基于自学习模糊强化学习的燃料电池混合动力汽车寿命延长能量管理策略
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning
摘要：
建模难度大、模型时变、外部输入不确定是燃料电池混合动力汽车能量管理面临的主要挑战。为降低燃料电池混合动力汽车的燃油消耗，维持电池的长期运行，延长燃料电池系统的使用寿命，提出了一种基于模糊强化学习的能量管理策略。模糊Q学习是一种无模型强化学习，它可以通过与环境的交互来自我学习，因此不需要对燃料电池系统进行建模。此外，燃料电池的频繁启动将减少燃料电池系统的剩余使用寿命。该方法通过在强化学习的奖励中考虑对燃料电池启动次数的惩罚来抑制燃料电池的频繁启动。此外，应用模糊逻辑来近似Q-学习中的价值函数，可以解决连续状态和动作空间问题。最后，基于Python的训练测试平台验证了该方法在初始状态变化、模型变化和驾驶条件变化情况下的有效性和自学习能力的提高。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
