Reinforcement Learning-enabled Intelligent Energy Management for Hybrid Electric Vehicles

Description

Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed. Preface Introduction Motivation HEV Powertrain Literature Review Review Literature Algorithm Literature Summary Powertrain Modeling and Reinforcement Learning Control-Oriented Modeling Transmission Modeling Engine and Generator Modeling Battery Modeling EM Modeling Energy Management Modeling Reinforcement Learning Overview of Reinforcement Learning Markov Decision Processes Algorithms for RL: Q-Learning and Sarsa Algorithms for RL: Dyna-Q and Dyna-H Summary Prediction and Updating of Driving Information Predictive Algorithms Nearest Neighborhood Fuzzy Coding Long Short-Term Memory Online Updating Algorithm Evaluation of Prediction Performance NND-Enabled Prediction Results Comparison of NND and FCG Evaluation of LSTM Summary Evaluation of Intelligent Energy Management System Benchmark Energy Management Methods Dynamic Programming-Based Controller Stochastic Dynamic Programming-Based Controller Optimality of RL-Based Energy Management Evaluation of Q-Learning and Sarsa Evaluation of Dyna-Q and Dyna-H RL-Based Predictive Energy Management Evaluation of Real-Time Energy Management Summary Conclusion References Author's Biography