  Modeling difficulty, time-varying model, and uncertain external inputs are
the main challenges for energy management of fuel cell hybrid electric
vehicles. In the paper, a fuzzy reinforcement learning-based energy management
strategy for fuel cell hybrid electric vehicles is proposed to reduce fuel
consumption, maintain the batteries' long-term operation, and extend the
lifetime of the fuel cells system. Fuzzy Q-learning is a model-free
reinforcement learning that can learn itself by interacting with the
environment, so there is no need for modeling the fuel cells system. In
addition, frequent startup of the fuel cells will reduce the remaining useful
life of the fuel cells system. The proposed method suppresses frequent fuel
cells startup by considering the penalty for the times of fuel cell startups in
the reward of reinforcement learning. Moreover, applying fuzzy logic to
approximate the value function in Q-Learning can solve continuous state and
action space problems. Finally, a python-based training and testing platform
verify the effectiveness and self-learning improvement of the proposed method
under conditions of initial state change, model change and driving condition
change.
