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动力锂离子电池关键参数估计方法研究进展

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作者:李远茂, 刘桂雄

作者单位:华南理工大学机械与汽车工程学院,广东 广州 510640


关键词:锂离子电池;热失控;电化学模型参数;荷电状态


摘要:

锂离子电池热失控、电化学模型参数和荷电状态等关键参数无法直接通过设备或无损方法进行直接测量,需通过复杂模型或算法进行估计,在参数估计方面仍然面临着挑战。该文对动力锂离子电池关键参数进行系统综述,包括动力锂离子电池热失控扩散建模、电化学模型参数估计、荷电状态参数估计等方法的基本原理、相关研究进展与应用,比较分析各类方法的优缺点及其适用对象,研究指出:研究电池包层级的热失控仿真技术有助于电池包的热扩散特性分析、电池热管理系统设计优化;电化学模型参数估计通常需应用元启发式算法,但选择合适的元启发算法仍需要深入研究、加强;深度学习网络+注意力机制、元启发式算法优化网络超参数等均为提升荷电状态参数估计性能重要方法。


Research progress of key parameter estimation methods for power lithium-ion batteries
LI Yuanmao, LIU Guixiong
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Abstract: Key parameters such as thermal runaway, electrochemical model parameters, and state of charge of lithium-ion batteries cannot be directly measured by devices or non-destructive methods. They require estimation through complex models or algorithms, posing challenges in parameter estimation. This paper provides a systematic review of key parameters of lithium-ion batteries, including thermal runaway diffusion modeling, electrochemical model parameter estimation, and state of charge parameter estimation. It elaborates on the basic principles, research progress, and applications of various methods, comparing and analyzing their advantages, disadvantages, and applicability. It is pointed out that research on thermal runaway simulation technology at the battery pack level contributes to thermal diffusion characteristics analysis and optimization of battery thermal management system design. Electrochemical model parameter estimation typically requires the use of metaheuristic algorithms, but further research and strengthening are needed to select appropriate metaheuristic algorithms. Important methods to enhance state of charge parameter estimation performance include deep learning networks with attention mechanisms and metaheuristic algorithm optimization of network hyperparameters.
Keywords: lithium-ion battery; thermal runaway; electrochemical model parameters; state of charge
2024, 50(6):1-9,34 收稿日期: 2024-04-10;收到修改稿日期: 2024-05-06
基金项目: 广东省重点领域研发计划项目(2019B090908003)
作者简介: 李远茂(1991-),男,广东高州市人,博士研究生,研究方向为智能检测与仪器。
参考文献
[1] 于智斌, 田易之. 由MIEKPF-EKPF算法协同估计锂离子电池SOC与SOH[J]. 电池, 2023, 53(2): 160-164.
YU Z B, TIAN Y Z. Collaborative estimation of SOC and SOH of Li-ion battery by MIEKPF-EKPF algorithm[J]. Battery Bimonthly, 2023, 53(2): 160-164.
[2] 李杰, 贾长旺, 成林海, 等. 随机路面下轮毂电机偏心对电动汽车平顺性影响[J]. 东北大学学报(自然科学版), 2022, 43(8): 1113-1119.
LI J, JIA C W, CHENG L H, et al. Influence of in-wheel motors' eccentricity on ride comfort of electric vehicles on random roads[J]. Journal of Northeastern University( Natural Science), 2022, 43(8): 1113-1119.
[3] 张雷, 刘青松, 王震坡. 基于鲁棒积分滑模的四轮轮毂电机驱动电动汽车电液复合制动防抱死控制研究[J]. 机械工程学报, 2022, 58(24): 243-253.
ZHANG L, LIU Q S, WANG Z B. Research on electro-hydraulic composite ABS control for four-wheel-independent-drive electric vehicles based on robust integral sliding mode control[J]. Journal of Mechanical Engineering, 2022, 58(24): 243-253.
[4] 蔡家富, 刘桂雄. 动力锂电池模组多工位多电性能参数测试调度方法研究[J]. 中国测试, 2022, 48(1): 9-13.
CAI J F, LIU G X. Research on multi station and multi electric performance parameter test scheduling method of power lithium battery module[J]. China Measurement & Test, 2022, 48(1): 9-13.
