An Online Energy Management Strategy for Fuel Cell Vehicles Based on Fuzzy Q-Learning and Road Condition Recognition
Duo Yang, Siyu Wang, Yuefeng Liao, Rui Pan
Abstract
Fuel cell vehicles represent a groundbreaking form of new energy vehicles, garnering considerable attention and development in recent years. The energy management of fuel cell hybrid electric vehicles has always been a research difficulty. This paper aims to improve the economy and durability of fuel cell and lithium battery hybrid systems, and proposes an innovative online energy management strategy that incorporates road condition recognition. Key features from multiple driving datasets are extracted to construct a classifier based on ensemble learning and decision tree, which can effectively identify road patterns. Then, a fuzzy Q-learning method is proposed for online power allocation. Different action spaces of Q-learning are designed to cater to different road patterns, enabling more effective adaptation to diverse roadway environments. In addition, the reward function is designed to account for hydrogen costs and degradation expenses. Through a series of simulation and hardware tests, the proposed method has been demonstrated to effectively achieve online energy management of fuel cell/battery system under varying road conditions. And a quantitative comparison validates that the proposed method outperforms traditional reinforcement learning algorithms in reducing both fuel consumption and system degradation rates.