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.

翻译：建模困难、时变模型及不确定外部输入是燃料电池混合动力汽车能量管理面临的主要挑战。本文提出一种基于模糊强化学习的燃料电池混合动力汽车能量管理策略，以降低燃料消耗、维持电池长期运行并延长燃料电池系统寿命。模糊Q学习是一种无需模型即可通过与环境交互实现自主学习的无模型强化学习方法，因此无需对燃料电池系统进行建模。此外，燃料电池频繁启停会缩短其剩余使用寿命。所提方法通过将燃料电池启动次数惩罚纳入强化学习奖励函数中，抑制了频繁启停行为。同时，应用模糊逻辑逼近Q学习中的价值函数，可解决连续状态与动作空间问题。最终，基于Python的训练与测试平台验证了该方法在初始状态变化、模型变化及驾驶工况变化条件下的有效性与自学习改进能力。