Full Vehicle Simulation for Powertrain
Electrification
Abhisek Roy
Application Engineer – System simulation and Controls development
MathWorks
Abhisek Roy | LinkedIn
© 2021 The MathWorks, Inc.
1
Electrified Powertrain Selection
▪
▪
Considering variants of single motor, parallel hybrids
Where is the best location for the motor?
2
Problem Statement
▪
Minimize:
– Fuel consumption (mpg for drive cycles Highway, City, US06)
– Acceleration time (t0-60mph)
▪
Subject to:
–
–
–
–
Actuator limits for motor & engine
Velocity within 2 mph window of drive cycle target velocity
SOC within [SOClow, SOChigh]
|SOCfinal – SOCinit| < tol → requires iteration on supervisory control parameter
3
Design Exploration: Challenges
▪
How can I efficiently assemble plant and controller models for a
variety of electrified powertrain configurations?
▪
How can I quickly perform attribute assessment on those variants?
▪
How can I determine which parameters are the most sensitive?
▪
How can I identify an optimal design during early design stages?
4
Design Exploration: Overview
1.
Start with a pre-built vehicle model and customize as needed
2.
Use response optimization to identify optimal parameter values
3.
Use sensitivity analysis to understand impact of parameters
5
Powertrain Blockset
▪
Goals:
– Provide starting point for engineers to build good plant / controller models
– Provide open and documented models
– Provide very fast-running models that work with popular HIL systems
6
Powertrain Blockset Features
Library of blocks
Pre-built reference applications
7
Flexible Modeling Framework
1.
Choose a vehicle configuration
3.
– Select a reference application as a
starting point
Customize the controllers
– Parameterize the controllers
– Customize supervisory control logic
– Add your own controller variants
4.
Perform closed-loop system
testing
– Sensitivity analyses
– Design optimization
– MIL / SIL / HIL testing
2.
Customize the plant model
– Parameterize the components
– Customize existing subsystems
– Add your own subsystem variants
8
Drivetrain
Energy Storage
and Auxiliary Drive
Propulsion
Transmission
Vehicle Dynamics
Vehicle Scenario Builder
9
▪
Reference Applications
▪
Full Vehicle Models
Virtual Engine Dynamometers
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Plant Model:
System level
11
Plant Model:
Driveline Subsystem
Conv
P0
P1
P2
P3
P4
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Plant Model:
Electrical Subsystem
650 V Battery & DC-DC Converter
(smaller sizing for P0)
30 kW Motor
(10 kW for P0)
13
Plant Model:
Engine Subsystem
1.5l Gasoline Engine
Maps generated from GT-POWER®
14
Controls-oriented Model Creation
Engine Dynamometer
Detailed, design-oriented model
Fast, but accurate controls-oriented model
15
Controller:
Hybrid Control Module
▪
▪
▪
Acceleration Pedal → Torque
Regenerative Brake Blending
Energy Management
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HEV Energy Management
▪
▪
▪
Instantaneous torque (or power)
command to actuators (engine,
electric machines)
Subject to constraints:
Tdemand = Teng + Tmot ???
Attempt to minimize energy
consumption, maintain drivability
17
Equivalent Consumption Minimization Strategy (ECMS)
▪
What is ECMS?
– Supervisory control strategy to decide when to use engine, motor or both
– Based on analytical instantaneous optimization
▪
Why use ECMS?
– Provides near optimal control if drive cycle is known a priori
– Can be enhanced with adaptive methods (i.e. Adaptive-ECMS)
min 𝑃𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡 𝑡 = 𝑃𝑓𝑢𝑒𝑙 𝑡 + 𝑠(𝑡) ∙ 𝑃𝑏𝑎𝑡𝑡𝑒𝑟𝑦 𝑡 ,
where s(t) are the “equivalent factors”
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Equivalent Consumption Minimization Strategy (ECMS)
Equivalent fuel saved
by future battery use
Equivalent fuel needed
to recharge battery
Drive
Mode
Regen
Mode
19
Equivalent Consumption Minimization Strategy (ECMS) Process
1.
Create torque
split vector
2.
Check constraints,
determine
infeasible conditions
3.
Calculate and
minimize cost
function
20
Equivalent Consumption Minimization Strategy (ECMS) Process
Infeasible Regions
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Equivalent Consumption Minimization Strategy (ECMS) Process
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Methodology
▪
Generate Powertrain Blockset mapped engine from GT-POWER model
▪
Perform architecture evaluation
– For each Px architecture (non-plug-in):
▪
Iterate on s (controller parameter) to achieve ΔSOC < 1% across each drive cycle
Assess fuel economy on city, highway and US06 drive cycles
▪
Assess acceleration performance on Wide Open Throttle (WOT) test
▪
– Compare fuel economy and performance across P0 – P4 architectures
▪
Perform P4 axle ratio sweep
– Assess attributes over a range of axle ratios
– Compare fuel economy and performance across P4 axle ratios
23
Charge Sustain Iteration Process
min ∆𝑆𝑂𝐶 2
Simulink Design Optimization
Update ‘s’
• Optimization / Global Optimization
• Parallel Computing
24
Architecture Comparison Results
City
Highway
US06
25
Architecture Comparison Results
Combined City (55%) / Highway (45%)
▪
Placing motors closer to the
drive wheel:
– Improves fuel economy (better
regen efficiency)
– Degrades performance (lower
mechanical advantage)
▪
Simulation allows you to quantify
the tradeoff
▪
ECMS provides a fair
comparison of alternatives
26
P4 Ratio Sweep Results
N = 4.56
N = 2.73
▪
P4 axle is independent of ICE axle
transmission ratios, shift maps, and
final ratio
▪
Quantify tradeoffs
– Higher ratios → Better for performance
and FTP75 / US06 mpg
– Lower ratios → Better for HWFET mpg
▪
Future study of 2-speed P4 axle
27
Subsystem Design: Challenges
▪
How can I assess the impact of my subsystem design on the full vehicle?
