锂离子电池系统温度场测量、感知与重构研究现状及关键问题

doi:10.19595/j.cnki.1000-6753.tces.251488

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (7185 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Lithium-ion batteries have become the core energy carriers in new energy vehicles and large-scale energy storage systems due to their high energy density, long cycle life, and rapid response characteristics. However, inadequate temperature monitoring and sensing capabilities under complex and harsh operating conditions severely constrain system thermal safety and operational stability. Temperature field reconstruction serves as a critical link between localized discrete temperature measurements and the global continuous temperature field. By deeply integrating multi-source temperature measurement data, high-fidelity mechanistic models, and advanced intelligent algorithms, this technology enables high-precision inversion and dynamic modeling of the spatial-temporal distribution characteristics of the battery's global temperature field, supporting accurate monitoring of thermal runaway. This paper presents a systematic review of recent advances in battery temperature monitoring technologies through a unified three-tier framework: temperature measurement, temperature perception, and temperature field reconstruction. In temperature measurement, non-contact and contact methods—the two mainstream approaches—are comparatively analyzed. Non-contact techniques, such as infrared thermometers, infrared thermal imaging, and fiber-optic non-contact thermometry, offer rapid, long-distance sensing suitable for harsh environments but are susceptible to interference from ambient radiation, surface emissivity variations, and other environmental factors. Contact-based methods are categorized into wired active sensors (e.g., fiber-optic sensors, thermocouples, and resistance temperature detectors) and wireless passive sensors (e.g., surface acoustic wave surface acoustic wave (SAW) sensors, LC resonant sensors, and radio frequency identification (RFID)-based temperature sensors). Wired active sensors provide high accuracy and fast response, whereas wireless passive types require no wiring or external power supply, making them well-suited for enclosed or structurally complex battery configurations. Both categories face challenges including limited spatial deployment density and insufficient long-term stability. In temperature perception, four representative methodologies are examined. The electrochemical-thermal coupling model integrates multi-physics equations to accurately describe the interaction between electrochemical reactions and thermal dynamics; following model order reduction—such as partial differential equation (PDE)-based reduced-order models (ROMs) and proper orthogonal decomposition (POD)—both computational efficiency and prediction accuracy are improved. The electro-thermal coupling model solves equivalent circuit networks and thermal models in a coupled manner; when enhanced with algorithms such as Kalman filtering, it achieves an average temperature estimation error as low as ±0.2℃. The electrochemical impedance spectroscopy (EIS) method estimates temperature by analyzing the mapping relationship between impedance spectra and thermal state; after characteristic frequency selection and fusion with machine learning algorithms, its error is typically controlled within ±0.8℃ to ±1.5℃. Data-driven approaches leverage the strong nonlinear mapping capability of neural networks to achieve accurate temperature estimation from a small number of measurement points, with reported average errors as low as ±0.1℃. Regarding temperature field reconstruction, two algorithmic paradigms are summarized: data-driven and data-mechanism joint-driven methods. Data-driven models—including back-propagation (BP) neural networks, gated recurrent units (GRUs), and long short-term memory (LSTM) networks—reconstruct the global temperature field using limited measurement data, yielding average errors in the range of ±0.17℃ to ±1.5℃; the incorporation of transfer learning helps reduce training time. Data-mechanism joint-driven approaches—such as PDE-informed deep operator networks (PDE-Deep ONet), Kalman filter-integrated models, and the Thermal Reconstruction via Constrained Minimization (TRCM) algorithm—combine physical constraints with data-fitting capabilities, achieving reconstruction errors as low as ±0.0761℃. By organizing existing research within the measurement-perception-reconstruction framework, this review identifies key technical bottlenecks and synthesizes core optimization pathways across diverse methodologies, offering a structured reference for advancing precision thermal management in lithium-ion battery systems.