[5] 高韶君, 刘伟峰, 符冬菊, 等. 废旧动力锂离子电池全组分回收技术研究进展(英文)[J]. 新型炭材料, 2022, 37(3): 435-460.
GAO S J, LIU W F, FU D J, et al. Research progress on recovering the components of spent li-ion batteries[J]. New Carbon Materials, 2022, 37(3): 435-460.
[6] 文茹馨, 刘惠颖, 梁言贺, 等. Vi-RNN算法储能电池在线SOC估计[J]. 中国测试, 2023, 49(5): 117-122.
WEN R W, LIU H Y, LIANG Y H, et al. Online SOC estimation of energy storage lithium battery based on Vi-RNN algorithm[J]. China Measurement & Test, 2023, 49(5): 117-122.
[7] 赵沁峰, 蔡艳平, 王新军. 基于WOA-ELM的锂离子电池剩余寿命间接预测[J]. 中国测试, 2021, 47(9): 138-145.
ZHAO Q F, CAI Y P, WANG X J. WOA-ELM based indirect prediction of remaining useful life of lithium-ion battery[J]. China Measurement & Test, 2021, 47(9): 138-145.
[8] 魏仁川, 许金鑫, 丁炯. 锂离子电池热参数快速测量方法研究 [J/OL]. 中国测试, 1-7[2024-06-11]. http://kns.cnki.net/kcms/detail/51.1714.TB.20230804.1026.014.html.
WEI R C, XU J X, DING J. Research on rapid measurement of thermal parameters of lithium-ion battery[J/OL]. China Measurement and Test, 1-7[2024-06-11]. http://kns.cnki.net/kcms/detail/51.1714.TB.20230804.1026.014.html.
[9] 朱晓庆, 王震坡, WANG H, 等. 锂离子动力电池热失控与安全管理研究综述[J]. 机械工程学报, 2020, 56(14): 91-118.
ZHU X Q, WANG Z P, WANG H. Review of thermal runaway and safety management for lithium-ion traction batteries in electric vehicles[J]. Journal of Mechanical Engineering, 2020, 56(14): 91-118.
[10] 张亚军, 王贺武, 冯旭宁, 等. 动力锂离子电池热失控燃烧特性研究进展[J]. 机械工程学报, 2019, 55(20): 17-27.
ZHANG Y J, WANG H B, FENG X N, et al. Research progress on thermal runaway combustion characteristics of power lithiumion batteries[J]. Journal of Mechanical Engineering, 2019, 55(20): 17-27.
[11] OYEWOLE I, CHEHADE A, KIM Y. A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation[J]. Applied Energy, 2022, 312.
[12] 王义军, 左雪. 锂离子电池荷电状态估算方法及其应用场景综述[J]. 电力系统自动化, 2022, 46(14): 193-207.
WANG Y J, ZUO X. A Review of charging state estimation methods and application scenarios of lithium-ion batteries[J]. Automation of Electric Power Systems, 2022, 46(14): 193-207.
[13] 赵杨梅, 时玮, 张雪楠, 等. 考虑石墨嵌锂平台的磷酸铁锂电池电化学参数辨识[J/OL]. 电源学报, 1-14[2024-06-11].http://kns.cnki.net/kcms/detail/12.1420.TM.20220223.1844.009.html.
ZHAO Y M, SHI W, ZHANG X N, et al. Identification of electrochemical parameters of lithium iron phosphate battery considering lithium insertion platform of graphite[J/OL]. Journal of Power Supply, 1-14[2024-06-11].http://kns.cnki.net/kcms/detail/12.1420.TM.20220223.1844.009.html.
[14] 陈洪涛. 锂电池电化学模型参数辨识研究[D]. 北京: 北京交通大学, 2019.
CHEN H T. Study on parameters identification of electrochemical model of lithium battery[D]. Beijing: Beijing Jiaotong University, 2019.
[15] HATCHARD T D, MACNEIL D D, BASU A, et al. Thermal model of cylindrical and prismatic lithium-ion cells[J]. Journal of The Electrochemical Society, 2001, 148(7): A755-A761.
[16] KIM G H, PESARAN A, SPOTNITZ R. A three-dimensional thermal abuse model for lithium-ion cells[J]. Journal of Power Sources, 2007, 170(2): 476-489.
