Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

中文翻译：

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基于专家辅助深度确定性策略梯度算法的混合动力公交车电池能量管理

能源管理是保证混合动力系统可靠性和经济性的一种使能技术。本文为混合动力电动公交车 (HEB) 提出了一种基于机器学习的新型能源管理策略，强调了车载锂离子电池 (LIB) 系统的热安全性和退化意识。首先，深度确定性策略梯度（DDPG）算法首次与专家辅助系统相结合，以增强“冷启动”性能并优化HEB的功率分配。其次，在所提出的算法框架中，嵌入了对超温和锂离子电池退化的惩罚，以提高热安全执法和整体驾驶成本降低方面的管理质量。