Lms imagine lab mbse_for_vehicle_electrification__30122015

Unrestricted © Siemens AG 2015 Realize innovation.

How to address vehicle electrification

engineering challenges? Optimizing development process with model-based systems engineering

Unrestricted © Siemens AG 2015

Page 2 Siemens PLM Software

• Introduction to mechatronic system simulation

• Global trends and challenges of vehicle electrification

• From electrification of standard vehicle architecture to

hydrid and electric vehicles

• Full automotive boardnet with regenerative braking

• Electrical network

• Energy recovery systems

• Hybrid bus with a detailed electric powertrain

• The battery and key systems for hybridization

• Examples

• Wrap-up

Table of content



System simulation & LMS Imagine.Lab Amesim

• Simulation of the complete system

• Description of physical phenomena based on few

“macroscopic” parameters

• Multi-physics / multi-level approach

• The simulation model is an assembly of components

• Components are described with analytical or tabulated

models

• We are looking for static/dynamic responses (time &

frequency domains)

LMS Imagine.Lab Amesim

model

Mechanical

Power

Pneumatic Power

Thermal Power

Internal Combustion Engine

Mechanical

Power

Electric Power

Thermal Power

Alternator



Capability for plant modeling

Vehicle model – Synthesis & analysis Scenarios Performance

Thermal Transmission Electrics Engine Driver Vehicle Dyn.

Sub-systems models & tools












• Examples

• Wrap-up

Table of content



Global trends and challenges of vehicle electrification

Power consumption of vehicle electrical systems has

increased dramatically over the past years

• On-electric power consumption to reach several kW for

comfort and security equipment (saturated 12V network)

Electrical network complexity increased

• Variety and complexity increasing, especially with electrified

powertrain architectures (hybrid and full electric vehicles)

Growing complexity of control & electronic systems

• Increasingly complex energy management laws needed for

lowering fuel consumption



Vehicle electrification using LMS Imagine.Lab Amesim

• Fuel economy

• Electric boardnet

• Electrification of actuators

• Energy recovery systems design

Conventional architectures Hybrid architectures Hybrid & electric vehicles

• Components & subsystems sizing

• Drivability

• Powertrain controls validation

• Battery thermal management & ageing













• Examples

• Wrap-up

Table of content



Full automotive boardnet with regenerative breaking

Demo content

• A mission profile (driving cycle), a driver + ECU, an engine & a gearbox & a vehicle

• An alternator, a battery & an electric loads

The driver rides the vehicle following a NEDC cycle profile.

In this demo example, two control strategies are proposed to regulate the network voltage:

• In the first strategy, the network reference voltage is set to a constant value.

• In the second strategy, the network reference voltage is changed with time to optimize the energy

consumption. The electrical network is used to perform regenerative braking and save energy.

Example #1

Objective

• Assess the vehicle fuel consumption on various electric loads and voltage strategies

• Assess the battery state-of-charge



Full automotive boardnet with regenerative breaking

Example #1












• Examples

• Wrap-up

Table of content



Electrical network

Demo content

• Modeling of electrical components and systems

• Battery

• Wires

• Fuses

• Loads with their thermal ports

Example #2

Objective

• Design the electrical network

• Select the fuses

• Run default analysis and analyze the impact of the short circuit on the system



Electrical network

Example #2





• Electrification of standard vehicle architecture






• Examples

• Wrap-up

Table of content



Energy recovery systems

Demo content

• A mission profile (driving cycle), a driver + ECU, an engine & a gearbox & a vehicle

• An starter/alternator, a battery & an electric load

• A stop/start & regenerative breaking controller

Example #3

Objective

• Assess the vehicle fuel consumption on various driving cycle

• Validate pre-sizing of the electrical machine, the battery

• Define the control strategy

• Compare different strategies (start&stop / regenerative breaking)




Starter/alternator,

electric load & battery

Stop&Start and

electric machine controller

Example #3




• To model the starter/alternator, we used a electric machine

(tabulated).

• Depending on the signal input, the electric machine can be a

generator (input < 0) or a motor (input > 0).

• The input signal is actually the torque command (if signal = 5, it

means that the electric machine will generate a 5 Nm torque). If

signal = -3, the electric machine will act as a generator consuming

3 Nm.

• The electric machine can be resized from a reference machine

using an user interface.

Input Signal

Example #3




For the engine STOP/START controller, we use the StateChart tool.

3 states have been defined:

• Engine ON : the engine is operating normally

• Engine startup : the engine is restarting

• Engine OFF : the engine is off

The transition between those states have been defined as follow:

1. From Engine ON to Engine OFF : if the car velocity is smaller than 1 m/s and the

driver load is 0 (acceleration pedal), the engine is switch off

2. From Engine OFF to Engine startup : if the first gear is engaged, the engine starts

up.

3. From Engine Startup to engine on : if the engine velocity is higher than 800 rpm, the

engine is ON.

1

2

3

Example #2

1

2

3







Engine RPM, total fuel consumption, battery State of Charge (no regenerative braking, no stop/start, regenerative braking, no stop/start, regenerative braking, stop/start)

Example #3




Remarks

• Thanks to the batch option, you can compare very easily and fastly different strategies.

