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| A Review of SOC Estimation Methods for Lithium-Ion Batteries Based on Electrochemical Model |
| Wu Longxing, Pang Hui, Jin Jiamin, Geng Yuanfei, Liu Kai |
| School of Mechanical and Precision Instrument Engineering Xi’an University of Technology Xi’an 710048 China |
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Abstract Accurate state of charge (SOC) estimation is one of the important functions for battery management system. Currently, the model-based approach is the most widely used solution for lithium-ion battery SOC estimation. Compared with the equivalent circuit model (ECM), the electrochemical model (EM) has gradually become the research focus of the next generation advanced battery management systems due to its ability to estimate the SOC coupled with electrochemical mechanism. However, the existing review studies of the model-based battery SOC estimation methods are mostly focused on ECMs, and the EMs are rarely discussed systematically. For this reason, the EM-based SOC estimation algorithms are reviewed in this paper. First, the modeling and parameter identification methods of EMs are summarized, and the existing approaches to EM-based SOC estimation are discussed. Then, existing challenges and the future prospects of the EM-based SOC estimation are presented. The insights presented in this paper are expected to catalyze the development and application of the EM-based advanced battery management system algorithms.
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Received: 12 July 2021
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[1] Su Tonglun, Lü Nawei, Zhao Zhixing, et al.Safety warning of lithium-ion battery energy storage station via venting acoustic signal detection for grid application[J]. Journal of Energy Storage, 2021, 38: 102498.
[2] Huang Zebo, Mu Anle.Research and analysis of performance improvement of vanadium redox flow battery in microgrid: a technology review[J]. International Journal of Energy Research, 2021, 45(10): 14170-14193.
[3] 王榘, 熊瑞, 穆浩. 温度和老化意识融合驱动的电动车辆锂离子动力电池电量和容量协同估计[J].电工技术学报,2020, 35(23): 4980-4987.
Wang Ju, Xiong Rui, Mu Hao.Co-estimation of lithium-ion battery state-of-charge and capacity through the temperature and aging awareness model for electric vehicles[J]. Transactions of China Electrotechnical Society. 2020, 35(23): 4980-4987.
[4] Hu Xiaosong, Han Jie, Tang Xiaolin, et al.Powertrain design and control in electrified vehicles: a critical review[J]. IEEE Transactions on Transportation Electrification, 2021, 7(3): 1990-2009.
[5] 谷苗, 夏超英, 田聪颖. 基于综合型卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2019, 34(2): 419-426.
Gu Miao, Xia Chaoying, Tian Congying.Li-ion battery state of charge estimation based on comprehensive Kalman filter[J]. Transactions of China Electrotechnical Society, 2019, 34(2): 419-426.
[6] 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: 118-128.
Shu Xing, Liu Yonggang, Shen Jiangwei, 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: 118-128.
[7] 潘海鸿, 张沫, 王惠民, 等. 基于多影响因素建立锂离子电池充电内阻的动态模型[J]. 电工技术学报, 2021, 36(10): 2199-2206.
Pan Haiou, Zhang Mo, Wang Huiming, et al.Establishing a dynamic model of lithium-ion battery charging internal resistance based on multiple factors[J]. Transactions of China Electrotechnical Society, 2021, 36(10): 2199-2206.
[8] 刘新天, 何耀, 曾国建, 等. 考虑温度影响的锂电池功率状态估计[J]. 电工技术学报, 2016, 31(13): 155-163.
Liu Xintian, He Yao, Zeng Guojian, et al.State of power estimation for Li-ion battery considering the effect of temperature[J]. Transactions of China Electrotechnical Society, 2016, 31(13): 155-163.
[9] 曹铭, 黄菊花, 杨志平, 等. 车用锂离子动力电池自适应状态联合估计研究[J]. 控制理论与应用, 2020, 37(9): 1951-1962.
Cao Ming, Huang Juhua, Yang Zhiping, et al.Research on adaptive state of charge and state of power joint estimation algorithm of vehicle lithium ion power batteries[J]. Control Theory & Applications, 2020, 37(9): 1951-1962.
[10] 王春雨, 崔纳新, 李长龙, 等. 基于电热耦合模型和多参数约束的动力电池峰值功率预测[J]. 机械工程学报, 2019, 55(20): 28-35.
Wang Chunyu, Cui Naxin, Li Changlong, et al.State of Power prediction based on electro-thermal battery model and multi-parameter constrains for lithium-ion battery[J]. Journal of Mechanical Engineering, 2019, 55(20): 28-35.
[11] Hannan M A, Lipu M S H, Hussain A, et al. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations[J]. Renewable and Sustainable Energy Reviews, 2017, 78: 834-854.
