适用于宽温度范围的锂离子电池SOC估计方法

doi:10.19799/j.cnki.2095-4239.2024.0341

摘要/Abstract

摘要：

精确的荷电状态(SOC)估计是确保动力电池安全稳定运行的关键所在。然而，在实际应用中，环境温度的变化以及噪声干扰等因素使得SOC的精确估计变得困难重重。为了解决这一问题，本文提出一种基于多新息自适应鲁棒无迹卡尔曼滤波(MIARUKF)算法的宽温度范围下锂离子电池SOC多时间尺度联合估计方法，该算法在无迹卡尔曼滤波(UKF)算法的基础上，融合多新息理论、自适应滤波与鲁棒算法。所提算法利用多新息向量对状态估计值进行修正，并对噪声协方差进行及时更新，从而提高SOC的估计精度，通过引入H∞滤波算法来提高该算法的鲁棒性。同时为了降低电池管理系统(BMS)的计算负担，使用UKF算法在宏观时间尺度上在线估计模型参数，采用MIARUKF算法在微观时间尺度上估计电池SOC。最后，在不同SOC初始值、不同温度条件下，对电池SOC的估计结果进行比较和分析，本文所提方法最大绝对误差和平均绝对误差分别为1.05%和0.42%，表明该算法具有较高的精度和较好的鲁棒性。

关键词: 锂离子电池, 荷电状态, 多温度, 多新息自适应鲁棒无迹卡尔曼滤波

Abstract:

Accurate state of charge (SOC) estimation is the key to ensure the safe and stable operation of power batteries. However, in practical applications, the environment factors such as temperature change and noise interference make the accurate estimation of SOC difficult. In order to solve this problem, this paper proposes a joint estimation method of multi-timescales of the SOC of lithium ion batteries in wide temperature range based on the multi-new interest adaptive robust untrace Kalman filter (MIARUKF) algorithm, which integrates multi-new interest theory, adaptive filtering and robust algorithm based on the UKF algorithm. The proposed algorithm uses the multi-interest vector to correct the state estimates and timely update the noise covariance, so as to improve the estimation accuracy of SOC and improve the robustness of the algorithm by introducing the H filtering algorithm. Meanwhile, in order to reduce the computational burden of BMS, the UKF algorithm was used to estimate the model parameters online on the macroscopic time scale, and the MIARUKF algorithm was used to estimate the battery SOC on the microscopic time scale. Finally, the estimation results of battery SOC were compared and analyzed under different initial SOC initial values and temperature conditions, and the maximum and average absolute errors of the proposed method were 1.05% and 0.42%, respectively, indicating the high accuracy and good robustness.

Key words: lithium-ion battery, state of charge (SOC), multiple temperatures, MIARUKF

中图分类号:

TM 912

胡雪峰, 常先雷, 刘肖肖, 徐威, 张文彬. 适用于宽温度范围的锂离子电池SOC估计方法[J]. 储能科学与技术, 2024, 13(9): 2983-2994.

Xuefeng HU, Xianlei CHANG, Xiaoxiao LIU, Wei XU, Wenbin ZHANG. SOC estimation of lithium-ion batteries under multiple temperatures conditions based on MIARUKF algorithm[J]. Energy Storage Science and Technology, 2024, 13(9): 2983-2994.

