Master Thesis

ASEMS: AUTONOMOUS-SPECIFIC ENERGY MANAGEMENT STRATEGY ASEMS: AUTONOMOUS-SPECIFIC ENERGY MANAGEMENT STRATEGY BY SAEED AMIRFARHANGI BONAB, B.A.SC. A Thesis Submitted to the Department of Mechanical Engineering and the School of Graduate Studies in Partial Fullfilment of the Requirements for the Degree of Master of Applied Science McMaster University © Copyright by Saeed Amirfarhangi Bonab, August 2019 McMaster University MASTER OF APPLIED SCIENCE (2019) (Mechanical Engi- neering) Hamilton, Ontario, Canada TITLE: ASEMS: Autonomous-specific Energy Management Strategy AUTHOR: Saeed Amirfarhangi Bonab, B.A.Sc. (McMaster University) SUPERVISOR: Professor Ali Emadi NUMBER OF PAGES: ix, 160 ii Lay Abstract The automotive industry is on the verge of groundbreaking transformations as a result of electrification and autonomous driving. Electrified autonomous car of the future is sustainable, energy-efficient, more convenient, and safer. In addition to the advantages of electrification and autonomous driving individually, the intersection and interaction of these mainstreams provide new opportunities for further improvements on the vehicles. Autonomous cars generate an unprecedented amount of real-time data due to excessive use of perception sensors and processing units. This thesis considers the case of an autonomous hybrid electric vehicle and presents the novel idea of autonomous-specific energy management strategy. Specifically, this thesis is a proof-of-concept, a trial to exploit the motion planning data for a self-driving car to improve the fuel economy of the hybrid electric power unit by adopting a more efficient energy management strategy. With the ever-increasing number of autonomous hybrid electric vehicles, particularly in the self-driving fleets, the presented method shows an extremely promising potential to reduce the fuel consumption of these vehicles. iii Abstract This thesis addresses the problem of energy management of a hybrid electric power unit for an autonomous vehicle. We introduce, evaluate, and discuss the idea of autonomous-specific energy management strategy. This method is an optimization- based strategy which improves the powertrain fuel economy by exploiting motion planning data. First, to build a firm base for further evaluations, we will develop a high-fidelity system-level model for our case study using MATLAB/Simulink. This model mostly concerns about energy-related aspects of the powertrain and the vehicle. We will derive and implement the equations for each of the model subsystems. We derive model parameters using available data in the literature or online. Evaluation of the developed model shows acceptable conformity with the actual dynamometer data. We will use this model to replace the built-in rule-based logic with the proposed strategy and assess the performance. Second, since we are considering an optimization-based approach, we will develop a novel convex representation of the vehicle and powertrain model. This translates to reformulating the model equations using convex functions. Consequently, we will express the fuel-efficient energy management problem as the convex optimization problem. We will solve the optimization problem using dedicated numerical solvers. Extracting the control inputs using this approach and applying them on the high-fidelity model provides similar results to dynamic programming in terms of fuel consumption but in substantially less amount of time. This will act as a pivot for the subsequent real-time analysis. Third, we will perform a proof-of-concept for the autonomous-specific energy management strategy. We implement an optimization-based path and trajectory planning for a vehicle in the simplified driving scenario of a racing track. Accordingly, we use motion planning data to obtain the energy management strategy by solving an optimization problem. We will let the vehicle to travel around the circuit with the ability to perceive and plan up to an observable horizon using the receding horizon approach. Developed approach for energy management strategy shows a substantial reduction in the fuel consumption of the high-fidelity model, compared to the rule-based controller. iv Acknowledgements This research was undertaken, in part, thanks to funding from the Canada Excellence Research Chairs Program. I want to sincerely thank my supervisor and mentor, Professor Ali Emadi, for trusting, motivating, and empowering me throughout my graduate studies. Thanks to all researchers at McMaster Automotive Resource Centre for contributing to building an outstanding work environment. I would like to offer my special thanks to Fiat Chrysler Automobiles team, specifically those involved in the leadership in automotive powertrain (LEAP) project. I would like to acknowledge all the people that I have collaborated with, specifically my colleagues in room 218. Thanks to Giuseppe for making the room culture more friendly. Thanks to Iman for exploiting the multilingual