Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state-of-the-art in hybrid electric vehicle system modelling and management. With a focus on learning-based energy management strategies, the book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.The book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multi objective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, the book also introduces State of Charge and State of Health prediction methods and real time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modelling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 System modeling of hybrid electric vehicle - p. 39 3. Chapter 3 Neural network modeling for SOH of lithium-ion battery and performance degradation prediction of fuel cell - p. 67 4. Chapter 4 Optimal fuzzy energy management for fuel cell/supercapacitor system using neural network–based driving pattern recognition - p. 117 5. Chapter 5 Optimal fuzzy energy management system design based on NSGA-III-SD for battery/supercapacitor HEV - p. 173 6. 1: Introduction - p. 175 7. 2: Problem description - p. 180 8. 3: NSGA- III based on similarity and diversity selection - p. 183 9. 4: Fuzzy EMS based on NSGA-Ⅲ-SD - p. 195 10. 5: Robust NSGA-III-SD optimal fuzzy EMS - p. 218 11. 6: Conclusions - p. 232 12. Chapter 6 Q learning-based hybrid energy management strategy - p. 237 13. Chapter 7 Improved DDPG hybrid energy management strategy based on LSH - p. 281 14. Chapter 8 Further idea on meta EMS for HEV - p. 313 15. Index - p. 325
Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state-of-the-art in hybrid electric vehicle system modelling and management. With a focus on learning-based energy management strategies, the book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.The book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multi objective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, the book also introduces State of Charge and State of Health prediction methods and real time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modelling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 System modeling of hybrid electric vehicle - p. 39 3. Chapter 3 Neural network modeling for SOH of lithium-ion battery and performance degradation prediction of fuel cell - p. 67 4. Chapter 4 Optimal fuzzy energy management for fuel cell/supercapacitor system using neural network–based driving pattern recognition - p. 117 5. Chapter 5 Optimal fuzzy energy management system design based on NSGA-III-SD for battery/supercapacitor HEV - p. 173 6. 1: Introduction - p. 175 7. 2: Problem description - p. 180 8. 3: NSGA- III based on similarity and diversity selection - p. 183 9. 4: Fuzzy EMS based on NSGA-Ⅲ-SD - p. 195 10. 5: Robust NSGA-III-SD optimal fuzzy EMS - p. 218 11. 6: Conclusions - p. 232 12. Chapter 6 Q learning-based hybrid energy management strategy - p. 237 13. Chapter 7 Improved DDPG hybrid energy management strategy based on LSH - p. 281 14. Chapter 8 Further idea on meta EMS for HEV - p. 313 15. Index - p. 325
