Energy Management of Fuel Cell Hybrid Vehicle Based on Partially Observable Markov Decision Process

• Published in: IEEE transactions on control systems technology Vol. 28; no. 2; pp. 318 - 330
• Main Authors: Shen, Di, Lim, Cheng-Chew, Shi, Peng, Bujlo, Piotr
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer simulation; Convex programing; Electric motors; Energy flow; Energy management; fuel cell hybrid vehicle; Fuel cells; Hybrid vehicles; Markov analysis; Markov chain Monte Carlo (MCMC); Markov chains; Markov processes; Model accuracy; model predictive control (MPC); partially observable Markov decision process (POMDP); Performance evaluation; Power demand; Power generation; Resistance
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2018.2878173

This paper presents a nonmyopic energy management strategy (EMS) for controlling multiple energy flow in fuel cell hybrid vehicles. The control problem is solved by convex programing under a partially observable Markov decision process-based framework. We propose an average-reward approximator to estimate a long-term average cost instead of using a model to predict future power demand. Thus, the dependence between the system closed-loop performance and the model accuracy for predicting the future power demand is decoupled in the energy management design for fuel cell hybrid vehicles. The energy management scheme consists of a real-time self-learning system, an average-reward filter based on the Markov chain Monte Carlo sampling, and an action selector system through the rollout algorithm with a convex programing-based policy. The performance evaluation of the EMS is conducted via simulation studies using the data obtained from real-world driving experiments and its performance is compared with three benchmark schemes.