[12] Ng K S, Moo C S, Chen Y P, et al.Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries[J]. Applied Energy, 2009, 86(9): 1506-1511.
[13] Zhang Shuzhi, Guo Xu, Dou Xiaoxin, et al.A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery[J]. Sustainable Energy Technologies and Assessments, 2020, 40: 100752.
[14] Yu Quanqing, Wan Changjiang, Li Junfu, et al.An open circuit voltage model fusion method for state of charge estimation of lithium-ion batteries[J]. Energies, 2021, 14(7): 1797.
[15] Ren Zhong, Du Changqing, Wu Zhongyi, et al.A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium-ion batteries[J]. International Journal of Energy Research, 2021.
[16] Jiang Bo, Dai Haifeng, Wei Xuezhe, et al.Joint estimation of lithium-ion battery state of charge and capacity within an adaptive variable multi-timescale framework considering current measurement offset[J]. Applied Energy, 2019, 253: 13619.
[17] 刘征宇, 黎盼春, 朱诚诚, 等. 基于组合模型的锂电池参数辨识和电池荷电状态在线联合估计[J]. 中国机械工程,2020, 31(10): 1162-1168.
Liu Zhengyu, Li Pangchun, Zhu Chengcheng, et al.Lithium battery parameter identificaiton and SOC online joint estimation based on combined model[J]. China Mechanical Engineering, 2020, 31(10): 1162-1168.
[18] He Zhigang, Li Yaotai, Sun Yanyan, et al.State-of-charge estimation of lithium ion batteries based on adaptive iterative extended Kalman filter[J]. Journal of Energy Storage, 2021, 39: 102593.
[19] Zou Zhiyue, Xu Jun, Mi Chris, et al.Evaluation of model based state of charge estimation methods for lithium-ion batteries[J]. Energies, 2014, 7(8): 5065-5082.
[20] 蒋聪, 王顺利, 李小霞, 等. 基于改进EKF算法变温度下的动力锂电池SOC估算[J]. 储能科学与技术, 2020, 9(1): 145-151.
Jiang Cong, Wang Shunli, Li Xiaoxia, et al.The estimation method of SOC for power lithium battery based on improved EKF algorithm adaptive to various temperature[J]. Energy Storage and Technology, 2020, 9(1): 145-151.
[21] 王晓兰, 靳皓晴, 刘祥远. 基于融合模型的锂离子电池荷电状态在线估计[J]. 工程科学学报, 2020, 42(9): 1200-1208.
Wang Xiaolan, Jin Haoqing, Liu Xiangyuan.Online estimation of the state of charge of a lithium-ion battery based on the fusion model[J]. China Journal of Engineering, 2020, 42(9): 1200-1208.
[22] 刘芳, 马杰, 苏卫星, 等. 基于自适应回归扩展卡尔曼滤波的电动汽车动力电池全生命周期的荷电状态估算方法[J]. 电工技术学报, 2020, 35(4): 698-707.
Liu Fang, Ma Jie, Su Weixing, et al.State of charge estimation method of electric vehicle power battery life cyle based on auto regression extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(4): 698-707.
[23] Xiong Rui, Sun Fengchun, Gong Xianzhi, et al.A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles[J]. Applied Energy, 2014, 113: 1421-1433.
[24] 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.
[25] 贾俊, 胡晓松, 邓忠伟, 等. 数据驱动的锂离子电池健康状态综合评分及异常电池筛选[J]. 机械工程学报, 2021, 57(14): 141-149.
Jia Jun, Hu Xiaosong, Deng Zhongwei, et al.Data-driven comprehensive evaluation of lithium-ion battery state of health and abnormal battery screening[J]. Journal of Mechanical Engineering, 2021, 57(14): 141-149.
[26] Tang Xiaopeng, Liu Kailong, Li Kang, et al.Recovering large-scale battery aging dataset with machine learning[J]. Patterns, 2021, 2(8): 1000302.
[27] Li Weihan, Sengupta N, Dechent P, et al.One-shot battery degradation trajectory prediction with deep learning[J]. Journal of Power Sources, 2021, 506: 230024.
[28] How D N T, Hannan M A, Lipu M S H, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: a review[J]. IEEE Access, 2019, 7: 136116-136136.
[29] Hu Xiaosong, Li Shengbo, Peng Huei.A comparative study of equivalent circuit models for Li-ion batteries[J]. Journal of Power Sources, 2012, 198: 359-367.
[30] Yang Jufeng, Cai Yingfeng, Pan Chaofeng, et al.A novel resistor-inductor network-based equivalent circuit model of lithium-ion batteries under constant-voltage charging condition[J]. Applied Energy, 2019, 254: 113726.