图/表 11

图1

图2

图3

图4

表1

图5

图6

表2

图7

图8

图9

参考文献 19

1	LIU Z, DANG X J, JING B Q, et al. A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter[J]. Electric Power Systems Research, 2019, 177: 105951. DOI: 10.1016/j.epsr.2019.105951.
2	CHEN K, ZHOU S Y, LIU K, et al. State of charge estimation for lithium-ion battery based on whale optimization algorithm and multi-kernel relevance vector machine[J]. 2023, 158(10): 104110. DOI: 10.1063/5.0139376.
3	ZHOU W L, ZHENG Y P, PAN Z J, et al. Review on the battery model and SOC estimation method[J]. Processes, 2021, 9(9): 1685. DOI: 10.3390/pr9091685.
4	LEONORI S, BALDINI L, RIZZI A, et al. A physically inspired equivalent neural network circuit model for SoC estimation of electrochemical cells[J]. Energies, 2021, 14(21): 7386. DOI: 10.3390/en14217386.
5	BUCHICCHIO E, DE ANGELIS A, SANTONI F, et al. Battery SOC estimation from EIS data based on machine learning and equivalent circuit model[J]. Energy, 2023, 283: 128461. DOI: 10.1016/j.energy.2023.128461.
6	李伟, 刘伟嵬, 邓业林. 基于扩展卡尔曼滤波的锂离子电池荷电状态估计[J]. 中国机械工程, 2020, 31(3): 321-327, 343. DOI: 10.3969/j.issn.1004-132X.2020.03.010.
	LI W, LIU W W, DENG Y L. SOC estimation for lithium-ion batteries based on EKF[J]. China Mechanical Engineering, 2020, 31(3): 321-327, 343. DOI: 10.3969/j.issn.1004-132X.2020.03.010.
7	HIDALGO-REYES J I, GÓMEZ-AGUILAR J F, ALVARADO-MARTÍNEZ V M, et al. Battery state-of-charge estimation using fractional extended Kalman filter with Mittag-Leffler memory[J]. Alexandria Engineering Journal, 2020, 59(4): 1919-1929. DOI: 10.1016/j.aej.2019.12.006.
8	安治国, 田茂飞, 赵琳, 等. 基于自适应无迹卡尔曼滤波的锂电池SOC估计[J]. 储能科学与技术, 2019, 8(5): 856-861. DOI: 10.12028/j.issn.2095-4239.2019.0113.
	AN Z G, TIAN M F, ZHAO L, et al. SOC estimation of lithium battery based on adaptive untracked Kalman filter[J]. Energy Storage Science and Technology, 2019, 8(5): 856-861. DOI: 10.12028/j.issn.2095-4239.2019.0113.
9	ZHENG L F, ZHU J G, WANG G X, et al. Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter[J]. Energy, 2018, 158: 1028-1037. DOI: 10.1016/j.energy.2018.06.113.
10	WANG S L, JIA X Y, TAKYI-ANINAKWA P, et al. Review—Optimized particle filtering strategies for high-accuracy state of charge estimation of LIBs[J]. Journal of the Electrochemical Society, 2023, 170(5): 050514. DOI: 10.1149/1945-7111/acd148.
11	WANG K, FENG X, PANG J B, et al. State of charge (SOC) estimation of lithium-ion battery based on adaptive square root unscented Kalman filter[J]. International Journal of Electrochemical Science, 2020, 15(9): 9499-9516. DOI: 10.20964/2020.09.84.
12	GUO F, HU G D, XIANG S, et al. A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters[J]. Energy, 2019, 178: 79-88. DOI: 10.1016/j.energy.2019.04.126.
13	MENG J H, STROE D I, RICCO M, et al. A simplified model-based state-of-charge estimation approach for lithium-ion battery with dynamic linear model[J]. IEEE Transactions on Industrial Electronics, 2019, 66(10): 7717-7727. DOI: 10.1109/TIE.2018. 2880668.
14	陈德海, 王超, 朱正坤, 等. 交互多模型无迹卡尔曼滤波算法预测锂电池SOC[J]. 储能科学与技术, 2020, 9(1): 257-265. DOI: 10.19799/j.cnki.2095-4239.2019.0207.
	CHEN D H, WANG C, ZHU Z K, et al. Lithium battery state-of-charge estimation based on interactive multimodel unscented Kalman filter Algorithm[J]. Energy Storage Science and Technology, 2020, 9(1): 257-265. DOI: 10.19799/j.cnki.2095-4239.2019.0207.
15	HE H W, XIONG R, FAN J X. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach[J]. Energies, 2011, 4(4): 582-598. DOI: 10.3390/en4040582.
16	LIANG Y W, WANG S L, FAN Y C, et al. An error covariance correction-adaptive extended Kalman filter based on piecewise forgetting factor recursive least squares method for the state-of-charge estimation of lithium-ion batteries[J]. Journal of Energy Storage, 2023, 68: 107629. DOI: 10.1016/j.est.2023.107629.
17	FENG S, LI X G, ZHANG S, et al. A review: State estimation based on hybrid models of Kalman filter and neural network[J]. Systems Science & Control Engineering, 2023, 11(1). DOI: 10.1080/21642583.2023.2173682.
18	CHEN Z G, ZHOU J X, ZHOU F, et al. State-of-charge estimation of lithium-ion batteries based on improved H infinity filter algorithm and its novel equalization method[J]. Journal of Cleaner Production, 2021, 290: 125180. DOI: 10.1016/j.jclepro. 2020.125180.
19	DING F, WANG X H, MAO L, et al. Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering[J]. Digital Signal Processing, 2017, 62: 211-223. DOI: 10.1016/j.dsp. 2016.11.010.