potential of the room to the maximum. Thanks to Lucas for the lunchtime discussions. Thanks to Carin for being a car enthusiast. Thanks to Atriya for the homemade snacks. Thanks to Diego for possessing a good sense of humor. Thanks to Peter for teaching me the work-life balance. Thanks to Jeremy for the dynamic programming. And thanks to Sumedh for his research. The last but definitely not the least, I cannot find the words to express my deep appreciation to my beloved family and my wife, Nayereh, for believing and supporting me throughout my life. This would not have been possible without you. v Notation and Abbreviations A-HEV Autonomous Hybrid Electric Vehicle ABS Anti-Lock Brake ACC Adaptive Cruise Controller ADAS Advanced Driver-Assistant Systems AI Artificial Intelligence ASEMS Autonomous-Specific Energy Management Strategy AV Autonomous Vehicle AWD All Wheel Drive BEV Battery Electric Vehicle CAFE Corporate Average Fuel Economy CNN Convolutional Neural Network DP Dynamic Programming ECMS Equivalent Consumption Minimization Strategy EMS Energy Management Strategy EPA Environmental Protection Agency ESS Energy Storage System F1 Formula 1 vi FC Fuel Consumption FCA Fiat Chrysler Automobiles FIA Federation Internationale de l’Automobile GHG Greenhouse Gas GPS Global Positioning System HEV Hybrid Electric Vehicle ICE Internal Combustion Engine ITS Intelligent Transportation System KKT Karush-Kuhn-Tucker LDV Light-duty Vehicle LMI Linear Matrix Inequality LP Linear Programming M/G Motor/Generator MPC Model Predictive Control NEDC New European Driving Cycle OCV Open Circuit Voltage OEM Original Equipment Manufacturer PGS Planetary Gear Set PHEV Plug-in Hybrid Electric Vehicle PMP Pontryagin Minimum Principle QP Quadratic Programming SDP Semidefinite Programming vii SOC State of Charge SOH State of Health UDDS Urban Dynamometer Driving Schedule VMT Vehicle Miles Traveled VTTS Value of Travel Time Saving ZEV Zero Emission Vehicle viii Contents 1 Introduction 1 1.1 Motivation . .2 1.2 Thesis Outline and Contributions . .6 1.2.1 Chapter 2: Hybrid Electric Powertrains . .7 1.2.2 Chapter 3: Autonomous Driving . .7 1.2.3 Chapter 4: Fundamentals of Energy Management Strategies . .7 1.2.4 Chapter 5: High-fidelity Model . .7 1.2.5 Chapter 6: Fast Offline Energy Management Strategy . .8 1.2.6 Chapter 7: Real-time Energy Management Strategy for an Au- tonomous Vehicle . .8 1.3 Publications . .9 2 Hybrid Electric Powertrains 10 2.1 Introduction . 10 2.2 Electrification Level and Architecture . 11 2.3 Fuel Economy . 14 2.4 Emissions . 15 2.5 Performance . 16 2.6 Future Trend . 19 3 Autonomous Driving 21 3.1 Introduction . 21 3.2 Global Impact . 22 3.3 Level of Autonomy . 27 3.4 Autonomous Vehicle Modules . 28 ix 4 Fundamentals of Energy Management Strategies 32 4.1 Introduction . 32 4.2 Rule-based and Fuzzy Rule-based . 33 4.3 Equivalent Consumption Minimization Strategy . 34 4.4 Dynamic Programming . 35 4.5 Pontryagin’s Minimum Principle . 35 4.6 Convex Optimization . 36 4.7 Model Predictive Control . 38 4.8 Comparison . 39 5 High-fidelity Vehicle Model 40 5.1 Introduction . 40 5.2 Vehicle Model . 42 5.2.1 Internal Combustion Engine . 46 5.2.2 Motor Generator Units and Power Electronics . 46 5.2.3 Battery . 48 5.2.4 Power-split . 50 5.2.5 Final Drive and Wheel . 51 5.2.6 Longitudinal Dynamics . 51 5.3 Control Unit . 54 5.4 Driver . 58 5.5 Evaluation . 59 6 Fast Offline Energy Management Strategy 64 6.1 Introduction . 64 6.2 Convex Model . 65 6.2.1 Pre-processing . 66 6.2.2 Power-split . 67 6.2.3 Internal Combustion Engine . 68 6.2.4 Motor Generator Units and Power Electronics . 73 6.2.5 Battery . 75 6.3 Optimal Control Problem . 77 6.4 Results . 81 6.4.1 Relaxations . 81 6.4.2 Results on Convex Model . 83 x McMaster - Mechanical Engineering M.A.Sc. Thesis - Saeed Amirfarhangi Bonab 6.4.3 Trade-off Curve . 89 6.4.4 Results on High-fidelity Model . 90 7 Real-time Energy Management Strategy for an Autonomous Vehicle 93 7.1 Introduction . 93 7.2 Path Planning . 96 7.2.1 Discrete Path Planning . 96 7.2.2 Optimization Problem . 100 7.2.3 Results . 103 7.2.4 Improving Accuracy of the Linear Model . 105 7.3 Trajectory Planning . 107 7.3.1 Discrete Trajectory Planning . 107 7.3.2 Optimization Problem . 109 7.3.3 Results . 111 7.4 Iterative Motion Planning . 114 7.5 Real-time Energy Management . 122 7.5.1 Optimization Problem . 122 7.5.2 Iterative Motion Planning and EMS . 124 7.5.3 Results . 126 8 Conclusions and Future Work 135 8.1 Conclusions . 135 8.2 Future Work . 137 8.2.1 Advanced High-Fidelity Model . 138 8.2.2 Convex Optimization Problem Complexity . 138 8.2.3 Solving Convex Optimization Problem . 138 8.2.4 Motion Planning Data . 139 8.2.5 Vehicle Path Controller . 139 8.2.6 State Estimation . 139 8.2.7 Testing on an A-HEV . 139 Appendices 141 A Interactive Interface 142 xi Chapter 1 Introduction The automotive industry is a quintessential combination of art, technology, and business.

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