[31] Lai Xin, Wang Shuyu, Ma Shangde, et al.Parameter sensitivity analysis and simplification of equivalent circuit model for the state of charge of lithium-ion batteries[J]. Electrochimica Acta, 2019, 330: 135239.
[32] 庞辉, 郭龙, 武龙星, 等. 考虑环境温度影响的锂离子电池改进双极化模型及其荷电状态估算[J]. 电工技术学报, 2021, 36(10): 2178-2189.
Pang Hui, Guo Long, Wu Longxing, et al.An improved dual polarization model of li-ion battery and its state of charge estimation considering ambient temperature[J]. Transactions of China Electrotechnical Society, 2021, 36(10): 2178-2189.
[33] Wang Yuhai, Li Junfu, Zhang Jinming, et al.Lithium-iron-phosphate battery electrochemical modelling under a wide range of ambient temperatures[J]. Journal of Electroanalytical Chemistry, 2021, 882: 115041.
[34] 庞辉. 基于电化学模型的锂离子电池多尺度建模及其简化方法[J]. 物理学报, 2017, 66(23): 312-322.
Pang Hui.Multi-scale modeling and its simplicaiton method of li-ion battery based on electrochemical model[J]. Acta Physica Sinica, 2017, 66(23): 312-322.
[35] Li Changlong, Cui Naxin, Wang Chunyu, et al.Simplified electrochemical lithium-ion battery model with variable solid-phase diffusion and parameter identification over wide temperature range[J]. Journal of Power Sources, 2021, 497: 229900.
[36] Du Xinhao, Meng Jinhao, Zhang Yingmin, et al.An information appraisal procedure endows reliable online parameter identification to lithium-ion battery model[J]. IEEE Transactions on Industrial Electronics, 2021, DOI:10.1109/TIE.2021-309192.
[37] 来鑫, 李云飞, 郑岳久, 等. 基于SOC-OCV优化曲线与EKF的锂离子电池荷电状态全局估计[J]. 汽车工程, 2021, 43(1): 19-26.
Lai Xin, Li Yunfei, Zheng Yuejiu, et al.An Overall estimation of state of charge on SOC-OCV optimization curve and EKF for lithium-ion battery[J]. Automotive Engineering, 2021, 43(1): 19-26.
[38] 马建, 张大禹, 赵轩, 等. 基于随机加权自适应容积卡尔曼的电池SOC估计[J]. 中国公路学报,2019, 32(11): 234-244.
Ma Jian, Zhang Dayu, Zhao Xuan, et al.State of charge estimation for battery based on adaptively random weighted cubature Kalman filter[J]. China Journal of Highway and Transport, 2019, 32(11): 234-244.
[39] 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.
Li Wei, Liu Weiwei, Deng Yelin.SOC estimation for lithium-ion batteries based on EKF[J]. China Mechanical Engineering, 2020, 31(3): 321-327.
[40] Shen Jiangwei, Xiong Jian, Shu Xing, 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, 2021, 45(4): 5586-5601.
[41] Hu Yang, Yin Yilin, Bi Yalan, et al.A control oriented reduced order electrochemical model considering variable diffusivity of lithium ions in solid[J]. Journal of Power Sources, 2020, 468: 228322.
[42] Jokar A, Rajabloo B, Desilets M, et al.Review of simplified Pseudo-two-dimensional models of lithium-ion batteries[J]. Journal of Power Sources, 2016, 327: 44-55.
[43] Xiong Rui, Cao Jiayi, Yu Quanqing, et al.Critical review on the battery state of charge estimation methods for electric vehicles[J]. IEEE Access, 2018, 6: 1832-1843.
[44] Chaturvedi N A, Klein R, Christensen J, et al.Algorithms for advanced battery-management systems[J]. IEEE Control Systems Magazine, 2010, 30(3): 49-68.
[45] 高铭琨, 徐海亮, 吴明铂. 基于等效电路模型的动力电池SOC估计方法综述[J]. 电气工程学报, 2021, 16(1): 90-102.
Gao Mingkun, Xu Hailiang, Wu Mingbo.Review of SOC estiamtion methods for power battery based on equivalent circuit model[J]. Journal of Electrical Engineering, 2021, 16(1): 90-102.
[46] 孙冬, 许爽, 李超, 等. 锂离子电池荷电状态估计方法综述[J]. 电池, 2018, 48(4): 284-287.
Sun Dong, Xu Shuang, Li Chao, et al.Review of state of charge estimation method for Li-ion battery[J]. Battery Bimothly, 2018, 48(4): 284-287.
[47] 余运俊,谌新,万晓凤.电动汽车电池荷电状态估计研究综述[J]. 电源学报, 2014(3): 95-102.