规格	ICR18650-22P
额定电压	3.62 V
额定容量	2.2 Ah
充电截止电压	4.2 V
放电截止电压	2.75 V

温度	EKF		UKF		RUKF		ARUKF		MIARUKF		MIARUKF+UKF
温度	AAE	MAE	AAE	MAE	AAE	MAE	AAE	MAE	AAE	MAE	AAE	MAE
-10 ℃	2.68%	5.13%	1.99%	4.93%	1.77%	2.44%	1.29%	2.22%	0.93%	1.76%	0.42%	1.05%
25 ℃	1.73%	3.40%	1.68%	2.85%	1.47%	2.12%	1.20%	1.91%	0.53%	1.12%	0.25%	0.70%
40 ℃	1.60%	3.55%	1.47%	2.63%	1.43%	1.90%	1.04%	1.70%	0.60%	1.10%	0.23%	0.59%

[1]	孙中麟, 李嘉波, 田迪, 王志璇, 邢晓静. 基于COA-LSTM和VMD的锂离子电池剩余寿命预测[J]. 储能科学与技术, 2024, 13(9): 3254-3265.
[2]	陆继忠, 彭思敏, 李晓宇. 基于多特征量分析和LSTM-XGBoost模型的锂离子电池SOH估计方法[J]. 储能科学与技术, 2024, 13(9): 2972-2982.
[3]	沈思远, 刘亚坤, 罗栋煌, 李雨珺, 郝伟. 储能锂离子电池模组暂态过电压防护设计与电路研发[J]. 储能科学与技术, 2024, 13(9): 3277-3286.
[4]	陈媛, 章思源, 蔡宇晶, 黄小贺, 刘炎忠. 融合多项式特征扩展与CNN-Transformer模型的锂电池SOH估计[J]. 储能科学与技术, 2024, 13(9): 2995-3005.
[5]	邢瑞鹤, 翁素婷, 李叶晶, 张佳怡, 张浩, 王雪锋. AI辅助电池材料表征与数据分析[J]. 储能科学与技术, 2024, 13(9): 2839-2863.
[6]	何宁, 杨芳芳. 考虑能量和温度特征的锂离子电池早期寿命预测[J]. 储能科学与技术, 2024, 13(9): 3016-3029.
[7]	管鸿盛, 钱诚, 孙博, 任羿. 贫数据条件下锂离子电池容量退化轨迹预测方法[J]. 储能科学与技术, 2024, 13(9): 3084-3093.
[8]	柯学, 洪华伟, 郑鹏, 李智诚, 范培潇, 杨军, 郭宇铮, 蒯春光. 基于多时间尺度建模自动特征提取和通道注意力机制的锂离子电池健康状态估计[J]. 储能科学与技术, 2024, 13(9): 3059-3071.
[9]	田成文, 孙丙香, 赵鑫泽, 付智城, 马仕昌, 赵博, 张旭博. 基于数据驱动的锂离子电池快速寿命预测[J]. 储能科学与技术, 2024, 13(9): 3103-3111.
[10]	李清波, 张懋慧, 罗英, 吕桃林, 解晶莹. 基于等效电路模型融合电化学原理的锂离子电池荷电状态估计[J]. 储能科学与技术, 2024, 13(9): 3072-3083.
[11]	何智峰, 陶远哲, 胡泳钢, 王其聪, 杨勇. 机器学习强化的电化学阻抗谱技术及其在锂离子电池研究中的应用[J]. 储能科学与技术, 2024, 13(9): 2933-2951.
[12]	孙丙香, 杨鑫, 周兴振, 马仕昌, 王志豪, 张维戈. 基于简化阻抗模型和比较元启发式算法的锂离子电池参数辨识方法[J]. 储能科学与技术, 2024, 13(9): 2952-2962.
[13]	黄煜峰, 梁焕超, 许磊. 基于卡尔曼滤波算法优化Transformer模型的锂离子电池健康状态预测方法[J]. 储能科学与技术, 2024, 13(8): 2791-2802.
[14]	王志勇, 蔡君瑶, 佘英奇, 钟树林, 潘康华. 氮杂环导电高分子改性锂离子电池石墨负极材料[J]. 储能科学与技术, 2024, 13(8): 2511-2518.
[15]	陈峥, 杨博, 赵志刚, 申江卫, 肖仁鑫, 夏雪磊. 考虑锂电池温度和老化的荷电状态估算[J]. 储能科学与技术, 2024, 13(8): 2813-2822.