基于片段充电数据和DUKF的锂电池健康状态实时评估

doi:10.13229/j.cnki.jdxbgxb.20240871

摘要/Abstract

摘要：

精确实时地评估电池健康状况是电动汽车电池管理系统的核心，本文提出了一种新的电池全充时间估计模型。首先，利用无迹卡尔曼滤波处理非线性问题时的高估计精度，设计了精度进一步提升的双无迹卡尔曼滤波预测-校正框架，能精确估计锂电池当下的全充时间。在此框架下，利用高斯过程回归和支持向量回归预测结果做线性加权学习了无迹卡尔曼滤波的量测方程。实验结果表明：本文提出的框架具有高精度和实时性，估计180次全充时间的平均相对误差为0.001 6。与EKF和DEKF算法相比，平均相对误差分别减小了98.87%和98.15%。

关键词: 电池健康状态, 片段数据, 双无迹卡尔曼滤波, 高斯过程回归, 支持向量回归

Abstract:

Accurate and real-time evaluation of the battery's state of health is the core of the battery management system in electric vehicles. This paper proposes a novel model for estimating the full charging time of lithium batteries. Firstly， utilizing the high estimation accuracy of the Unscented Kalman Filter for nonlinear problems， a dual Unscented Kalman Filter prediction correction framework with further improved accuracy is designed， which can accurately estimate the current full charge time of lithium batteries. Under this framework， the measurement equation of the Unscented Kalman Filter is linearly weighted using Gaussian Process Regression and Support Vector Regression prediction results. The experimental results show that the framework proposes in this paper has high accuracy and real-time performance， with an average relative error of 0.001 6 for estimating 180 full charge times. Compared with the EKF and DEKF based algorithms， the average relative error has reduced by 98.87% and 98.15%， respectively.

Key words: state of health, segment data, dual unscented kalman filter, gaussian process regression, support vector regression

中图分类号:

TM911

宋显华,孙文璐,谢巍. 基于片段充电数据和DUKF的锂电池健康状态实时评估[J]. 吉林大学学报(工学版), 2026, 56(2): 355-367.

Xian-hua SONG,Wen-lu SUN,Wei XIE. Real-time estimation of Li-ion battery state of health based on segment charging data and DUKF[J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(2): 355-367.

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