通过政策学习对混合动力电动汽车动力电池进行适应性最佳控制
Qinglin Zhu1, Huanli Sun2, Ziliang Zhao1
1College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
Computational intelligence and neuroscience
|June 7, 2023
概括
本研究介绍了一种在线政策学习算法,用于优化对电池充电状态 (SOC) 观察者的控制. 该方法使用自适应神经网络来近似系统不确定性,增强电池管理.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 动力电池充电状态 (SOC) 估计对于电动汽车的性能至关重要.
- 由于非线性系统动态和无法测量的状态,精确的SOC观测具有挑战性.
- 现有的最佳控制方法通常需要复杂的模型知识,并且可能是计算密集的.
研究的目的:
- 开发一种新的在线政策学习算法,以最佳控制动力电池SOC观察员.
- 为非线性动力电池系统设计基于自适应神经网络 (NN) 的最佳控制策略.
- 为了解决电池内部状态的不可测量性,例如电阻电容电压和SOC.
主要方法:
- 对于非线性动力电池系统,使用了第二阶 (RC) 相当电路模型.
- 使用神经网络 (NN) 来近似未知的系统不确定性.
- 设计了一个可变时间增益非线性状态观察器,用于无法测量的参数.
- 开发了一个基于政策学习的在线算法,省略了NN演员,只依赖批评者NN.
主要成果:
- 通过使用NNs成功近似未知的系统不确定性.
- 开发了一种功能性的非线性状态观察器,能够估计无法测量的状态.
- 拟议的在线政策学习算法在实现最佳控制方面表现出有效性.
- 模拟结果验证了开发的最佳控制理论的有效性.
结论:
- 这项研究提出了在线政策学习的突破性应用,用于动力电池SOC观测者的最佳控制.
- 适应性NN方法有效地处理系统不确定性和无法测量的状态.
- 简化政策学习算法 (仅限关键NN) 提供了一种潜在的更有效的方法来实现最佳的控制设计.
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