Key words： Lithium-ion battery temperature measurement temperature sensing temperature field reconstruction

Received: 25 August 2025

PACS:

TM911

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Xu Zhicheng
	Lu Yichun
	Zhang Xian
	Bai Yaping
	Jiang Kai

Cite this article:

Xu Zhicheng,Lu Yichun,Zhang Xian等. Research Status and Key Issues of Temperature Field Measurement, Sensing and Reconstruction for Lithium-Ion Battery System[J]. Transactions of China Electrotechnical Society, 2026, 41(17): 6053-6072.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.251488 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I17/6053

[1] Abo-Khalil A G, Ali Abdelkareem M, Sayed E T, et al. Electric vehicle impact on energy industry, policy, technical barriers, and power systems[J]. International Journal of Thermofluids, 2022, 13: 100134.
[2] Olabi A G, Wilberforce T, Sayed E T, et al.Battery energy storage systems and SWOT (strengths, weakness, opportunities, and threats) analysis of batteries in power transmission[J]. Energy, 2022, 254: 123987.
[3] 徐志成, 杨达, 张闯, 等. 基于极化衰减特征与通道关注混合神经网络的锂离子电池容量在线估计[J]. 电工技术学报, 2025, 40(17): 5683-5702.
Xu Zhicheng, Yang Da, Zhang Chuang, et al.Online estimation of lithium-ion battery capacity based on polarization decay feature and hybrid neural network with channel attention[J]. Transactions of China Electrotechnical Society, 2025, 40(17): 5683-5702.
[4] 徐志成, 张弘毅, 田金鑫, 等. 锂离子电池电化学-热耦合模型场域系统参数随动态工况的敏感性量化评估[J]. 电工技术学报, 2025, 40(20): 6716-6732.
Xu Zhicheng, Zhang Hongyi, Tian Jinxin, et al.Quantitative assessment of the field system parameter sensitivity for lithium-ion battery electrochemical-thermal coupling model with dynamic operating conditions[J]. Transactions of China Electrotechnical Society, 2025, 40(20): 6716-6732.
[5] Hu Tong, Ma Fei, Xu Xiaoming.Analysis of thermal runaway propagation characteristics of lithium-ion battery module under local high temperature[J]. High Temperatures-High Pressures, 2022, 51(3): 195-212.
[6] Zhang Fangshu, Feng Xuning, Xu Chengshan, et al.Thermal runaway front in failure propagation of long-shape lithium-ion battery[J]. International Journal of Heat and Mass Transfer, 2022, 182: 121928.
[7] Janakiraman U, Garrick T R, Fortier M E.Review: lithium plating detection methods in Li-ion batteries[J]. Journal of the Electrochemical Society, 2020, 167(16): 160552.
[8] Lü Shuaishuai, Wang Xingxing, Lu Wenfan, et al.The influence of temperature on the capacity of lithium ion batteries with different anodes[J]. Energies, 2022, 15(1): 60.
[9] Wei Lisi, Liu Huanling, Tang Chuangeng, et al.Investigation of novel type of cylindrical lithium-ion battery heat exchangers based on topology optimization[J]. Energy, 2024, 304: 131886.
[10] Sheng Lei, Zhang Hengyun, Zhang Hua, et al.Lightweight liquid cooling based thermal management to a prismatic hard-cased lithium-ion battery[J]. International Journal of Heat and Mass Transfer, 2021, 170: 120998.
[11] Nagpure S C, Dinwiddie R, Babu S S, et al.Thermal diffusivity study of aged Li-ion batteries using flash method[J]. Journal of Power Sources, 2010, 195(3): 872-876.
[12] 钟国彬, 王羽平, 王超, 等. 大容量锂离子电池储能系统的热管理技术现状分析[J]. 储能科学与技术, 2018, 7(2): 203-210.
Zhong Guobin, Wang Yuping, Wang Chao, et al.The review of thermal management technology for large-scale lithium-ion battery energy storage system[J]. Energy Storage Science and Technology, 2018, 7(2): 203-210.
[13] Goh N W, Poh J J, Yeo J Y, et al.Design and development of a low cost, non-contact infrared thermometer with range compensation[J]. Sensors, 2021, 21(11): 3817.
[14] Mikolajek M, Martinek R, Koziorek J, et al.Temperature measurement using optical fiber methods: overview and evaluation[J]. Journal of Sensors, 2020, 2020(1): 8831332.
