基于DTV-IGPR模型的锂离子电池SOH估计方法

被引：18

作者：

王萍

彭香园

程泽

机构：

[1] 天津大学电气自动化与信息工程学院

来源：

汽车工程 | 2021年 / 43卷 / 11期

关键词：

锂离子电池; 健康状态; 健康特征; 温度; 高斯过程回归;

D O I：

10.19562/j.chinasae.qcgc.2021.11.017

中图分类号：

TM912 [蓄电池];

学科分类号：

080802 [电力系统及其自动化];

摘要：

锂离子电池的健康状态(state of health,SOH)是保障电动汽车安全可靠运行的关键因素。现有的SOH估计方法通常忽略容量衰退过程中能够表征电池老化的温度信息。鉴于此,本文中提出一种基于电池表面温度的差分温度伏安(differential temperature voltammetry,DTV)曲线的获取方法和一种滑动平均(moving average,MA)与卡尔曼滤波(Kalman filtering,KF)结合的滤波方法以提取健康特征。同时利用组合核函数改进了传统高斯过程回归(Gaussian process regression,GPR)算法以拟合电池容量全局衰退和局部波动这两种趋势,从而建立DTV-IGPR电池老化模型进行SOH估计。在两种不同环境温度下获取的牛津数据集和NASA数据集中进行单电池和多电池验证,结果均表明,所提方法具有较高的SOH估计精度和鲁棒性。

引用

页码：1710 / 1719

页数：10

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