燃料电池汽车的能源管理策略基于在线驾驶条件识别,使用双模型预测控制
Fuxiang Li1, Xiaolin Wang1, Xucong Bao1
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Street, Jiangning District, Nanjing 211106, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究介绍了燃料电池电动汽车 (FCEV) 的先进能源管理策略,该策略使用波形变形长短内存 (WT-LSTM) 进行驾驶识别和双模预测控制 (MPC). 在WTL-DMPC EMS显著提高FCEV的经济性能和节能.
科学领域:
- 可持续的运输可持续的运输
- 汽车工程中的人工智能
- 控制系统 控制系统
背景情况:
- 燃料电池电动汽车 (FCEV) 对于应对气候变化和能源危机至关重要.
- 有效的能源管理策略 (EMS) 对于在各种驾驶条件下优化FCEV动力总成效率至关重要.
研究的目的:
- 为FCEVs提出一种新的双模型预测控制能源管理策略 (DMPC EMS).
- 通过基于LSTM的驾驶状况识别来提高FCEV的经济性能和稳定性.
- 通过波形变换 (WT) 提高驾驶条件识别的准确性和适应性.
主要方法:
- 波形变换器 (WT) 与长短期内存 (LSTM) 的集成,以增强驾驶条件识别.
- 开发一个双重模型预测控制 (MPC) 和明确的MPC (eMPC) 框架,具有统一的目标和约束.
- 基于预测的驾驶条件实现双MPC切换逻辑,以提高适应性.
主要成果:
- 拟议的WTL-DMPC EMS显示了FCEV经济表现的显著改善.
- 与现有的基线方法相比,该战略实现了显著的节能能力.
- 在驾驶条件识别中观察到更高的准确性和概括性.
结论:
- WTL-DMPC EMS 为优化 FCEV 能源管理提供了强大而高效的解决方案.
- 集成WT-LSTM和双MPC可以提高各种驾驶场景的适应性.
- 这种方法在提高FCEV的效率和可持续性方面显示出相当大的希望.
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