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《工程(英文)》 >> 2021年 第7卷 第10期 doi: 10.1016/j.eng.2020.10.022

基于多模型融合驱动的锂离子动力电池荷电状态和容量联合估计研究

a Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
b Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC 3122, Australia

收稿日期: 2020-06-01 修回日期: 2020-08-19 录用日期: 2021-10-25 发布日期: 2021-02-09

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摘要

锂离子动力电池(LIB)已成为各种电动载运工具的首选电源系统,包括电动汽车、电动轮船、电动火车和电动飞机。在全气候全寿命周期运行的电动载运工具中,锂离子电池的能量管理需要实时准确估计电池的荷电状态(SOC)和容量。本文提出了一种多阶段模型融合算法可协同估计SOC和容量。首先,基于正态分布假设,利用模型在不同老化状态下的残差均值和方差计算权重,建立参数稳定的融合模型。其次,将具有预测性的微分增益引入比例-积分观测器(PIO)以提高收敛速度。再次,将多阶段融合模型与比例-积分-微分观测器(PIDO)结合,建立了一种融合算法,可实现复杂应用环境下SOC和容量的协同估计。然后,讨论了融合算法的收敛性和抗噪性能。最后,搭建硬件在环平台,验证了融合算法的性能。不同老化状态和温度下的验证结果表明,融合算法可以实现SOC和容量的高精度协同估计,误差分别在2%和3.3%以内。

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