电子测量与仪器学报

高峰,贾建芳,元淑芳,李孟威.基于 GRU-UKF 的锂离子电池 SOC 估计方法研究[J].电子测量与仪器学报,2022,36(11):160-169

基于 GRU-UKF 的锂离子电池 SOC 估计方法研究

Research on SOC estimation method of lithium-ion battery based on GRU-UKF

DOI：

英文关键词:lithium-ion battery state of charge (SOC) GRU UKF

基金项目:国家自然科学基金（72071183）、山西省回国留学人员科研项目(2020 114)、高能电池材料与器件山西省重点实验室开放基金（2022HPBMD01002)项目资助

作者	单位
高峰	1. 中北大学电气控制工程学院
贾建芳	1. 中北大学电气控制工程学院，2. 高能电池材料与器件山西省重点实验室
元淑芳	1. 中北大学电气控制工程学院
李孟威	1. 中北大学电气控制工程学院

Author	Institution
Gao Feng	1. School of Electrical and Control Engineering, North University of China
Jia Jianfang	1. School of Electrical and Control Engineering, North University of China，2. Shanxi Key Laboratory of High Performance Battery Materials and Devices, North University of China
Yuan Shufang	1. School of Electrical and Control Engineering, North University of China
Li Mengwei	1. School of Electrical and Control Engineering, North University of China

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中文摘要:

精确估计锂离子电池荷电状态(SOC)是电池管理系统的关键技术之一,直接影响着动力锂电池组的使用效率和安全性。锂离子电池特性复杂,其 SOC 无法直接测量,且受电流、温度等因素的影响较大。为此,提出了一种基于门控循环单元 (GRU)神经网络与无迹卡尔曼滤波(UKF)相结合的组合算法。该方法利用 GRU 网络获得可测量的电流、电压、温度与锂电池 SOC 之间的非线性关系,并以此作为 UKF 的观测方程。然后,通过 UKF 估计 SOC 值以提高算法的估计精度。实验结果表明, 在不同温度以及不同的工况下,本文所提方法的均方根误差(RMSE)和平均绝对误差(MAE)分别小于 0. 51%和 0. 46%,均能提高 SOC 的估计精度。

英文摘要:

Accurate estimation of the state of charge ( SOC) of lithium-ion batteries is one of the key technologies in the battery management system, which has a vital impact on the service efficiency and safety of power battery pack. Lithium-ion batteries have complicated characteristics and SOC cannot be directly measured which are greatly affected by the current and temperature. Therefore, combining a gated recurrent unit (GRU) neural network with an unscented Kalman filter (UKF) algorithm is presented. The method uses GRU neural network to obtain the nonlinear relationship between the SOC and measurements, including the current, voltage, temperature. The relationship is used as the observation equation of UKF, and the SOC is estimated by the UKF to improve the accuracy and stability of estimation algorithm. Experimental results show that under different temperatures and different working conditions the root mean square error and the mean absolute error of the SOC estimate are less than 0. 51% and 0. 46%, respectively, which can improve the accuracy of SOC estimation.

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