Yu Yunjun, Chen Xin, Wan Xiaofeng.Reviewer of estiamting the state of battery charge for electric vehicle[J]. Journal of Power Supply, 2014(3): 95-102.
[48] Dai Haifeng, Jiang Bo, Hu Xiaosong, et al.Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends[J]. Renewable and Sustainable Energy Reviews, 2021, 138: 110480.
[49] Qays M O, Buswig Y, Hossain M L, et al.Recent progress and future trends on state of charge estimation methods to improve battery-storage efficiency: a review[J]. CSEE Journal of Power and Energy Systems, 2020, DOI:10.17775/CSEEJPES. 2019.03060.
[50] Shrivastava P, Soon T K, Idris M Y I B, et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries[J]. Renewable and Sustainable Energy Reviews, 2019, 113: 109233.
[51] Lai Xin, Zheng Yuejiu, Sun Tao.A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries[J]. Electrochimica Acta, 2018, 259: 566-577.
[52] Hu Xiaosong, Feng Fei, Liu Kailong, et al.State estimation for advanced battery management: Key challenges and future trends[J]. Renewable and Sustainable Energy Reviews, 2019, 114: 109334.
[53] Tian Jiaqiang, Wang Yujie, Chen Zonghai.An improved single particle model for lithium-ion batteries based on main stress factor compensation[J]. Journal of Cleaner Production, 2021, 278: 123456.
[54] 匡柯, 孙跃东, 任东生, 等. 车用锂离子电池电化学-热耦合高效建模方法[J]. 机械工程学报, 2021, 57(14): 10-22.
Kuang Ke, Sun Yuedong, Ren Dongsheng, et al.Efficient Approach for electrochemical-thermal coupled modeling of large-format lithium-ion power battery[J]. Journal of Mechanical Engineering, 2021, 57(14): 10-22.
[55] 胡晓松, 唐小林. 电动车辆锂离子动力电池建模方法综述[J]. 机械工程学报, 2017, 53(16): 34-45.
Hu Xiaosong, Tang Xiaolin.Review of modeling techniques for lithium-ion traction battreies in electric vehicles[J]. Journal of Mechanical Engineering, 2017, 53(16): 34-45.
[56] 程昀, 李劼, 贾明, 等. 锂离子电池多尺度数值模型的应用现状及发展前景[J]. 物理学报, 2015, 64(21): 137-152.
Cheng Jun, Li Jie, Jia Ming, et al.Application status and future of multi-scale numerical models for lithium ion battery[J]. Acta Physica Sinica, 2015, 64(21): 137-152.
[57] Kemper P, Li S E, Kum D.Simplification of pseudo two dimensional battery model using dynamic profile of lithium concentration[J]. Journal of Power Sources, 2015, 286: 510-525.
[58] Li Weihan, Fan Yue, Ringbeck F, et al.Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter[J]. Journal of Power Sources, 2020, 476: 228534.
[59] Plett G L.锂离子电池荷电状态不同估算方法的综述及讨论(英文)[J]. 汽车安全与节能学报, 2019, 10(3): 249-272.
Plett G L.Review and some perspectives on different methods to estimate state of charge of lithium-ion batteries[J]. Jouneral of Automotive Safety and Energy, 2019, 10(3): 249-272.
[60] 康鑫, 时玮, 陈洪涛. 基于锂离子电池简化电化学模型的参数辨识[J]. 储能科学与技术, 2020, 9(3): 969-978.
Kang Xin, Shi Wei, ChenHongtao. Parameter identification based on simplified electrochemical model of lithium ion battery[J]. Energy Storage Science and Technology, 2020, 9(3): 969-978.
[61] Doyle M, Fuller T F, Newman J.Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell[J]. Journal of the Electrochemical Society, 1993, 140(6): 1526-1533.
[62] Han Sangwoo, Tang Yifan, KhaleghI Rahimian S. A numerically efficient method of solving the full-order pseudo-2-dimensional (P2D) Li-ion cell model[J]. Journal of Power Sources, 2021, 490: 229571.
[63] 方儒卿, 张娜, 李哲. 3类锂离子电池多孔电极模型比较研究及电池正向设计应用[J]. 清华大学学报(自然科学版), 2021, 61(10): 1055-1065.
Fang Ruqing, Zhang Na, Li Zhe.Comparison study of three porous electrode models for the forward design if lithium-ion batteries[J]. Journal of Tsinghua Univerisity( Science & Technology), 2021, 61(10): 1055-1065.
[64] 蒋跃辉, 艾亮, 贾明, 等. 基于动态参数响应模型的动力锂离子电池循环容量衰减研究[J]. 物理学报, 2017, 66(11): 376-386.
