通过系统扩展的卡尔曼波器调节,对具有歇斯底里的离子电池进行先进状态估计
J Knox1,2, M Blyth3,4, A Hales5,6
1Faculty of Engineering, University of Bristol, Bristol, BS8 1TR, UK. wa20553@bristol.ac.uk.
Scientific reports
|May 30, 2024
概括
精确的电动汽车电池充电 (充电状态或SOC) 估计至关重要. 这项研究引入了改进的电池模型和自适应过器,显著减少了SOC估计误差的85%.
科学领域:
- 电化学 电化学 电化学
- 电池管理系统 电池管理系统
- 控制理论 控制理论
背景情况:
- 准确的充电状态 (SOC) 估计对于电动汽车 (EV) 部署至关重要.
- 直接测量SOC是不可行的,需要可靠的估计方法.
- 现有的电池模型和状态估计器需要改进以提高准确性.
研究的目的:
- 开发和验证一种歇斯底里性降级电池模型,以提高SOC预测.
- 实施和评估用于SOC估计的自适应扩展卡尔曼波器估计器.
- 为了提高电动汽车电池SOC估计的准确性和稳定性.
主要方法:
- 开发了一种新的参数化框架,以捕捉减少顺序模型中的电池歇斯底里效应.
- 歇斯底里模型对三个独立的NMC811离子电池进行了参数化.
- 适应性扩展卡尔曼波器与适应性协差矩阵被用于SOC估计.
主要成果:
- 提出的歇斯底里电池模型在18小时的汽车驱动周期中将电压RMS误差降低了50%.
- 适应性波器参数化方案显示了显著的价值.
- 与行业标准方法相比,SOC估计误差减少了85%.
结论:
- 歇斯底里降低级电池模型和自适应扩展卡尔曼过器对于准确的SOC预测非常有效.
- 拟议的参数化框架增强了对hysteresis的标准化方法.
- 扩展卡尔曼波器中的自适应共变矩阵产生了更高的SOC估计准确性.
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