基于双自适应扩展粒子滤波器的锂离子电池状态联合估计

doi:10.19595/j.cnki.1000-6753.tces.222357

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (2500 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Nowadays, it is necessary for Lithium-ion batteries to realize accurate estimation of state of charge (SOC) and state of health (SOH) for improvement in energy management, utilization efficiency, and safety performance. The particle filter is widely used in estimates of SOC and SOH. Particles are generated and recursively updated from a nonlinear process model, thus accurately characterizing the nonlinear characteristics of the battery. However, the basic particle filter suffers from particle degeneracy, where most particle weights approach to zero and contribute little to further estimation. Furthermore, as batteries age, estimation error increases due to parameter changes in the battery model if not updated in time. Therefore, this paper proposes the dual adaptive extended particle filter (DAEPF) based on the fractional-order battery model. State estimation accuracy is utilized to adjust noise covariance adaptively, and the extended Kalman filter (EKF) realizes local linearization of the particle distribution function. SOC and SOH are simultaneously estimated at different time steps so that the aging effect of parameters on SOC estimation is avoided with a low computation burden.
Firstly, this study establishes the factional-order equivalent circuit model, which replaces integer-order capacitors with constant phase elements. The parameters in the proposed model are identified by an adaptive genetic algorithm, proving that the factional-order model has a lower error when estimating terminal voltage. Secondly, considering the balance between convergence rate and estimation accuracy, the noise covariance of the system is adaptively adjusted according to the prior and the posterior estimates. Thirdly, the proposal distribution is obtained by local linearization, and the extended Kalman filter is utilized to compute the expectation and variance of normally distributed particles. As a result, the particle distribution is closer to the actual value, and the weights of particles are updated in real time. Finally, the SOC and SOH of the Lithium-ion battery are estimated by two particle filters, one for SOC estimation and the other for parameter estimation. With battery parameters updated constantly, the SOC estimation is improved under different working conditions.
In the pulse discharge test, the mean absolute error in the estimated terminal voltage of the fractional-order circuit model is only 0.005 2 V, 22.4% lower than the integer model. The UDDS test verifies the effectiveness of the proposed joint-estimation algorithm. The results show that DAEPF can estimate SOC in less than 151 seconds with a maximum error of 1.35%, mean absolute error of 0.39%, and root-mean-square error of 0.48%, all lower than the single filter. Moreover, as SOC is estimated in the state filter, SOH is computed with a larger time step in the parameter filter whose mean absolute error is lower than 0.5%.
The conclusions of this study are as follows: (1) Compared with the integer-order circuit model, the factional-order circuit model can better describe the dynamic process of the battery with lower estimation error in terminal voltage. (2) The noise covariance should be adjusted based on the difference between the prior and posterior estimates. (3) The extended Kalman filter is suitable for local linearization of the proposed distribution of particles, which assumes a normal distribution of particles and gives expectation and variance of the distribution. (4) Joint estimation of SOC and SOH by the developed particle filters can update battery parameters in time and thus improve SOC estimation accuracy. In return, the new states are fed into the parameter filter for further estimation. The test results demonstrate that DAEPF achieves higher estimation accuracy and a faster convergence rate.

Key words： Lithium-ion battery fractional order model state of charge state of health adaptive extended particle filter

Received: 23 December 2022

PACS:

TM912

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Liu Yiqi
	Lei Wanjun
	Liu Qian
	Gao Yichao
	Dong Ming

Cite this article:

Liu Yiqi,Lei Wanjun,Liu Qian等. Joint State Estimation of Lithium-Ion Battery Based on Dual Adaptive Extended Particle Filter[J]. Transactions of China Electrotechnical Society, 2024, 39(2): 607-616.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.222357 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I2/607

