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 10 Plant Model: System level 11 Plant Model: Driveline Subsystem Conv P0 P1 P2 P3 P4 12 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 16 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” 18 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 21 Equivalent Consumption Minimization Strategy (ECMS) Process 22 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 30 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 Get started for free with MATLAB Onramp, then build your skills with our self-paced trainings and instructor-led courses. Flexible Training • • • Over 50 courses available Virtual and in class offerings available Private customized events Expert Trainers Proven Methods • Hands-on instructions • MS and PhD degrees • Use of Adult Learning Principles • Unparalleled products knowledge • 95% rated real-world application to their jobs • 98% rating as subject matter experts • 96% of attendees recommend to others • 104% average increase in productivity • 144% average increase in competence 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