▪
How can I perform closed-loop testing of my subsystem?
▪
How can I incorporate detailed, multi-domain subsystem models into a
system level model?
28
Simscape
▪
Enables physical modeling (acausal)
of multidomain physical systems
– Assemble a schematic
– Equations derived automatically
– Leverage MATLAB and Simulink
V+
V29
Powertrain Blockset / Simscape Integration
▪
Create detailed, multi-domain subsystem models with Simscape
▪
Incorporate them into system level vehicle models from Powertrain Blockset
▪
Validate subsystem performance with closed loop simulation
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Battery Thermal Management
Incorporate battery thermal
management system into
EV reference application
31
Simulate large battery packs and BMS
32
Learn More about Battery Management System
33
Battery Thermal Management
▪
Three modes to control
coolant temperature
– Heating
– Ambient cooling (via radiator)
– Two-phase cooling (via HVAC)
▪
Control strategies
– Feedforward + PID
– Minimize power usage
EV Battery Cooling System - MATLAB & Simulink (mathworks.com)
34
Conventional Vehicle with Simscape Engine Cooling
1. Heat rejection calculation
1
2. Heat distributed between
oil and coolant
2
3. Temperature of cylinder
used to validate cooling
system performance
1
2
3
35
Custom Drivetrain or Transmission
▪
Replace portions of reference
application with custom models
assembled from Simscape libraries
▪
Use Variant Subsystems to
shift back and forth based on
current simulation task
Pre-Built Drivetrain
Custom Drivetrain
Custom Transmission
36
Simscape Vehicle Templates
© 2021 The MathWorks, Inc.
37
Tune
Regenerative
Braking
Algorithms
38
Simscape™ Vehicle Templates
Available on File Exchange
▪
Download Simscape Vehicle Templates
directly from the File Exchange
– Get files saved for use with
MATLAB® releases R2018b and higher
– Files hosted on GitHub so we
can react quickly to requests
▪
See Overview for list of capabilities
https://www.mathworks.com/solutions/physical-modeling/simscape-vehicle-templates.html
https://www.mathworks.com/matlabcentral/fileexchange/79484-simscape-vehicle-templates
39
Calibrating PMSM Torque Control Lookup Tables using ModelBased Calibration
40
Simulink as a Simulation Integration Platform
Focus of this talk
Data
Management
Solver
Technology
Vehicle
Configuration
Multi-actor
Scenarios
Visualization
Simulink
41
Resources
42
Advance your skills with MATLAB and Simulink courses
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43
Power Electronics Control Design with Simulink and Simscape
After this 1-day course you will be able to:
▪
Model direct current (dc) power
electronic components
▪
Control the level of fidelity in a model
▪
Develop controls for power electronics
▪
Model three-phase alternating current
(ac) power electronic components
▪
Control power electronics for motor
drive applications
See detailed course outline
44
Battery Modeling and Algorithm Development with Simulink®
After this 1-day course you will be able to:
▪
Perform cell characterization
▪
Model battery packs
▪
Add thermal fidelity to battery models
▪
Design control scheme for contactor
operation
▪
Perform State-Of-Charge estimation and
cell balancing
▪
Compute power limits and design fault
diagnostic system
45
MathWorks Consulting
Create
Model
Integrate
Software
Author
Scenarios
Simulate
& Analyze
Deploy
Simulation
MathWorks Out-of-the-box capability
MathWorks Consulting Services
Custom solution
https://www.mathworks.com/services/consulting.html
46
MathWorks Consulting | Proven Solutions
Reduce learning curve
Quicker implementation
Avoid common mistakes
Open and transparent
deliverables with
knowledge transfer
https://www.mathworks.com/services/consulting/customer-success-stories.html
https://www.mathworks.com/services/consulting/proven-solutions.html
47
Call to action and Q&A
▪
If you are working on a vehicle level
simulation project, identify a problem
statement, request MathWorks for a
trial of Powertrain Blockset
Abhisek Roy | LinkedIn
▪
▪
For Battery Modeling or Battery
Management System related projects,
request for a trial of Simscape and
Stateflow
Application Engineer
MathWorks
aroy@mathworks.com
For Motor Control Design and
Implementation, try out the new Motor
Control Blockset
48