Jiang Yuehui, Ai Liang, Jia Ming, et al.Cyclic capacity fading of the power lithium ion battery based on a numerical modeling with dynamic responses[J]. Acta Physica Sinica, 2017, 66(11): 376-386.
[65] Cai L, White R E.Mathematical modeling of a lithium ion battery with thermal effects in COMSOL Inc. Multiphysics (MP) software[J]. Journal of Power Sources, 2011, 196(14): 5985-5989.
[66] Kwon K H, Shin C B, Kang T H, et al.A two-dimensional modeling of a lithium-polymer battery[J]. Journal of Power Sources, 2006, 163(1): 151-157.
[67] Martínez-rosas E, Vasquez-medrano R, Flores-tlacuahuac A. Modeling and simulation of lithium-ion batteries[J]. Computers & Chemical Engineering, 2011, 35(9): 1937-1948.
[68] Reimers J N.Algorithmic Improvements and PDE decoupling, for the simulation of porous electrode cells[J]. Journal of The Electrochemical Society, 2013, 160(6): A811.
[69] Torchio M, Magni L, Gopaluni R B, et al.LIONSIMBA: a Matlab framework based on a finite volume model suitable for Li-ion battery design, simulation, and control[J]. Journal of The Electrochemical Society, 2016, 163(7): A1192.
[70] Rodríguez A, Plett G L, Trimboli m S. Comparing four model-order reduction techniques, applied to lithium-ion battery-cell internal electrochemical transfer functions[J]. eTransportation, 2019, 1: 100009.
[71] Haran B S, Popov B N, White R E.Determination of the hydrogen diffusion coefficient in metal hydrides by impedance spectroscopy[J]. Journal of Power Sources, 1998, 75(1): 56-63.
[72] Guo M, Sikha G, White R E.Single-particle model for a lithium-ion cell: thermal behavior[J]. Journal of the Electrochemical Society, 2011, 158(2): A122.
[73] 程麒豫, 张希, 高一钊, 等. 基于降阶电化学模型估算锂离子电池状态[J]. 电池, 2021, 51(2): 121-124.
Cheng Qiyu, Zhang Xi, Gao Yizhao.Estimation staate of Li-ion batttery based on reduced-order electrochemical model[J]. Battery Bimothly, 2021, 51(2): 121-124.
[74] Wang C Y, Srinivasan V.Computational battery dynamics (CBD)—electrochemical/thermal coupled modeling and multi-scale modeling[J]. Journal of Power Sources, 2002, 110(2): 364-376.
[75] Zhang Qi, White R E.Comparison of approximate solution methods for the solid phase diffusion equation in a porous electrode model[J]. Journal of Power Sources, 2007, 165(2): 880-886.
[76] Subramanian V R, Ritter J A, White R E.Approximate solutions for galvanostatic discharge of spherical particles i. constant diffusion coefficient[J]. Journal of The Electrochemical Society, 2001, 148(11): E444.
[77] 李涛, 程夕明, 胡晨华. 锂离子电池电化学降阶模型性能对比研究[J]. 物理学报, 2021, 70(13): 423-434.
Li Tao, Cheng Ximing, Hu Chenhua.Comparative study of reduced-order electrochemical models of the lithium-ion battery[J]. Acta Physica Sinica, 2021, 70(13): 423-434.
[78] Li Y, Vilathgamuwa M, Choi S S, et al.Development of a degradation-conscious physics-based lithium-ion battery model for use in power system planning studies[J]. Applied Energy, 2019, 248: 512-525.
[79] 徐兴, 徐琪凌, 王峰, 等. 车用锂离子动力电池电化学模型修正方法研究[J]. 机械工程学报, 2019, 55(12): 128-136.
Xu Xing, Xu Qiling, Wang Feng, et al.Modification method of electrochemical mdoel for vehicular lithium-ion power battery[J]. Journal of Mechanical Engineering, 2019, 55(12): 128-136.
[80] 徐兴, 王位, 陈龙. 基于GA的车用锂离子电池电化学模型参数辨识[J]. 汽车工程, 2017, 39(7): 813-821.
Xu Xing, Wang Wei, Chen Long.Parameter identification of electrochemical model for vehicula lithium ion battery based on genetic algorithm[J]. Automobile Engineering, 2017, 39(7): 813-821.
[81] Zhao Yinyin, Choe S Y.Evaluation of order reduction techniques for porous electrode diffusion equation in lithium ion model[J]. SAE Technical Papers, 2014, DOI:10.4271/2014-01-1835.
[82] Liu Ji, Li Guang, Fathy H K.A computationally efficient approach for optimizing lithium-ion battery charging[J]. Journal of Dynamic Systems, Measurement, and Control, 2015, 138(2): 021009.
