深度强化学习和模糊逻辑控制器代码设计用于燃料电池驱动电动汽车的能源管理
Seyed Mehdi Rakhtala Rostami1, Zeyad Al-Shibaany2,3, Peter Kay4
1School of Engineering, University of the West of England Bristol, Bristol, UK. Mehdi.Rakhtalarostami@uwe.ac.uk.
Scientific reports
|December 27, 2024
概括
本研究介绍了使用深度增强学习 (DRL) 和模糊逻辑为燃料电池电动汽车 (FCEV) 提供先进的能源管理系统. DRL-F战略提高了混合动力电动汽车的系统精度和组件寿命.
科学领域:
- * 汽车工程 汽车工程
- * * 控制系统 控制系统
- *可再生能源系统 *可再生能源系统
背景情况:
- *燃料电池电动汽车 (FCEV) 对零排放运输和减轻燃油经济挑战至关重要.
- *有效的能源管理系统 (EMS) 对于优化功率跟踪,系统精度和组件寿命 (燃料电池和电池) 等性能指标至关重要.
- *现有的EMS在处理混合动力发动机中的不确定性和短暂电流方面面临挑战.
研究的目的:
- *为FCEVs设计和实施一个层次化的能源管理框架.
- *为了提高系统准确性,功率跟踪和聚合物电解质膜燃料电池 (PEMFC) 和离子电池的寿命.
- * 为了减少FCEV动力系统内的短暂和峰值电流.
主要方法:
- * 制定FCEV能源管理的分层,双层控制战略.
- * 实施深度决定性政策梯度与双延迟 (DDPG TD3) 的低级控制循环.
- * 整合模糊逻辑,用于高级监管负载共享.
- * 在层次框架内设计三个深度强化学习 (DRL) 控制器.
主要成果:
- * 拟议的深度强化学习和模糊逻辑监督控制 (DRL-F) 策略与超扭曲算法和模糊逻辑监督控制 (STW-F) 相比,表现优越.
- * DRL-F在EUDC驾驶周期下实现了根平均平方误差 (RMSE) 的21.05%降低和平均误差的8.31%降低.
- * DRL-F方法在管理系统不确定性和干扰方面被证明更强大,更准确.
结论:
- *先进的学习方法,DRL-F,为FCEVs提供了更强大的,更准确和更精确的EMS.
- *这种层次控制策略有效地优化了能源流,提高了系统效率和组件耐用性.
- *这些发现支持在下一代基于的电动汽车中采用基于DRL的EMS.
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