The invention relates to a multi-target deep reinforcement learning energy management method for a fuel cell hybrid vehicle, and the method comprises the following steps: S1, obtaining a typical driving data training neural network model, obtaining a driving condition recognition model, and recognizing different driving conditions based on the driving condition recognition model; s2, establishing a simulation environment, defining an intelligent agent in the simulation environment, and designing a multi-objective optimization reward function; s3, performing gradient training on the intelligent agent by adopting a depth deterministic strategy to obtain a trained intelligent agent; and S4, testing the trained intelligent agent, then obtaining actual driving data, outputting an actual working condition based on the driving working condition identification model, and inputting the actual working condition into the trained intelligent agent to obtain an actual energy management strategy. Compared with the prior art, the method has the advantages that energy management of the multi-target hybrid electric vehicle is achieved, and then the overall performance of the hybrid electric vehicle is improved.
本发明涉及一种燃料电池混动汽车多目标深度强化学习能量管理方法,方法包括以下步骤:S1、获取典型的行驶数据训练神经网络模型,得到行驶工况识别模型,基于所述驶工况识别模型识别不同的行驶工况;S2、建立模拟环境,在模拟环境中定义智能体,并设计多目标优化奖励函数;S3、采用深度确定性策略梯度训练智能体,得到训练完成的智能体;S4、对训练完成的智能体进行测试,然后获取实际行驶数据,基于行驶工况识别模型输出实际工况,将实际工况输入训练完成的智能体,得到实际能量管理策略。与现有技术相比,本发明具有实现多目标混合动力汽车的能量管理,进而提高混合动力汽车的整体性能等优点。
Multi-target deep reinforcement learning energy management method for fuel cell hybrid vehicle
一种燃料电池混动汽车多目标深度强化学习能量管理方法
2025-05-06
Patent
Electronic Resource
Chinese
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion |
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