考虑锂电池温度和老化的荷电状态估算

doi:10.19799/j.cnki.2095-4239.2024.0141

摘要/Abstract

摘要：

针对锂离子动力电池工作环境复杂且电池老化导致内部参数辨识精度低与荷电状态估计误差大的难题，本文提出了一种多新息最小二乘法与平方根容积卡尔曼滤波估计锂离子电池荷电状态的联合算法，实现动力电池在全服役周期内多温度条件下的状态估算。首先，为解决传统最小二乘法对历史数据利用率低的问题，在最小二乘法中融入多新息理论，采用一阶RC等效电路建立电池模型，利用多新息最小二乘法对电池内部参数进行参数辨识；然后，采用平方根容积卡尔曼滤波估算电池SOC；最后，通过多温度全寿命的电池实验数据对本文所提算法进行验证，并且与扩展卡尔曼滤波、容积卡尔曼滤波算法进行对比，证明本文提出算法的有效性。实验结果表明：本文提出的多新息最小二乘-平方根容积卡尔曼滤波算法在多温度全寿命条件下，能够准确反映动力电池内部参数和精确估算电池SOC，电压平均绝对误差不超过40 mV，SOC的估算误差控制在2%范围内。

关键词: 锂电池, 多新息最小二乘法, 平方根容积卡尔曼滤波, 多温度, 荷电状态

Abstract:

To solve the low internal parameter identification accuracy and large charge state estimation error caused by complex working environments and the aging of lithium-ion power batteries, this study proposed a combined algorithm of a multi-innovation least squares method and square root cubature Kalman filter to estimate the charge state of lithium-ion batteries, and realized the state estimation of power batteries under multitemperature conditions during the its lifetime. First, to solve the low utilization rate of historical data by the traditional least squares method, the multi-innovation theory was incorporated into the least squares method, a first-order RC equivalent circuit was used to establish the battery model, and the internal parameters of the battery were identified by the multi-innovation least squares method. Subsequently, the SOC was estimated by the square root cubature Kalman filter. Finally, the effectiveness of the proposed algorithm was verified by comparing the experimental data of the multitemperature battery with that obtained using the extended Kalman filter and cubature Kalman filter algorithms. The experimental results showed that the proposed algorithm could accurately reflect the internal parameters of the power battery and estimate the SOC of the battery under the condition of the multitemperature lifetime. The average absolute voltage error was less than 40 mV, and the SOC estimation error was controlled within 2%.

Key words: lithium-ion battery, multi-innovation least square algorithm, square root cubature Kalman filter, multi-temperature, state of charge

中图分类号:

TM 912

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.

Zheng CHEN, Bo YANG, Zhigang ZHAO, Jiangwei SHEN, Renxin XIAO, Xuelei XIA. State of charge estimation considering lithium battery temperature and aging[J]. Energy Storage Science and Technology, 2024, 13(8): 2813-2822.

