考虑迟滞特性的卡尔曼滤波和门控循环单元神经网络的锂离子电池SOC联合估计

doi:10.19562/j.chinasae.qcgc.2023.09.017

摘要/Abstract

摘要：

由于迟滞特性的存在，电池管理系统难以准确获得开路电压（OCV）与荷电状态（SOC）之间的状态关系。为有效地运行和管理动力电池，本文研究了考虑迟滞特性的锂离子电池模型，选用带有遗忘因子的递推最小二乘法进行参数在线辨识。提出了一种联合门控循环单元（GRU）神经网络和自适应扩展卡尔曼滤波（AEKF）的SOC估计，分别以AEKF和GRU神经网络的估计结果为模型值和测量值，通过卡尔曼滤波（KF）得到最终的SOC估计结果，并作为下一时刻AEKF的输入。结果表明，常温环境下考虑迟滞特性的模型对端电压预测及联合估计法对SOC估计的均方根误差（RMSE）分别在0.5 mV和0.64%以内；低温及变温环境下端电压预测及SOC估计的RMSE分别在0.9 mV和0.72%以内。考虑迟滞特性的模型及联合估计法具有良好的精度和鲁棒性。

关键词: 锂离子电池, 迟滞特性, 荷电状态, 门控循环单元神经网络, 自适应扩展卡尔曼滤波

Abstract:

Due to the existence of hysteresis characteristics， it is difficult for battery management systems to accurately obtain the state relationship between open circuit voltage （OCV） and state of charge （SOC）. In order to effectively operate and manage the power battery， this paper investigates a lithium-ion battery model that considers the hysteresis characteristics and selects FFRLS for online identification of parameters. A SOC estimation method combining gated recurrent unit （GRU） neural network and adaptive extended Kalman filter （AEKF） is proposed， using the estimated results of the AEKF and GRU neural network as the model and measured values respectively， and the final SOC estimation results are obtained by Kalman filter （KF）， which is used as the input to the AEKF at the next moment. The results show that the root mean square error （RMSE） of the prediction of voltages by models considering hysteresis characteristics and the SOC estimation by the joint estimation method is within 0.5 mV and 0.64% respectively for the ambient temperature environment. The RMSE for terminal voltage prediction and SOC estimation is within 0.9 mV and 0.72% for low and variable temperature environment respectively. The model considering the hysteresis characteristics and joint estimation method have good accuracy and robustness.

Key words: lithium-ion batteries, hysteresis characteristics, state of charge, GRU neural network, adaptive extended Kalman filter

胡明辉,朱广曜,刘长贺,唐国峰. 考虑迟滞特性的卡尔曼滤波和门控循环单元神经网络的锂离子电池SOC联合估计[J]. 汽车工程, 2023, 45(9): 1688-1701.

Minghui Hu,Guangyao Zhu,Changhe Liu,Guofeng Tang. Joint Estimation of State of Charge for Lithium-Ion Battery with Kalman Filtering and Gated Recurrent Unit Neural Networks Considering Hysteresis Characteristics[J]. Automotive Engineering, 2023, 45(9): 1688-1701.

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电池形状	额定电压	额定容量	最大放电电流	截止电压
电池形状	额定电压	额定容量	最大放电电流	充电	放电
软包	3.2 V	20 A·h	60 A	3.65 V	2.5 V

模型	迟滞特性	工况	RMSE/mV	MAE/mV
1阶RC模型	未考虑迟滞特性	IDST	0.18	1.77
	未考虑迟滞特性	IFUDS	0.92	12.19
	考虑迟滞特性	IDST	0.10	1.31
	考虑迟滞特性	IFUDS	0.48	9.66
2阶RC模型	未考虑迟滞特性	IDST	0.15	0.87
	未考虑迟滞特性	IFUDS	0.88	12.11
	考虑迟滞特性	IDST	0.08	0.69
	考虑迟滞特性	IFUDS	0.45	9.64

温度	模型	RMSE/mV	MAE/mV
-5 ℃环境温度	1阶RC模型	0.77	12.27
-5 ℃环境温度	2阶RC模型	0.59	11.15
变环境温度	1阶RC模型	1.07	19.88
变环境温度	2阶RC模型	0.90	18.29

α₀	2.629	α₁	1.499×10^-1
α₂	-1.690×10^-2	α₃	1.079×10^-3
α₄	-4.153×10^-5	α₅	9.991×10^-7
α₆	-1.508×10^-8	α₇	1.385×10^-10
α₈	-7.059×10^-13	α₉	1.526×10^-15

方法	安时积分		GRU		AEKF		联合估计
方法	IDST	IFUDS	IDST	IFUDS	IDST	IFUDS	IDST	IFUDS
RMSE/%	1.814	2.063	1.369	1.333	0.533	1.006	0.369	0.643
MAE/%	3.139	3.576	5.674	6.056	1.223	1.360	1.074	1.019