对插电式混合动力电动汽车集成能源和热管理的全球优化算法的研究
Junyu Jiang1, Yuanbin Yu1, Haitao Min1
1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种自适应电网优化-动态编程 (AGO-DP) 算法,以提高插电式混合动力电动汽车 (PHEV) 的能源效率. 新方法显著减少了计算时间,并提高了最佳能源管理策略的准确性.
科学领域:
- 汽车工程 汽车工程
- 能源系统 能源系统
- 优化算法 优化算法
背景情况:
- 提高插电式混合动力电动汽车 (PHEV) 的能源效率至关重要.
- 综合热能管理战略 (IETMS) 的优化是PHEV性能的关键.
- 现有的动态编程 (DP) 方法面临着全球优化的计算挑战.
研究的目的:
- 为PHEV IETMS提出一个新的自适应电网优化-动态编程 (AGO-DP) 算法.
- 提高能源管理的全球优化速度和准确性.
- 量化 PHEV 中 IETMS 的节能潜力.
主要方法:
- 开发了一个AGO-DP算法,减少状态变量 (SOC,电池温度) 的优化范围.
- 根据实际可行的地区实施适应性电网分配.
- 在各种驾驶条件下模拟并将AGO-DP与传统DP算法进行比较.
主要成果:
- 在驾驶条件上,AGO-DP将州可行的区域减少了30%以上.
- 与DP相比,计算时间减少了33.29-84.67%.
- 全球最佳解决方案准确度提高了0.94-16.85%,发动机和交流控制更高效.
结论:
- AGO-DP提供了一种更快,更准确的方法来优化PHEV能源管理.
- 通过AGO-DP优化的IETMS显示出显著的节能潜力 (3.68-23.74%).
- 与基本能源管理相比,AGO-DP可提高节能潜力0.55至3.26%.
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