图/表 11

图1

图2

图3

表1

图4

图5

图6

图7

表2

图8

表3

参考文献 25

1	赵轩, 李美莹, 余强, 等. 电动汽车动力锂电池状态估计综述[J]. 中国公路学报, 2023, 36(6): 254-283. DOI: 10.19721/j.cnki.1001-7372.2023.06.021.
	ZHAO X, LI M Y, YU Q, et al. State estimation of power lithium batteries for electric vehicles: A review[J]. China Journal of Highway and Transport, 2023, 36(6): 254-283. DOI: 10.19721/j.cnki.1001-7372.2023.06.021.
2	陈峥, 陈洋, 申江卫, 等. 基于优化支持向量回归算法的锂离子电池可用容量估计[J]. 储能科学与技术, 2023, 12(10): 3203-3213. DOI: 10.19799/j.cnki.2095-4239.2023.0387.
	CHEN Z, CHEN Y, SHEN J W, et al. Available capacity estimation of lithium-ion batteriesbased on the optimized support vector regression algorithm[J]. Energy Storage Science and Technology, 2023, 12(10): 3203-3213. DOI: 10.19799/j.cnki.2095-4239.2023.0387.
3	舒星, 刘永刚, 申江卫, 等. 基于改进最小二乘支持向量机与Box-Cox变换的锂离子电池容量预测[J]. 机械工程学报, 2021, 57(14): 118-128. DOI: 10.3901/JME.2021.14.118.
	SHU X, LIU Y G, SHEN J W, et al. Capacity prediction for lithium-ion batteries based on improved least squares support vector machine and box-cox transformation[J]. Journal of Mechanical Engineering, 2021, 57(14): 118-128. DOI: 10.3901/JME.2021.14.118.
4	黄鹏超, 鄂加强. 基于双自适应卡尔曼滤波的锂电池状态估算[J]. 储能科学与技术, 2022, 11(2): 660-666. DOI: 10.19799/j.cnki.2095-4239.2021.0411.
	HUANG P C, E J Q. State estimation of lithium-ion battery based on dual adaptive Kalman filter[J]. Energy Storage Science and Technology, 2022, 11(2): 660-666. DOI: 10.19799/j.cnki.2095-4239.2021.0411.
5	袁照凯, 范秋华, 王冬青, 等. 基于MIAEKF的多温度下锂电池SOC估计[J]. 储能科学与技术, 2024, 13(2): 680-690. DOI:10.19799/j.cnki.2095-4239.2023.0533.
	YUAN Z K, FAN Q H, WANG D Q, et al. State of charge estimation for lithium-ion batteries under multiple temperatures based on the MIAEK algorithm[J]. Energy Storage Science and Technology, 2024, 13(2): 680-690. DOI:10.19799/j.cnki.2095-4239.2023.0533.
6	SUN D M, YU X L, WANG C M, et al. State of charge estimation for lithium-ion battery based on an intelligent adaptive extended Kalman filter with improved noise estimator[J]. Energy, 2021, 214: 119025. DOI: 10.1016/j.energy.2020.119025.
7	李宁, 何复兴, 马文涛, 等. 基于经验模态分解的门控循环单元神经网络的锂离子电池荷电状态估计[J]. 电工技术学报, 2022, 37(17): 4528-4536. DOI: 10.19595/j.cnki.1000-6753.tces.211069.
	LI N, HE F X, MA W T, et al. State-of-charge estimation of lithium-ion battery based on gated recurrent unit using empirical mode decomposition[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4528-4536. DOI: 10.19595/j.cnki.1000-6753.tces. 211069.
8	刘素贞, 袁路航, 张闯, 等. 基于超声时域特征及随机森林的磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2022, 37(22): 5872-5885. DOI: 10.19595/j.cnki.1000-6753.tces.211585.
	LIU S Z, YUAN L H, ZHANG C, et al. State of charge estimation of LiFeO₄ batteries based on time domain features of ultrasonic waves and random forest[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5872-5885. DOI: 10.19595/j.cnki.1000-6753.tces.211585.
9	LI X Y, SHU X, SHEN J W, et al. An on-board remaining useful life estimation algorithm for lithium-ion batteries of electric vehicles[J]. Energies, 2017, 10(5): 691. DOI: 10.3390/en10050691.
10	WANG Y H, HUANG H H, WANG H X. A new method for fast state of charge estimation using retired battery parameters[J]. Journal of Energy Storage, 2022, 55: 105621. DOI: 10.1016/j.est.2022.105621.
11	HONG J C, WANG Z P, CHEN W, et al. Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles[J]. Journal of Energy Storage, 2020, 30: 101459. DOI: 10.1016/j.est.2020.101459.
12	MAO X J, SONG S J, DING F. Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with Levy flight[J]. Journal of Energy Storage, 2022, 49: 104139. DOI: 10.1016/j.est.2022.104139.
13	KANG L W, ZHAO X, MA J. A new neural network model for the state-of-charge estimation in the battery degradation process[J]. Applied Energy, 2014, 121: 20-27. DOI: 10.1016/j.apenergy.2014.01.066.
