Of Charge, temperature,

and current (formula or table)

Diffusion phenomenon approximated by a state space

representation. The number of state variables is adapted to meet

the required accuracy.

Possibility to model a pack or an element.

Parameterization in the temporal or frequential domain.

SOC calculation

Battery advanced model: Introduction

Objective of the numerical battery model:

In function of the inlet I & T, predict the voltage response of the battery

Definitions:

Open Circuit Voltage (= OCV) : voltage at the thermodynamic equilibrium (I = 0)

Overpotential (= η) : voltage ≠ between OCV and voltage experimentally observed

Origins of overpotentials:

• Charge transfer: ηct

• Mass transport: ηdiff

• Ohmic phenomena: ηohm

Determination of the voltage:


Battery advanced model: equivalent electric circuit


Red frame: RC circuit modeling the charge transfer

Cyan frame: voltage source modeling the O.C.V

Green frame: association of “n” RC circuits modeling the diffusion

Blue frame: 2 resistances in parallel with diodes modeling the charge

and the discharge resistances

diffusion overvoltage

for all electrodes

+ -

double layer overvoltage

compared to equilibrium

except diffusion

ohmic drop

and migration drop

equilibrium voltage

(algebraic sum of Nernst voltages)

Electrical parameters dependency

Dependence on SOC, T and I Dependence on SOC, T and I

Dependence on SOC and T Dependence on SOC, T and I


diffusion overvoltage

for all electrodes

+ -

double layer overvoltage

compared to equilibrium

except diffusion

ohmic drop

and migration drop

equilibrium voltage

(algebraic sum of Nernst voltages)

Rct

Cct

OCV dtc

dssr Rc

Rd

Introduction of identification assistant


Identification of battery model parameters:

The identification of these dependencies is difficult:

No norm or standard procedure to identify the variation

Dependence on current is not or badly known

It is suitable to obtain temporal measures of U, I, T:

With a large range of SOC, current pulsations and temperature

An identification of different parameters can be done ”time zone” after “time zone”

The goal of this identification tool:

To obtain 2D or 3D tables stored in text files for each parameter of the model

To automate the process of the identification

Identification assistant tool


Battery Assistant Tool Identification from experimental data the parameters necessary in the

battery model

Communication between Simulation and Identification


Launch by a click

Generation of data files for the

different parameters in function

of SOC,T and I

experimental data

Data reading

Visualization

Identification

Parameter file

Identification tool

Identification assistant tool: data inputs

Voltage measurements

Current measurements

• Charge and discharge

• Several magnitude of:

• Current pulsations

• S.O.C

Temperature measurements


The calibration tool identifies the parameters of the

battery model by fitting the voltage measurements in

function current and temperature measurements

Process of identification: graphical interface


After identification

of OCV and RΩ

Diffusion overpotential

missing

After identification of OCV, RΩ and Rdiff

Experimental voltage measures to fit

After identification of OCV


LV

Battery

Auxilliary

loads

Aux.

Control

auxiliary subsystem

Vehicle

controller Power

converter

Electric

motor

Mecha.

transmission

Brake

accelerator


Mechanical link

Electric link

Control link

HV

Battery

Battery

charging

Energy

management

unit


The LMS Imagine.Lab Automotive Electrics

Features

Alternator, battery, loads, wire and fuse

components

Quasi static and transient models

Electrical, mechanical and thermal modeling

Real time compliant


Represent complete automotive boardnet and

manage electrical energy

Evaluate the influence of every component for

thorough validation

Validate control laws and optimize your network

Benefits

Table of Content

Industry Context




Conclusion


RENAULT makes the driveability of conventional and electric vehicle

objective with LMS Imagine.Lab AMESim

Challenges

Simulate several vehicle maneuvers (take-off, tip-in or

engine start) at the first stages of a vehicle project

Consider conventional, hybrid electric or full electric

powertrains

Be able to perform sensitivity analysis on a large set of

parameters

Solution

LMS Imagine.Lab Transmission Comfort solution

LMS Imagine.Lab Electrical Systems solution

Benefits

Assess early in the design process the impact of the

stiffness of mounting blocs or driveline on the driveability

Balance driveability and fuel economy through control

strategies parameters

“ LMS Imagine.Lab AMESim allows to answer our crucial need of assessing drivability quality

from the very first steps of a vehicle project.”

Benjamin ELLER – RENAULT – International LMS Engineering Simulation Conference , Munich, 25th February 2010


Automotive boardnet designs and analysis at Renault

Challenges

Early sizing and optimization of the electrical system (alternator – battery – wires and fuses, loads, …)

Optimization of the 14V electrical system

Validation & optimization of electrical energy management laws

Solutions

Trainings and technical expertise

Modeling and simulation

Benefits

Multiphysics approach (electrical, mechanical and thermal) and electronic simulation

Management and sharing of models along the design process

Different levels of models adapted to every need (from steady-state to slow transients)

“LMS Imagine.Lab allows us to build a stable and performing multiphysics model for the 14V

electrical system. Simulation makes it possible to optimize advanced energy management laws in

early phases during the V-cycle.”

Emmanuel LAURAIN – Renault – Simulation expert designer

Photographe : Anthony Bernier


Table of Content

Industry Context




Conclusion


Conclusion


A FLEXIBLE TOOL can address every kind of architecture, transmission,…

A MULTILEVEL APPROACH From tabulated models to detailed physical equations

ABILTY TO ADDRESS MORE ISSUES Impact of thermal management, Driving Comfort, Electrical

architecture and auxiliaries electrification…

A MULTIPHYSIC PLATFORM Ability to simulate the whole system and interaction between

mechanical, electrical, thermal, hydraulic phenomenon & control

READY TO USE E/HE VEHICLES MODELS You don’t have to start from scratch

Thank you

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