[1] 高岩, 李少彦, 辛颂旭, 等. 2021年中国储能发展现状与展望[J]. 水力发电, 2022, 48(9): 1-4, 55.
Gao Yan, Li Shaoyan, Xin Songxu, et al.Status and prospect of China's energy storage development in 2021[J]. Water Power, 2022, 48(9): 1-4, 55.
[2] 高聚忠, 邢荔波. 我国能源发展战略浅析[J]. 煤质技术, 2015(增刊1): 49-54.
Gao Juzhong, Xing Libo.Discussion on developing strategy of Chinese energy resources[J]. Coal Quality Technology, 2015(S1): 49-54.
[3] Dunn B, Kamath H, Tarascon J M.Electrical energy storage for the grid: a battery of choices[J]. Science, 2011, 334(6058): 928-935.
[4] 孙金磊, 唐传雨, 李磊, 等. 基于状态与模型参数联合估计的老化电池可充入电量估计方法[J]. 电工技术学报, 2022, 37(22): 5886-5898.
Sun Jinlei, Tang Chuanyu, Li Lei, et al.An estimation model of rechargeable electric quantity for aging battery based on joint estimation of state and model parameters[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5886-5898.
[5] 黄凯, 丁恒, 郭永芳, 等. 基于数据预处理和长短期记忆神经网络的锂离子电池寿命预测[J]. 电工技术学报, 2022, 37(15): 3753-3766.
Huang Kai, Ding Heng, Guo Yongfang, et al.Prediction of remaining useful life of lithium-ion battery based on adaptive data processing and long short-term memory network[J]. Transactions of China Electrotechnical Society, 2022, 37(15): 3753-3766.
[6] 孙丙香, 任鹏博, 陈育哲, 等. 锂离子电池在不同区间下的衰退影响因素分析及任意区间的老化趋势预测[J]. 电工技术学报, 2021, 36(3): 666-674.
Sun Bingxiang, Ren Pengbo, Chen Yuzhe, et al.Analysis of influencing factors of degradation under different interval stress and prediction of aging trend in any interval for Lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 666-674.
[7] 李超然, 肖飞, 樊亚翔, 等. 基于门控循环单元神经网络和Huber-M估计鲁棒卡尔曼滤波融合方法的锂离子电池荷电状态估算方法[J]. 电工技术学报, 2020, 35(9): 2051-2062.
Li Chaoran, Xiao Fei, Fan Yaxiang, et al.A hybrid approach to Lithium-ion battery SOC estimation based on recurrent neural network with gated recurrent unit and Huber-M robust Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2051-2062.
[8] 卢婷, 杨文强. 锂离子电池全生命周期内评估参数及评估方法综述[J]. 储能科学与技术, 2020, 9(3): 657-669.
Lu Ting, Yang Wenqiang.Review of evaluation parameters and methods of Lithium batteries throughout its life cycle[J]. Energy Storage Science and Technology, 2020, 9(3): 657-669.
[9] 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.
Li Ning, He Fuxing, Ma Wentao, et al.State-of-charge estimation of Lithium-ion battery based on gated recurrent unit using empirical mode decom-position[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4528-4536.
[10] 刘聪聪, 李珺凯, 刘凯文, 等. 基于人工智能的锂电池SOC预测建模与优化[J]. 无线电通信技术, 2019, 45(3): 237-242.
Liu Congcong, Li Junkai, Liu Kaiwen, et al.Modeling and optimization of SOC prediction for Lithium battery based on artificial intelligence[J]. Radio Communications Technology, 2019, 45(3): 237-242.
[11] Hu J N, Hu J J, Lin H B, et al.State-of-charge estimation for battery management system using optimized support vector machine for regression[J]. Journal of Power Sources, 2014, 269: 682-693.
[12] 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.
Liu Suzhen, Yuan Luhang, Zhang Chuang, 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.
[13] 巫春玲, 胡雯博, 孟锦豪, 等. 基于最大相关熵扩展卡尔曼滤波算法的锂离子电池荷电状态估计[J]. 电工技术学报, 2021, 36(24): 5165-5175.
Wu Chunling, Hu Wenbo, Meng Jinhao, et al.State of charge estimation of Lithium-ion batteries based on maximum correlation-entropy criterion extended Kalman filtering algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5165-5175.
[14] 陈息坤, 孙冬, 陈小虎. 锂离子电池建模及其荷电状态鲁棒估计[J]. 电工技术学报, 2015, 30(15): 141-147.
Chen Xikun, Sun Dong, Chen Xiaohu.Modeling and state of charge robust estimation for Lithium-ion batteries[J]. Transactions of China Electrotechnical Society, 2015, 30(15): 141-147.
[15] 谢长君, 费亚龙, 曾春年, 等. 基于无迹粒子滤波的车载锂离子电池状态估计[J]. 电工技术学报, 2018, 33(17): 3958-3964.
Xie Changjun, Fei Yalong, Zeng Chunnian, et al.State-of-charge estimation of Lithium-ion battery using unscented particle filter in vehicle[J]. Transa-ctions of China Electrotechnical Society, 2018, 33(17): 3958-3964.
[16] 刘淑杰, 郝昆昆, 王永, 等. 基于改进粒子滤波算法的动力锂离子电池荷电状态估计[J]. 大连理工大学学报, 2020, 60(4): 392-401.
Liu Shujie, Hao Kunkun, Wang Yong, et al.State of charge estimation of power Lithium-ion battery based on improved particle filter algorithms[J]. Journal of Dalian University of Technology, 2020, 60(4): 392-401.
[17] 彭方想, 南金瑞, 孙立清. 基于权值选择粒子滤波算法的锂离子电池SOC估计[J]. 太原理工大学学报, 2020, 51(5): 750-755.
Peng Fangxiang, Nan Jinrui, Sun Liqing.SOC estimation of Lithium-ion battery based on weight selection particle filter algorithm[J]. Journal of Taiyuan University of Technology, 2020, 51(5): 750-755.
[18] 印学浩, 宋宇晨, 刘旺, 等. 基于多时间尺度的锂离子电池状态联合估计[J]. 仪器仪表学报, 2018, 39(8): 118-126.
Yin Xuehao, Song Yuchen, Liu Wang, et al.Multi-scale state joint estimation for Lithium-ion battery[J]. Chinese Journal of Scientific Instrument, 2018, 39(8): 118-126.
[19] Petráš I.Fractional-order nonlinear systems: modeling, analysis and simulation[M]. Springer Science & Business Media, 2011.
[20] Ortigueira M D, Machado J A T. Fractional calculus applications in signals and systems[J]. Signal Processing, 2006, 86(10): 2503-2504.
[21] 黄凯, 郭永芳, 李志刚. 基于信息反馈粒子群的高精度锂离子电池模型参数辨识[J]. 电工技术学报, 2019, 34(增刊1): 378-387.
Huang Kai, Guo Yongfang, Li Zhigang.High precision parameter identification of Lithium-ion battery model based on feedback particle swarm optimization algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(S1): 378-387.
[22] 黄小平, 王岩, 缪鹏程. 粒子滤波原理及应用: MATLAB仿真[M]. 北京: 电子工业出版社, 2017.