[83] Mehta R, Gupta A.An improved single-particle model with electrolyte dynamics for high current applications of lithium-ion cells[J]. Electrochimica Acta, 2021, 389: 138623.
[84] Schmidt A P, Bitzer M, Imre Á W, et al.Experiment-driven electrochemical modeling and systematic parameterization for a lithium-ion battery cell[J]. Journal of Power Sources, 2010, 195(15): 5071-5080.
[85] Li J, Lotfi N, Landers R G, et al.A single particle model for lithium-ion batteries with electrolyte and stress-enhanced diffusion physics[J]. Journal of the Electrochemical Society, 2017, 164(4): A874.
[86] 刘征宇, 杨昆, 魏自红, 等. 包含液相扩散方程简化的锂离子电池电化学模型[J]. 物理学报, 2019, 68(9): 251-258.
Liu Zhengyu, Yang Kun, Wei Zihong, et al.Electrochemical model of lithium ion battery with simplified liquid phase diffusion equation[J]. Acta Physica Sinica, 2019, 68(9): 251-258.
[87] Prada E, Di Domenico D, Creff Y, et al.Simplified electrochemical and thermal model of LiFePO4-graphite Li-ion batteries for fast charge applications[J]. Journal of The Electrochemical Society, 2012, 159(9): A1508.
[88] Di Domenico D, Stefanopoulou A, Fiengo G.Lithium-ion battery state of charge and critical surface charge estimation using an electrochemical model-based extended Kalman filter[J]. Journal of Dynamic Systems, Measurement, and Control, 2010, 132(6): 061302.
[89] Guduru A, Northrop P W C, Jain S, et al. Analytical solution for electrolyte concentration distribution in lithium-ion batteries[J]. Journal of Applied Electrochemistry, 2012, 42(4): 189-199.
[90] Tanim T R, Rahn C D, Wang C Y.State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model[J]. Energy, 2015, 80: 731-739.
[91] 杨俊, 张希, 高一钊. 锂电池电化学传递函数模型建模及参数辨识[J]. 电源技术, 2019, 43(7): 1132-1135.
Yang Jun, Zhang Xi, Gao Yizhao.Modeling and parameter idetification of electrochemical transfer function model for lithium battery[J]. Power Technology, 2019, 43(7): 1132-1135.
[92] Fan Guodong, Pan Ke, Canova M, et al.Modeling of Li-ion cells for fast simulation of high C-rate and low temperature operations[J]. Journal of the Electrochemical Society, 2016, 163(5): A666.
[93] Luo Weilin, Lyu Chao, Wang Lixin, et al.An approximate solution for electrolyte concentration distribution in physics-based lithium-ion cell models[J]. Microelectronics Reliability, 2013, 53(6): 797-804.
[94] Luo Weilin, Lü Chao, Wang Lixin, et al.A new extension of physics-based single particle model for higher charge-discharge rates[J]. Journal of Power Sources, 2013, 241: 295-310.
[95] Han Xuebing, Ouyang Minggao, Lu Languang, et al.Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part II: Pseudo-two-dimensional model simplification and state of charge estimation[J]. Journal of Power Sources, 2015, 278: 814-825.
[96] Deng Zhongwei, Yang Lin, Deng Hao, et al.Polynomial approximation pseudo-two-dimensional battery model for online application in embedded battery management system[J]. Energy, 2018, 142: 838-850.
[97] Liu Boyang, Tang Xiaopeng, Gao Furong.Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model[J]. Electrochimica Acta, 2020, 344: 136098.
[98] Li Changlong, Cui Naxin, Wang Chunyu, et al.Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods[J]. Energy, 2021, 221: 119662.
[99] Senthil Kumar V.Reduced order model for a lithium ion cell with uniform reaction rate approximation[J]. Journal of Power Sources, 2013, 222: 426-441.
[100] Wu Longxing, Liu Kai, Pang Hui.Evaluation and observability analysis of an improved reduced-order electrochemical model for lithium-ion battery[J]. Electrochimica Acta, 2021, 368: 137604.
[101] 田华, 王伟光, 舒歌群, 等. 基于多尺度、电化学-热耦合模型的锂离子电池生热特性分析[J].天津大学学报(自然科学与工程技术版), 2016, 49(7): 734-741.
Tian Hua, Wang Weiguang, Shu Gequn, et al.Analysis of heat generation in a li-ion battery based on a multi-scale and electorchemical-thermal model[J]. Journal of Tianjin University (Science and Technology), 2016, 49(7): 734-741.
[102] Li Junfu, Wang Dafang, Deng Lei, et al.Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries[J]. Journal of Energy Storage, 2020, 31: 101538.
[103] Yang Shichun, Gao Xinlei, Li Yalun, et al.Minimum lithium plating overpotential control based charging strategy for parallel battery module prevents side reactions[J]. Journal of Power Sources, 2021, 494: 229772.