14	SHEN J W, XIONG J, SHU X, et al. State of charge estimation framework for lithium‐ion batteries based on square root cubature Kalman filter under wide operation temperature range[J]. International Journal of Energy Research, 2020, 45: 5586-5601. DOI: 10.1002/er.6186.
15	HOSSAIN M, HAQUE M E, ARIF M T. Online model parameter and state of charge estimation of Li-ion battery using unscented Kalman filter considering effects of temperatures and C-rates[J]. IEEE Transactions on Energy Conversion, 2022, 37(4): 2498-2511. DOI: 10.1109/TEC.2022.3178600.
16	杨帆, 和嘉睿, 陆鸣, 等. 基于BP-UKF算法的锂离子电池SOC估计[J]. 储能科学与技术, 2023, 12(2): 552-559. DOI: 10.19799/j.cnki.2095-4239.2022.0574.
	YANG F, HE J R, LU M, et al. SOC estimation of lithium-ion batteries based on BP-UKF algorithm[J]. Energy Storage Science and Technology, 2023, 12(2): 552-559. DOI: 10.19799/j.cnki.2095-4239.2022.0574.
17	熊然, 王顺利, 于春梅, 等. 基于Thevenin模型和改进扩展卡尔曼的特种机器人锂离子电池SOC估算方法[J]. 储能科学与技术, 2021, 10(2): 695-704. DOI: 10.19799/j.cnki.2095-4239.2020.0397.
	XIONG R, WANG S L, YU C M, et al. An estimation method for lithium-ion battery SOC of special robots based on Thevenin model and improved extended Kalman[J]. Energy Storage Science and Technology, 2021, 10(2): 695-704. DOI: 10.19799/j.cnki.2095-4239.2020.0397.
18	XU Y D, HU M H, ZHOU A J, et al. State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter[J]. Applied Mathematical Modelling, 2020, 77: 1255-1272. DOI: 10.1016/j.apm.2019.09.011.
19	郑涛, 张里, 侯杨成, 等. 基于自适应CKF的老化锂电池SOC估计[J]. 储能科学与技术, 2020, 9(4): 1193-1199. DOI: 10.19799/j.cnki.2095-4239.2020.0005.
	ZHENG T, ZHANG L, HOU Y C, et al. SOC estimation of aging lithium battery based on adaptive CKF[J]. Energy Storage Science and Technology, 2020, 9(4): 1193-1199. DOI: 10.19799/j.cnki.2095-4239.2020.0005.
20	李建林, 肖珩. 锂离子电池建模现状综述[J]. 储能科学与技术, 2022, 11(2): 697-703. DOI: 10.19799/j.cnki.2095-4239.2021.0450.
	LI J L, XIAO H. Review on modeling of lithium-ion battery[J]. Energy Storage Science and Technology, 2022, 11(2): 697-703. DOI: 10.19799/j.cnki.2095-4239.2021.0450.
21	陈峥, 赵广达, 沈世全, 等. 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. DOI: 10.19799/j.cnki.2095-4239.2020.0288.
	CHEN Z, ZHAO G D, SHEN S Q, et al. SOC estimation of aging lithium-ion battery based on a migration model[J]. Energy Storage Science and Technology, 2021, 10(1): 326-334. DOI: 10.19799/j.cnki.2095-4239.2020.0288.
22	林鹏, 刘涛, 金鹏, 等. 基于多新息辨识算法的锂离子电池等效电路模型参数辨识[J]. 储能科学与技术, 2023, 12(10): 3155-3169. DOI: 10.19799/j.cnki.2095-4239.2023.0358.
	LIN P, LIU T, JIN P, et al. Identification of lithium-ion battery equivalent circuit model parameters based on the multi-innovation identification algorithm[J]. Energy Storage Science and Technology, 2023, 12(10): 3155-3169. DOI: 10.19799/j.cnki.2095-4239.2023.0358.
23	朱奕楠, 吕桃林, 赵芝芸, 等. 基于并行卡尔曼滤波器的锂离子电池荷电状态估计[J]. 储能科学与技术, 2021, 10(6): 2352-2362. DOI: 10.19799/j.cnki.2095-4239.2021.0169.
	ZHU Y N, LÜ T L, ZHAO Z Y, et al. State of charge estimation of lithium ion battery based on parallel Kalman filter[J]. Energy Storage Science and Technology, 2021, 10(6): 2352-2362. DOI: 10.19799/j.cnki.2095-4239.2021.0169.
24	李晓涵, 孙磊, 马勇, 等. 基于Sage-Husa EKF算法的锂离子电池能量状态估计[J]. 储能科学与技术, 2022, 11(11): 3603-3612. DOI: 10.19799/j.cnki.2095-4239.2022.0277.
	LI X H, SUN L, MA Y, et al. Energy state estimation of lithium-ion batteries based on Sage-Husa EKF algorithm[J]. Energy Storage Science and Technology, 2022, 11(11): 3603-3612. DOI: 10.19799/j.cnki.2095-4239.2022.0277.
25	CHEN Z, XIAO J P, SHU X, et al. Model-based adaptive joint estimation of the state of charge and capacity for lithium-ion batteries in their entire lifespan[J]. Energies, 2020, 13(6): 1410. DOI: 10.3390/en13061410.