[17] LOPEZ C F, JEEVARAJAN J A, MUKHERJEE P P. Characterization of Lithium-ion battery thermal abuse behavior using experimental and computational analysis[J]. Journal of The Electrochemical Society, 2015, 162(10): A2163-A2173.
[18] SHAH K, CHALISE D, JAIN A. Experimental and theoretical analysis of a method to predict thermal runaway in Li-ion cells[J]. Journal of Power Sources, 2016, 330: 167-174.
[19] LEI Z, MAO T Z, XIAO M X, et al. Thermal runaway characteristics on NCM lithium-ion batteries triggered by local heating under different heat dissipation conditions[J]. Applied Thermal Engineering, 2019, 159: 113847.
[20] KONG D P, WANG G Q, PING P, et al. Numerical investigation of thermal runaway behavior of lithium-ion batteries with different battery materials and heating conditions[J]. Applied Thermal Engineering, 2021, 189: 116661.
[21] GHALKHANI M, BAHIRAEI F, NAZRI G A, et al. Electrochemical–thermal model of pouch-type lithium-ion batteries[J]. Electrochimica Acta, 2017, 247: 569-587.
[22] CHIEW J, CHIN C, TOH W, et al. A pseudo three-dimensional electrochemical-thermal model of a cylindrical LiFePO4/graphite battery[J]. Applied Thermal Engineering, 2019, 147: 450-463.
[23] HE C X, YUE Q L, WU M C, et al. A 3D electrochemical-thermal coupled model for electrochemical and thermal analysis of pouch-type lithium-ion batteries[J]. International Journal of Heat and Mass Transfer, 2021, 181: 121855.
[24] HE T, ZHANG T, WANG Z, et al. A comprehensive numerical study on electrochemical-thermal models of a cylindrical lithium-ion battery during discharge process[J]. Applied Energy, 2022, 313: 118797.
[25] 金星, 苗西朋. 锂离子动力电池充放电热跟踪系统设计[J]. 中国测试, 2024, 50(1): 122-127.
JIN X, MIAO X P. Design of heat tracking system for charge and discharge of lithium-ion power battery[J]. China Measurement & Test, 2024, 50(1): 122-127.
[26] 李伟, 刘桂雄. 电化学与热滥用耦合模型的锂电池局部热扩散仿真试验技术[J]. 中国测试, 2021, 47(12): 157-162.
LI W, LIU G X. Lithium battery local thermal dispersion simulation test technology based on coupled electrochemical and thermal abuse models[J]. China Measurement & Test, 2021, 47(12): 157-162.
[27] DONG T, PENG P, JIANG F M. Numerical modeling and analysis of the thermal behavior of NCM lithium-ion batteries subjected to very high C-rate discharge/charge operations[J]. International Journal of Heat and Mass Transfer, 2018, 117: 261-272.
[28] 牛凯, 李静如, 李旭晨, 等. 电化学测试技术在锂离子电池中的应用研究[J]. 中国测试, 2020, 46(7): 90-101.
NIU K, LI J R, LI X C, et al. Research on the applications of electrochemical measurement technologies in lithium-ion batteries[J]. China Measurement & Test, 2020, 46(7): 90-101.
[29] JOKAR A, RAJABLOO B, DÉSILETS M, et al. An inverse method for estimating the electrochemical parameters of lithium-ion batteries[J]. Journal of The Electrochemical Society, 2016, 163(14): A2876-A2886.
[30] FAN G. Systematic parameter identification of a control-oriented electrochemical battery model and its application for state of charge estimation at various operating conditions[J]. Journal of Power Sources, 2020, 470: 228153.
[31] KOSTETZER L, NEBL C, STICH M, et al. Physicsbased modeling and parameter identification for lithium ion batteries under high current discharge conditions[J]. Journal of The Electrochemical Society, 2020, 167(14): 140549.
[32] LI W, DEMIR I, CAO D, et al. Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence[J]. Energy Storage Materials, 2022, 44: 557-570.
[33] SHUI Z Y, LI X H, FENG Y, et al. Combining reduced-order model with data-driven model for parameter estimation of lithium-ion battery[J]. IEEE Transactions on Industrial Electronics, 2023, 70(2): 1521-1531.
[34] KIM M, CHUN H, KIM J, et al. Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search[J]. Applied Energy, 2019, 254: 113644.
