http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management
Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state of the art in hybrid electric vehicle system modeling and management. With a focus on learning-based energy management strategies, this book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle. This 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, multiobjective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, this 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 modeling 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.
Front Cover -- Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management -- Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management -- Copyright -- Contents -- Preface -- Acknowledgments -- 1 -- Introduction -- 1. Motivation -- 2. Introduction on hybrid electric vehicles -- 2.1 Definition of hybrid electric vehicles -- 2.2 Classification of hybrid electric vehicles -- 2.2.1 Traditional hybrid electric vehicles -- 2.2.2 New hybrid electric vehicles -- 2.2.2.1 Lithium battery/supercapacitors HEV -- 2.2.2.2 Fuel cells/supercapacitors HEV
2.2.2.3 Fuel cells/lithium batteries HEV -- 3. Energy management systems -- 3.1 Rule-based EMSs -- 3.1.1 Deterministic rule based EMSs -- 3.1.2 On/off EMSs -- 3.1.3 Power follower EMSs -- 3.1.4 Fuzzy logic EMSs -- 3.2 Optimization-based EMSs -- 3.2.1 Global optimization EMSs -- 3.2.1.1 Dynamic programming -- 3.2.1.2 Stochastic dynamic programming -- 3.2.1.3 Genetic algorithm -- 3.2.2 Instantaneous optimization strategies -- 3.2.2.1 Equivalent consumption minimization strategy -- 3.2.2.2 Pontryagin's minimum principle -- 3.2.2.3 Model predictive control -- 3.3 Artificial intelligence based EMSs
3.3.1 Neural network -- 3.3.2 Support vector regression -- 3.3.3 Reinforcement learning -- 4. Challenges -- 4.1 Degradation modeling -- 4.2 Optimality -- 4.3 Balance between efficiency and performance -- References -- 2 -- System modeling of hybrid electric vehicle -- 1. Introduction -- 1.1 Background -- 1.2 Literature review -- 1.2.1 Supercapacitor -- 1.2.2 Battery -- 1.2.3 Fuel cell -- 1.3 Safety considerations and standards -- 1.3.1 Supercapacitors -- 1.3.2 Lithium-ion batteries -- 1.3.3 PEMFCs -- 1.4 Summary -- 2. Hybrid electric vehicle -- 2.1 FC/SC HEV -- 2.2 BAT/SC HEV
3. Modeling of power sources -- 3.1 Modeling of fuel cell -- 3.2 Modeling of battery -- 3.3 Modeling of supercapacitor -- 3.3.1 Circuit model -- 3.3.2 Power loaded supercapacitor -- 3.4 Vehicle speed power model -- 4. Simulation results -- 4.1 Fuel cell -- 4.2 Battery -- 4.3 Supercapacitor -- 4.4 Vehicle speed power results -- 4.5 Details of modeling resources -- 5. Conclusions -- References -- 3 -- Neural network modeling for SOH of lithium-ion battery and performance degradation prediction of fuel cell -- 1. Introduction -- 1.1 Background -- 1.2 Battery SOH estimation
1.3 PEMFC performance degradation prediction -- 1.4 Contributions -- 2. Transfer learning based CNN for SOH estimation -- 2.1 The architecture of CNN -- 2.2 CNN model constructing -- 2.2.1 CNN training algorithm -- 2.2.2 Evaluation of CNN model -- 2.3 Improvements of CNN -- 2.3.1 Method to prevent overfitting -- 2.3.2 Adjustment strategy of learning rate -- 2.4 Transfer learning method -- 2.5 The process of the CNN-based SOH estimation -- 2.6 Results and discussions -- 2.6.1 Experimental setting -- 2.6.2 Result analysis -- 2.6.3 Performance comparison based on NASA datasets
Description based upon print version of record.
3. NSGA-II-ESN for performance degradation prediction of PEMFC