型号	三元锂电池
额定容量	4 Ah
允许电压范围	2.75～4.2 V
最大充电倍率	3 C
允许放电温度	-20～60 ℃

算法	-10 ℃			30 ℃			60 ℃
算法	MAX /%	MAE /%	RMSE/%	MAX/%	MAE /%	RMSE/%	MAX /%	MAE /%	RMSE/%
EKF	1.77	1.10	1.66	2.28	1.57	1.87	5.21	1.09	1.45
CKF	2.93	1.22	1.65	2.10	0.94	1.29	4.91	1.62	2.34
SRCKF	1.55	0.81	1.12	1.19	0.42	0.67	1.42	0.42	0.77

算法	SOH=96.7%			SOH=93.1%			SOH=85.1%
算法	MAX /%	MAE /%	RMSE /%	MAX/%	MAE /%	RMSE/%	MAX /%	MAE/%	RMSE /%
EKF	2.05	1.14	1.88	2.21	1.50	2.47	1.54	1.29	2.37
CKF	3.61	1.29	1.70	2.27	1.10	1.87	2.38	1.09	2.00
SRCKF	1.14	0.63	1.12	1.28	0.73	1.10	1.67	0.87	1.24

[1]	杨凯悦, 谢欣兵, 杜晓钟. 基于离散元法的锂电池极片辊压过程探究[J]. 储能科学与技术, 2024, 13(8): 2570-2579.
[2]	周洪, 辛竹琳, 付豪, 张强, 魏凤. 基于专利数据挖掘的固态锂电池关键材料分析[J]. 储能科学与技术, 2024, 13(7): 2386-2398.
[3]	姜森, 陈龙, 孙创超, 王金泽, 李如宏, 范修林. 低温锂电池电解液的发展及展望[J]. 储能科学与技术, 2024, 13(7): 2270-2285.
[4]	王宇豪, 李志勇, 郭新. 聚合物基电解质在低温固态锂电池中的应用与挑战[J]. 储能科学与技术, 2024, 13(7): 2243-2258.
[5]	郝峻丰, 朱璟, 申晓宇, 岑官骏, 乔荣涵, 张新新, 田孟羽, 金周, 詹元杰, 孙蔷馥, 闫勇, 贲留斌, 俞海龙, 刘燕燕, 黄学杰. 锂电池百篇论文点评（2024.04.01—2024.05.31）[J]. 储能科学与技术, 2024, 13(7): 2361-2376.
[6]	李臣, 张会林, 张建平. 基于核函数和超参数优化的退役锂电池健康状态估计[J]. 储能科学与技术, 2024, 13(6): 2010-2021.
[7]	陈艺, 秦琪, 赵龙, 陈子坤, 王安宁. 新型储能技术的中国专利布局分析[J]. 储能科学与技术, 2024, 13(6): 2089-2098.
[8]	甄箫斐, 王贝贝, 张小虎, 孙一铭, 曹文炅, 董缇. 锂电池储能系统热失控气体生成及扩散规律研究[J]. 储能科学与技术, 2024, 13(6): 1986-1994.
[9]	刘宝泉, 曹小雨. 锂电池热失控早期典型气体精准检测方法[J]. 储能科学与技术, 2024, 13(6): 1995-2009.
[10]	时海欧. 基于深度学习的锂电池故障分析及应用[J]. 储能科学与技术, 2024, 13(6): 2054-2056.
[11]	冯娜娜, 杨明, 惠周利, 王瑞洁, 宁弘扬. 基于蚁狮优化高斯过程回归的锂电池剩余使用寿命预测[J]. 储能科学与技术, 2024, 13(5): 1643-1652.
[12]	姜媛媛, 屠芳芳, 张芳平, 王盈来, 蔡佳文, 杨东辉, 李艳红, 相佳媛, 夏新辉, 傅继澎. 高性能磷酸铁锂电池补锂技术及机制[J]. 储能科学与技术, 2024, 13(5): 1435-1442.
[13]	廉高棨, 叶敏, 王桥, 李岩, 麻玉川, 孙乙丁, 杜鹏辉. 基于改进模型与优化自适应CKF的锂离子电池快速变温工况下的SOC估计[J]. 储能科学与技术, 2024, 13(5): 1667-1676.
[14]	谢欣兵, 杨凯悦, 杜晓钟. 锂电池极片辊压过程力学行为与结构[J]. 储能科学与技术, 2024, 13(5): 1699-1706.
[15]	何林, 刘江岩, 刘彬, 李夔宁, 代帅. 数据分布多样性对锂电池SOC预测的泛化影响[J]. 储能科学与技术, 2024, 13(5): 1677-1687.