[35] XU L, LIN X, XIE Y, et al. Enabling high-fidelity electrochemical P2D modeling of lithium-ion batteries via fast and non-destructive parameter identification[J]. Energy Storage Materials, 2022, 45: 952-968.
[36] 李泓沛, 刘桂雄. 加窗LSTM网络的动力电池SOC预测优化算法[J]. 中国测试, 2021, 47(12): 87-91.
LI H P, LIU G X. SOC prediction optimization algorithm for power battery based on LSTM network with window function[J]. China Measurement & Test, 2021, 47(12): 87-91.
[37] 王子毅, 朱承治, 周杨林, 等. 基于动态可重构电池网络的OCV-SOC在线估计[J]. 中国电机工程学报, 2022, 42(8): 2919-2929.
WANG Z Y, ZHU C Z, ZHOU Y L, et al. OCV-SOC estimation based on dynamic reconfigurable battery network[J]. Proceedings of the CSEE, 2022, 42(8): 2919-2929.
[38] 邢厚超. 基于修正开路电压-安时积分法的电池管理系统研究[D]. 盐城: 盐城工学院, 2022.
XING H C. Research on battery management system based on modified open circuit voltage-ampere hour integration method[D]. Yancheng: Yancheng Institute of Technology, 2022.
[39] TIAN Y, LI D, TIAN J, et al. State of charge estimation of lithium-ion batteries using an optimal adaptive gain nonlinear observer[J]. Electrochimica Acta, 2017, 225: 225-234.
[40] SHRIVASTAVA P, SOON T K, IDRIS M Y I B, et al. Combined state of charge and state of energy estimation of lithium-ion battery using dual forgetting factor-based adaptive extended Kalman filter for electric vehicle applications[J]. IEEE Transactions on Vehicular Technology, 2021, 70(2): 1200-1215.
[41] SUN D, YU X, WANG C, 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.
[42] SAKILE R, SINHA U K. Lithium-ion battery state of charge estimation using a new extended nonlinear state observer[J]. Advanced Theory and Simulations, 2022, 5(3): 2100552.
[43] CHEN L P, GUO W L, LOPES A M, et al. State-of-charge estimation for lithium-ion batteries based on incommensurate fractional-order observer[J]. Communications in Nonlinear Science and Numerical Simulation, 2023, 118.
[44] YANG F, ZHANG S, LI W, et al. State-of-charge estimation of lithium-ion batteries using LSTM and UKF[J]. Energy, 2020, 201.
[45] MAMO T, WANG F K. Long short-term memory with attention mechanism for state of charge estimation of Lithium-Ion batteries[J]. IEEE Access, 2020, 8: 94140-94151.
[46] REN X, LIU S, YU X, et al. A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM[J]. Energy, 2021, 234.
[47] HANNAN M A, HOW D N T, LIPU M S H, et al. Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model[J]. Scientific Reports, 2021, 11(1): 19541.
[48] YANG B, WANG Y, ZHAN Y. Lithium battery state-of-charge estimation based on a Bayesian optimization bidirectional long short-term memory neural network[J]. Energies, 2022, 15(13).
[49] LI R, WANG H, DAI H, et al. Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network[J]. Energy, 2022, 250.
[50] YAN X, ZHOU G, WANG W, et al. A hybrid data-driven method for state-of-charge estimation of Lithium-ion batteries[J]. IEEE Sensors Journal, 2022, 22(16): 16263-16275.
[51] 李泓沛, 刘桂雄, 邓威. 基于LSTM+UKF融合的动力锂电池SOC估算方法[J]. 中国测试, 2022, 48(8): 22-28.
LI H P, LIU G X, DENG W. LSTM+UKF fusion-based SOC estimation method for powered Lithium batteries[J]. China Measurement & Test, 2022, 48(8): 22-28.
[52] 李小虎, 王军. 基于改进无迹卡尔曼滤波器的锂电池荷电状态估计[J]. 中国测试, 2023, 49: 105-110+130.
LI X H, WANG J. SOC estimation of Lithium battery based on improved unscented Kalman filter[J]. China Measurement & Test, 2023, 49: 105-110+130.
[53] 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 M K, XU H L, WU M B. Review of SOC estimation methods for power battery based on equivalent circuit model[J]. Journal of Electrical Engineering, 2021, 16(1): 90-102.