| 基于增强学习算法的插电式燃料电池电动汽车能量管理控制策略 |
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| 作者姓名： | 林歆悠 夏玉田 魏申申 |
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| 作者单位： | 1).福州大学机械工程及自动化学院, 福州 350002 |
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| 基金项目： | 国家自然科学基金资助项目51505086 |
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| 摘 要： | 以一款插电式燃料电池电动汽车(plug-in fuel cell electric vehicle，PFCEV)为研究对象，为改善燃料电池氢气消耗和电池电量消耗之间的均衡，实现插电式燃料电池电动汽车的燃料电池与动力电池之间的最优能量分配，考虑燃料电池汽车实时能量分配的即时回报及未来累积折扣回报，以整车作为环境，整车控制作为智能体，提出了一种基于增强学习算法的插电式燃料电池电动汽车能量管理控制策略.通过Matlab/Simulink建立整车仿真模型对所提出的策略进行仿真验证，相比于基于规则的策略，在不同行驶里程下，电池均可保持一定的电量，整车的综合能耗得到明显降低，在100、200和300 km行驶里程下整车百公里能耗分别降低8.84%、29.5%和38.6%；基于快速原型开发平台进行硬件在环试验验证，城市行驶工况工况下整车综合能耗降低20.8%，硬件在环试验结果与仿真结果基本一致，表明了所制定能量管理策略的有效性和可行性.
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| 关 键 词： | 燃料电池汽车 增强学习 能量管理 Q_learning算法 控制策略 |
| 收稿时间： | 2018-10-15 |
Energy management control strategy for plug-in fuel cell electric vehicle based on reinforcement learning algorithm |
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| Affiliation: | 1).College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350002, China2).Key Laboratory of Fluid Power and Intelligent Electro-Hydraulic Control, Fuzhou University, Fuzhou 350002, China |
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| Abstract: | To cope with the increasingly stringent emission regulations, major automobile manufacturers have been focusing on the development of new energy vehicles. Fuel-cell vehicles with advantages of zero emission, high efficiency, diversification of fuel sources, and renewable energy have been the focus of international automotive giants and Chinese automotive enterprises. Establishing a reasonable energy management strategy, effectively controlling the vehicle working mode, and reasonably using battery energy for hybrid fuel-cell vehicles are core technologies in domestic and foreign automobile enterprises and research institutes. To improve the equilibrium between fuel-cell hydrogen consumption and battery consumption and realize the optimal energy distribution between fuel-cell systems and batteries for plug-in fuel-cell electric vehicles (PFCEVs), considering vehicles as the environment and vehicle control as an agent, an energy management strategy for the PFCEV based on reinforcement learning algorithm was proposed in this paper. This strategy considered the immediate return and future cumulative discounted returns of a fuel-cell vehicle's real-time energy allocation. The vehicle simulation model was built by Matlab/Simulink to carry out the simulation test for the proposed strategy. Compared with the rule-based strategy, the battery can store a certain amount of electricity, and the integrated energy consumption of the vehicle was notably reduced under different mileages. The energy consumption in 100 km was reduced by 8.84%, 29.5%, and 38.6% under 100, 200, and 300 km mileages, respectively. The hardware-in-loop-test was performed on the D2P development platform, and the final energy consumption of the vehicle was reduced by 20.8% under urban dynamometer driving schedule driving cycle. The hardware-in loop-test results are consistent with the simulation findings, indicating the effectiveness and feasibility of the proposed energy management strategy. |
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