• Model of Electric machine are easy to use and can be rezized rapidly thanks to the

Interface for the pre-sizing step

• Basic/advanced control can be done thanks to the signal library and StateChart. Co-

Simulation or coupled simulation can be done as well with Matlab/Simulink

Example #3






• Full automotive boardnet with regenerative breaking





• Examples

• Wrap-up

Table of content



Hybrid bus with a detailed electric powertrain

Demo content

• Detailed models of capacitor, inverter, motor

• Advanced architecture models

Example #4

Objective

• Design a detailed hybrid/electric powertain

• Validate architecture choices by analyzing system performance



Hybrid bus with a detailed electric powertrain

Example #4






• Full automotive boardnet with regenerative breaking





• Examples

• Wrap-up

Table of content



The battery, a key system for hybridization

To reduce fuel consumption & pollutant emissions, drivetrain electrification is a viable alternative to conventional

vehicle architecture.

HEV/EV performance depends strongly on the performance of the battery pack.

Despite lot of advantages in terms of energy density, efficiency & lifetime, Li-ion battery are:

• Very expensive

• Highly temperature sensitive

• Lifetime is not so high

Battery temperature Low High

Damage the battery Decrease of battery

capacity

Acceptable working temperature

How to model a battery? How to get the model parameters? How to determine the heat flux on duty cycles?

Example #5



Models available in LMS Imagine.Lab Amesim

Qu

asi

sta

tic

0-1

Hz

Dyn

am

ic

0-1

00

Hz

Generic battery and

ultra-capacitor

Generic battery and

ultra-capacitor

Validated components

Battery identification

tool

Validated components

low

high

Several battery models are available, from simple to advanced ones:

Example #5




Demo content

• Detailed battery models

• Integration of thermal management

• Advanced architecture models

Example #5

Objective

• Evaluate the cooling system performance

• Optimize the refrigerant loop




Example : Advanced architecture model (2 loops example - liquid & refrigerant loop)

Co

nd

en

ser

Evap

ora

tor

Batt

ery

Pack

Ch

ille

r

Rad

iato

r

Example #5




Below the LMS Amesim model of battery cooling with the refrigerant and coolant loops:

Example #5




1. During all the simulation, the refrigerant loop is used primarily for the cabin cooling through the evaporator – compressor speed set to

3000 rpm

2. The battery target temperature is set to 29 degC

3. The battery is cooled down through the coolant loop

4. At the beginning of the simulation, the chiller is by-passed (ambient temperature is 25°C) but since cooling power is not sufficient, the

chiller is used (after 1089s)

5. The battery temperature can be regulated around 29 degC

6. The impact of the chiller operation can be seen on the air temperature at the evaporator outlet

Example #5











• Some references

• Wrap-up

Table of content



“LMS Imagine.Lab Amesim allows us to build a

stable and performing multi-physics model for the

14V electrical system and makes it possible to

optimize advanced energy management laws in

early phases during the V-cycle.”

Emmanuel Laurain

Simulation expert designer

RENAULT

Automotive boardnet design and analysis at RENAULT with LMS Imagine.Lab

Amesim

Challenges

• Early sizing and optimization of the 14V electrical system

• Validation and optimization of electrical energy management laws

Solution • LMS Imagine.Lab Automotive Electrical Systems solution

• LMS Engineering and Customer Services

Benefits • Contribute to product quality and development time and cost reduction

• Early define the alternator/battery combination considering loads

• Specify energy management laws to optimize energy consumption

• Assess the influence of every electrical organ on the network and other loads

• Efficiently manage and share models along the design process



“The LMS Imagine.Lab Amesim solutions enables

Continental to estimate the potential of a new

product to reduce CO2 emissions for a given

configuration, bringing a crucial answer to its

customers.”

Hervé Dupont

Advanced Development for Engine systems

CONTINENTAL

CONTINENTAL predicts CO2 emissions for a mild-hybrid vehicle with LMS

Imagine. Lab Amesim

Challenges

• Need to estimate CO2 emissions of a mild- hybrid vehicle

Solution • LMS Imagine.Lab Internal Combustion Engine / Hybrid and Electric

Vehicle solution

Benefits • Predict CO2 emissions and understand the interactions between the

contributors

• Achieve a complete and detailed energy balance

• Define the relevance of a new component or technological choices

• Develop, validate and pre-calibrate control functions



“LMS Imagine.Lab Amesim is of great help to

renewable energy and automotive industries

players. This allows design engineers to make the

right technical and economical choices in the right

time schedule.”

Eric Prada

Electrochemical R&D Engineer

IFPEN

IFPEN enhances lifetime battery modeling for longer-life green vehicles using


Challenges

• Develop battery aging simulation functionalities

• Strengthen positioning of LMS Imagine.Lab Amesim as a best-in-class

modeling and simulation platform

Solution • LMS Imagine.Lab Automotive Electrical Systems solution to:

• Encapsulate fundamental physical phenomena

• Operate simulation platform efficiently

• Analyze electrochemical energy storage system behavior

Benefits • Built easy-to-use, high-fidelity aging models

• Obtained reliable aging simulation results

• Analyzed 10 years of battery behavior in a few hours











• Some references

• Wrap-up

Table of content



Wrap-up

• Power consumption of vehicle electrical systems has increased

dramatically over the past years, electrical network complexity as well (like

14V / 48V hybrid network).

• Vehicle electrification is not only restricted to Hybrid Vehicle & Electric

Vehicle. All the subsystems will be, at mid or long term, electrically driven (AC

compressor, turbo-charger, etc.).

• Battery is one of the key component in vehicle electrification (very

expensive, highly temperature sensitive).

• Some battery models (generic or pre-calibrated) are available in LMS Imagine.Lab

Amesim

• Our battery identification tool could help to define the battery parameters

Unrestricted © Siemens AG 2015 Realize innovation.

Thank you!

Date post:	13-Feb-2017
Category:	Automotive
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