基于迁移模型的老化锂离子电池SOC估计

doi:10.19799/j.cnki.2095-4239.2020.0288

摘要/Abstract

摘要：

在锂离子电池荷电状态(SOC)估计过程中，由于电池老化引起的电池可用容量衰退和内部参数变化会对SOC估计结果造成很大影响。针对这一问题，本文将电池老化视为影响模型与SOC估计精度的不确定因素，提出了一种基于迁移模型的老化锂离子电池SOC估计新方法。首先以电池初始状态下的二阶RC等效电池模型为电池初始模型，利用递推最小二乘法(RLS)及多项式拟合法提取初始模型参数与SOC的函数关系式，并将函数关系式进行线性迁移得到迁移模型状态方程，再采用风险最小化粒子滤波算法(RSPF)在电池实际运行中更新迁移模型的迁移因子，最后结合低通滤波器实现SOC的精确估计。通过4组不同老化程度下的城市道路循环工况(UDDS)数据对迁移模型算法进行了验证，并与扩展卡尔曼滤波(EKF)和自适应扩展卡尔曼滤波(AEKF)两种算法进行了对比。实验结果表明，在不同的老化状态下，本文所提出的方法具有更高的精确性，估计得到的SOC均方根误差(RMSE)始终稳定在1.04%以内，验证了所提方法的有效性。本研究有助于推动迁移模型在老化锂离子电池SOC估计中的应用，对电动汽车全生命周期内使用过程中SOC的估计具有一定指导和参考意义。

关键词: 老化锂离子电池, 荷电状态, 迁移模型, 风险最小化粒子滤波

Abstract:

In the process of estimating the state of charge (SOC) for lithium-ion batteries, the estimation results can be significantly influenced by a reduction of available capacity and changes in the internal parameters of aging batteries. To address this problem, this paper proposes a novel method for estimating the SOC of aging lithium-ion batteries based on the migration model, by regarding the aging of batteries as an uncertain factor that affects the estimation of the SOC. First, with the second-order resistance-capacitance (RC) equivalent battery model at the original state as an initial battery model, recursive least squares (RLS) and polynomial fitting are employed to extract the relationship between parameters of the initial model and the SOC, which is then linearly migrated to obtain a state equation of the migration model. Second, migration factors of the model are updated in the actual running of batteries with the risk sensitive particle filter algorithm (RSPF). Finally, a precise estimation of the SOC is attained in combination with low-pass filters. Based on four sets of Urban Dynamometer Driving Schedule (UDDS) data at different degrees of aging, the migration model algorithm was verified and compared with the extended Kalman filter (EKF) and the adaptive extended Kalman filter (AEKF) algorithms. The experimental studies indicated that the model proposed in this paper is precise over a range battery ages and is effective, with a root-mean-square error (RMSE) of the SOC calculation less than 1.04%. The study can help to promote the application of the migration model in the estimation of the SOC of aging lithium-ion batteries, and can provide guidance and a reference for the estimation of the SOC of electric vehicle batteries throughout their lifecycles.

Key words: aging lithium-ion batteries, state of charge, migration model, risk sensitive particle filter

中图分类号:

TM 911

陈　峥, 赵广达, 沈世全, 舒　星, 申江卫. 基于迁移模型的老化锂离子电池SOC估计[J]. 储能科学与技术, 2021, 10(1): 326-334.

Zheng CHEN, Guangda ZHAO, Shiquan SHEN, Xing SHU, Jiangwei SHEN. SOC estimation of aging lithium-ion battery based on a migration model[J]. Energy Storage Science and Technology, 2021, 10(1): 326-334.

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SOH/%	RMSE/mV	MAE/mV	MAPE/mV
100	13.3	3.3	18.5
99	13.8	3.2	14
93	13.8	3.2	17.2
85	14.2	3.9	26.8

SOH/%	RMSE/%	MAE/%	MAPE/%
100	1.04	0.82	3.33
99	0.97	0.80	2.30
93	0.64	0.49	2.57
85	0.95	0.72	3.04

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