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Umfang: 1 online resource (348 pages)

Ausgabe: 1st ed.

ISBN: 0-443-13190-2

Anmerkung: 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 -- 3. NSGA-II-ESN for performance degradation prediction of PEMFC -- 3.1 ESN model -- 3.2 NSGA-II for ESN optimization -- 3.3 Overall modeling process -- 3.4 Result and discussion -- 3.4.1 Data description and preprocessing -- 3.4.2 Performance metrics -- 3.4.3 Materials and resources -- 3.4.4 Simulation experiments -- 4. Conclusions -- References -- 4 - Optimal fuzzy energy management for fuel cell/supercapacitor system using neural network-based driving pattern ... -- 1. Introduction -- 1.1 Background -- 1.2 Problem statement -- 1.3 Literature review -- 1.4 The contribution of method -- 2. Optimal NN driving pattern recognition -- 2.1 Feature extraction -- 2.2 Neural network driving pattern recognition -- 2.3 Feature selection and NN parameter optimization by GA. , 2.3.1 Hybrid encoding for NN structure and parameters -- 2.3.2 Fitness function -- 2.3.3 Genetic operators for MLPNN -- 2.3.4 Optimization processes of genetic algorithm -- 2.4 Simulation results -- 2.4.1 Driving pattern recognition -- 2.4.2 Comparison and analysis -- 3. Adaptive fuzzy EMS with driving pattern recognition -- 3.1 Adaptive fuzzy energy management controller -- 3.2 Adaptive fuzzy EMS optimization by GA -- 3.2.1 The design of objective function -- 3.2.2 Genetic encoding -- 3.2.3 Genetic operators -- 3.2.4 Optimization processes for adaptive fuzzy EMS -- 3.3 Simulation results -- 3.3.1 Optimal solution of adaptive fuzzy EMS by GA -- 3.3.2 Performances analysis and comparison -- 4. Resources and limitations -- 4.1 Resources -- 4.2 Limitations -- 5. Conclusions -- 5.1 Summary -- 5.2 Further consideration -- References -- 5 - Optimal fuzzy energy management system design based on NSGA-III-SD for battery/supercapacitor HEV -- 1. Introduction -- 1.1 Background -- 1.2 Literature review -- 1.3 The contribution of method -- 2. Problem description -- 2.1 Constraint multiobjective optimization problem -- 2.2 Robust multi-objective optimization problem -- 3. NSGA- III based on similarity and diversity selection -- 3.1 The framework of the proposed NSGA-III -- 3.2 Reference points generation -- 3.3 Association with reference points -- 3.4 Fitness functions evaluation -- 3.5 Evaluation metrics -- 3.6 Main parameters of NSGA-III-SD -- 3.7 Results comparison -- 4. Fuzzy EMS based on NSGA-Ⅲ-SD -- 4.1 Framework of NSGA-Ⅲ-SD optimal fuzzy EMS -- 4.2 Fuzzy EMS design -- 4.3 Encoding of NSGA-Ⅲ-SD -- 4.4 Optimization processes -- 4.5 Fuzzy EMS experiments -- 5. Robust NSGA-III-SD optimal fuzzy EMS -- 5.1 Framework of RNSGA-III-SD fuzzy EMS -- 5.2 Dynamic robust evaluation model based on prediction -- 5.3 Robust fitness function design. , 5.4 Robust NSGA-III-SD optimization processes -- 5.5 Experimental results -- 6. Conclusions -- References -- 6 - Q learning-based hybrid energy management strategy -- 1. Introduction -- 1.1 Background -- 1.2 Literature review -- 1.3 Motivation and innovation -- 2. Fuzzy energy management system using Q-learning -- 2.1 Framework of fuzzy energy management system utilizing Q-learning -- 2.2 EMS based on traditional Q-learning for HEV -- 2.3 Improved Q-learning-based algorithm -- 2.3.1 Q-table optimization by GA -- 2.3.2 Genetic encoding and operators -- 2.3.3 Offline training of Q-learning algorithm -- 2.3.4 Flow chart of the improved Q-learning EMS -- 2.3.5 Online Q-learning based fuzzy EMS -- 2.4 Simulation results -- 2.4.1 Off-line training -- 2.4.2 Performances comparison -- 3. Q-learning-based EMS with deterministic rule for HEV -- 3.1 QL in HEV energy control -- 3.2 Algorithm design -- 3.3 Real-time energy management -- 3.4 Simulation results -- 3.4.1 Off-line training -- 3.4.2 Real-time application -- 3.4.3 Performances comparison -- 4. Troubleshooting and limitation -- 4.1 Troubleshooting -- 4.1.1 Convergence issues -- 4.1.2 Safety range violation -- 4.1.3 Real-time adaptation -- 4.2 Limitations -- 4.2.1 Training dependency -- 4.2.2 Discretization artifacts -- 4.2.3 Deterministic rule and fuzzy rule rigidity -- 4.2.4 Sensitivity to hyperparameters -- 5. Conclusion -- 5.1 Summary and conclusions -- 5.2 Further information -- References -- 7 - Improved DDPG hybrid energy management strategy based on LSH -- 1. Introduction -- 1.1 Background -- 1.2 Problem statement -- 1.3 Literature review -- 1.4 Safety considerations and standards -- 2. Improved DDPG strategy based on LSH -- 2.1 Method description -- 2.1.1 LSH (SimHash) algorithm introduction -- 2.2 Method procedure -- 2.2.1 Initialize DDPG reinforcement learning module. , 2.2.2 Initialize LSH counting module -- 2.2.3 Online manage HEV energy system -- 2.3 Materials and resources -- 2.4 Optimization and troubleshooting -- 2.5 Limitations -- 3. Simulation and result analysis -- 3.1 Algorithm comparison experiment -- 3.2 Results -- 4. Discussion and evaluation -- 5. Conclusion -- 5.1 Summary -- 5.2 Further information -- References -- 8 - Further idea on meta EMS for HEV -- 1. Introduction -- 2. Meta-reinforcement learning for EMS -- 2.1 Framework of meta RL EMS -- 2.2 Meta-EMS based on MAML and PPO -- 3. Simulation results -- 3.1 Adaption ability comparison -- 3.2 Control performances comparison -- 4. Conclusion -- References -- Index -- Back Cover.

Weitere Ausg.: ISBN 0-443-13189-9

Sprache: Englisch