[104] Hu Xiaosong, Cao Dongpu, Egardt Bo.Condition monitoring in advanced battery management systems: Moving horizon estimation using a reduced electrochemical model[J]. IEEE/ASME Transactions on Mechatronics, 2017, 23(1): 167-178.
[105] Gao Yizhao, Liu Kailong, Zhu Chong, et al.Co-estimation of state-of-charge and state-of-health for lithium-ion batteries using an enhanced electrochemical model[J]. IEEE Transactions on Industrial Electronics, 2021, DOI: 10.1109/TIE.2021. 3066946.
[106] Wang Yujie, Tian Jiaqiang, Sun Zhendong, et al.A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems[J]. Renewable and Sustainable Energy Reviews, 2020, 131: 110015.
[107] Hu Xiaosong, Deng Zhongwei, Lin Xianke, et al.Research directions for next-generation battery management solutions in automotive applications[J]. Renewable and Sustainable Energy Reviews, 2021, 152: 111695.
[108] Zhang Xi, Lu Jinling, Yuan Shifei, et al.A novel method for identification of lithium-ion battery equivalent circuit model parameters considering electrochemical properties[J]. Journal of Power Sources, 2017, 345: 21-29.
[109] Li Yang, Vilathgamuwa M, Farrell T, et al.A physics-based distributed-parameter equivalent circuit model for lithium-ion batteries[J]. Electrochimica Acta, 2019, 299: 451-469.
[110] Zheng Yuejiu, Gao Wenkai, Han Xuebing, et al.An accurate parameters extraction method for a novel on-board battery model considering electrochemical properties[J]. Journal of Energy Storage, 2019, 24: 100745.
[111] Geng Zeyang, Wang Siyang, Lacey M J, et al.Bridging physics-based and equivalent circuit models for lithium-ion batteries[J]. Electrochimica Acta, 2021, 372: 137829.
[112] Pang Hui, Jin Jiamin, Wu Longxing, et al.A comprehensive physics-based equivalent-circuit model and state of charge estimation for lithium-ion batteries[J]. Journal of The Electrochemical Society, 2021, 168(9): 090552.
[113] Lai Q, Ahn H J, Kim Y J, et al.New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery[J]. Applied Energy, 2021, 295: 117034.
[114] Forman J C, Moura S J, Stein J L, et al.Genetic identification and fisher identifiability analysis of the Doyle-Fuller-Newman model from experimental cycling of a LiFePO4 cell[J]. Journal of Power Sources, 2012, 210: 263-275.
[115] Rahman M A, Anwar S, Izadian A.Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method[J]. Journal of Power Sources, 2016, 307: 86-97.
[116] Deng Zhongwei, Deng Hao, Yang Lin, et al.Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery[J]. Energy, 2017, 138: 509-519.
[117] Ramadesigan V, Chen K, Burns N A, et al.Parameter estimation and capacity fade analysis of lithium-ion batteries using reformulated models[J]. Journal of the Electrochemical Society, 2011, 158(9): A1048.
[118] Xiong Rui, Li Linlin, Li Zhirun, et al.An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application[J]. Applied energy, 2018, 219: 264-275.
[119] Munoz P M, Humana R M, Falagüerra T, et al.Parameter optimization of an electrochemical and thermal model for a lithium-ion commercial battery[J]. Journal of Energy Storage, 2020, 32: 101803.
[120] Wang Qiankun, Shen Jiani, Ma Zifeng, et al.Decoupling parameter estimation strategy based electrochemical-thermal coupled modeling method for large format lithium-ion batteries with internal temperature experimental validation[J]. Chemical Engineering Journal, 2021, 424: 130308.
[121] Li Zhe, Huan Jun, Liaw Bor Yann, et al.On state-of-charge determination for lithium-ion batteries[J]. Journal of Power Sources, 2017, 348: 281-301.
[122] How D N T, Hannan M A, Hussain LIPU M S, et al. State-of-charge estimation of li-ion battery in electric vehicles: a deep neural network approach[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5565-5574.
[123] 张振宇, 汪光森, 聂世雄, 等. 脉冲大倍率放电条件下磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2019, 34(8): 1769-1779.
Zhang Zhenyu, Wang Guangsen, Nie Shixiong, et al.State of charge estimation of LiFePO4 battery under the condition of high rate pulsed discharge[J]. Transactions of China Electrotechnical Society, 2019, 34(8): 1769-1779.
[124] Li Weihan, Cao Decheng, Jöst Dominik, et al.Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries[J]. Applied Energy, 2020, 269: 115104.
[125] Plett G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation[J]. Journal of Power Sources, 2004, 134(2): 277-292.
[126] 戴海峰, 孙泽昌, 魏学哲. 利用双卡尔曼滤波算法估计电动汽车用锂离子动力电池的内部状态[J]. 机械工程学报, 2009, 45(6): 95-101.
Dai Haifeng, Sun Zechang, Wei Xuezhe.Estimation of internal states of power lithium-ion batteries used on electric vehicles by dual extended Kalman filter[J]. Journal of Mechanical Engineering, 2009, 45(6): 95-101.
[127] Li Wenqian, Yang Yan, Wang Dongqing, et al.The multi-innovation extended Kalman filter algorithm for battery SOC estimation[J]. Ionics, 2020, 26(12): 6145-6156.
[128] Zhou Xin, Stein J L, Ersal T.Battery state of health monitoring by estimation of the number of cyclable Li-ions[J]. Control Engineering Practice, 2017, 66: 51-63.
[129] Santhanagopalan S, White R E.Online estimation of the state of charge of a lithium-ion cell[J]. Journal of Power Sources, 2006, 161(2): 1346-1355.
[130] Smith k A, Rahn C D, Wang C Y. Model-based electrochemical estimation and constraint management for pulse operation of lithium ion batteries[J]. IEEE Transactions on Control Systems Technology, 2010, 18(3): 654-663.
[131] Stetzel K D, Aldrich L L, Trimboli M S, et al.Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter[J]. Journal of Power Sources, 2015, 278: 490-505.
[132] Li Huanhuan, Zhang Wang, Yang Xinrong, et al.State of charge estimation for lithium-ion battery using an electrochemical model based on electrical double layer effect[J]. Electrochimica Acta, 2019, 326: 134966.
[133] 徐保荣, 王兴成, 张齐, 等. 自适应扩展卡尔曼滤波电池荷电状态估算方法[J]. 哈尔滨工业大学学报,2021,53(7): 92-98.
Xu Baorong, Wang Xingcheng, Zhang Qi, et al.Adaptive extended Kalman filter for estimating the charging state of battery[J]. Jouranl of Harbing Institute of Technology, 2021,53(7): 92-98.
[134] 邓昊, 杨林, 邓忠伟. 基于电化学机理模型的锂离子电池参数辨识及SOC估计[J]. 上海理工大学学报, 2018, (6): 557-565.
Deng Hao, Yang Lin, Deng Zhongwei.Lithium-ion battery parameter identification and SOC estimaiton based on electrochemical models[J]. Journal of University of Shanghai for Science and Technology, 2018, (6): 557-565.
[135] Lü Chao, Li Junfu, Zhang Lulu, et al.State of charge estimation based on a thermal coupling simplified first-principles model for lithium-ion batteries[J]. Journal of Energy Storage, 2019, 25: 100838.
[136] Fan Guodong, Li Xiaoyu, Zhang Ruigang.Global sensitivity analysis on temperature-dependent parameters of a reduced-order electrochemical model and robust state-of-charge estimation at different temperatures[J]. Energy, 2021, 223: 120024.
[137] Julier S J, Uhlmann J K.Unscented filtering and nonlinear estimation[J]. Proceeding of the IEEE, 2004, 92(3): 401-422.
[138] 谈发明, 赵俊杰, 王琪. 动力电池SOC估计的一种新型鲁棒UKF算法[J]. 汽车工程, 2019, 41(8): 944-952.
Tan Faming, Zhao Junjie, Wang Qi.A novel robust UKF algorithm for SOC estiamtion of traction battery[J]. Automotive Engineering, 2019, 41(8):944-952.
[139] Lin Xinyou, Tang Yunliang, Ren Jin, et al.State of charge estimation with the adaptive unscented Kalman filter based on an accurate equivalent circuit model[J]. Journal of Energy Storage, 2021, 41: 102840.
[140] 董祥祥, 武鹏, 葛传久, 等. 基于自适应无迹卡尔曼滤波的锂电池荷电状态估计[J]. 电工电能新技术, 2021, 40(2): 58-65.
Dong Xiangxiang, Wu Peng, Ge Chuanjiu, et al.State of charge estimation of Li-ion battery based on adaptive unscented Kalman filter[J]. Adanced Technology of Electrical Engineering and Energy, 2021, 40(2): 58-65.
[141] Santhanagopalan S, White R E.State of charge estimation using an unscented filter for high power lithium ion cells[J]. International Journal of Energy Research, 2010, 34(2): 152-163.
[142] Ringbeck F, Garbade M, Sauer D U.Uncertainty-aware state estimation for electrochemical model-based fast charging control of lithium-ion batteries[J]. Journal of Power Sources, 2 |
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2022, Vol. 37 