[15] Yen Kee Y, Asako Y, Lit Ken T, et al.Uncertainty of temperature measured by thermocouple[J]. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 2020, 68(1): 54-62.
[16] Chen Jie, Xu Xiangfan, Zhou Jun, et al.Interfacial thermal resistance: past, present, and future[J]. Reviews of Modern Physics, 2022, 94(2): 025002.
[17] Länge K, Rapp B E, Rapp M.Surface acoustic wave biosensors: a review[J]. Analytical and Bioanalytical Chemistry, 2008, 391(5): 1509-1519.
[18] 刘佳明, 陈红, 王广军, 等. 基于扩展状态空间模型的锂离子电池内热源场和温度场同时重构[J]. 工程热物理学报, 2024, 45(2): 478-487.
Liu Jiaming, Chen Hong, Wang Guangjun, et al.Simultaneous reconstruction of heat source field and temperature field in lithium ion battery based on extended state space model[J]. Journal of Engineering Thermophysics, 2024, 45(2): 478-487.
[19] Liu Zeyu, Xiong Chengfeng, Du Xiaofang.Study on real-time battery temperature prediction based on coupling of multiphysics fields and temporal networks[J]. IEEE Access, 2024, 12: 105511-105526.
[20] Zhou Yu, Deng Hua, Li Hanxiong, et al.Data-driven real-time prediction of pouch cell temperature field under minimal sensing[J]. IEEE Transactions on Transportation Electrification, 2023, 9(1): 1034-1041.
[21] Wang Xin, Cao Zhiyin, Liu Yuhong, et al.Temperature reconstruction in data centers using gappy pod combined with genetic algorithm and optimization of sensor layout[J]. Energy and Buildings, 2025, 326: 115078.
[22] Wang Yong, He Shihui, Wang Bingchuan.Evolutionary sensor placement for spatiotemporal modeling of battery thermal process[J]. IEEE Transactions on Industrial Informatics, 2022, 18(4): 2223-2232.
[23] 郭杰, 张华. 非接触式红外测温仪在变电站的应用[J]. 硅谷, 2015, 8(2): 95-95, 122.
Guo Jie, Zhang Hua.Application of non-contact infrared thermometer in substation[J]. Silicon Valley, 2015, 8(2): 95-95, 122.
[24] Dileep H, Jha K K, Mahapatra P S, et al.Thermal characterization of pouch cell using infrared thermography and electrochemical modelling for the design of effective battery thermal management system[J]. Applied Energy, 2024, 376: 124301.
[25] Stoynova A, Bonev B, Rizanov S.Thermographic study of thermal processes during battery charging and discharging[C]//2021 44th International Spring Seminar on Electronics Technology (ISSE), Bautzen, Germany, 2021: 1-6.
[26] 张凯, 李正国, 张强. 红外测温技术在动力电池热管理系统中的应用[J]. 汽车工业研究, 2013(4): 50-53.
Zhang Kai, Li Zhengguo, Zhang Qiang.Application of infrared temperature measurement technology in thermal management system of power battery[J]. Auto Industry Research, 2013(4): 50-53.
[27] 刘婕, 路桂娟. 消防用锂电池研究与分析[J]. 电源技术, 2016, 40(8): 1588-1589.
Liu Jie, Lu Guijuan.Research and analysis of lithium batteries for fire fighting apparatus[J]. Chinese Journal of Power Sources, 2016, 40(8): 1588-1589.
[28] Li Yitian, Pan Dong, Jiang Zhaohui, et al.Adaptive compensation method for the infrared temperature measurement error based on 3-D thermal imaging[J]. IEEE Sensors Journal, 2023, 23(10): 10525-10537.
[29] 王黎明, 付铠玮, 梅红伟, 等. 环境湿度对复合绝缘子红外测温的影响[J]. 高电压技术, 2019, 45(6): 1955-1961.
Wang Liming, Fu Kaiwei, Mei Hongwei, et al.Influence of environmental humidity on infrared measurement temperature of composite insulators[J]. High Voltage Engineering, 2019, 45(6): 1955-1961.
[30] 郑涛, 王瑞圳, 张红. 基于图像处理的全钒液流电池红外测温方法[J]. 电源技术, 2019, 43(9): 1499-1502.
Zheng Tao, Wang Ruizhen, Zhang Hong.Infrared temperature measurement of VRB system based on image processing[J]. Chinese Journal of Power Sources, 2019, 43(9): 1499-1502.
[31] Giulietti N, Chiariotti P, Zanelli F, et al.Combined use of infrared imaging and deep-learning techniques for real-time temperature measurement of train braking components[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 5009908.
[32] Pan Dong, Jiang Zhaohui, Li Yitian, et al.Intelligent compensation method of infrared temperature measurement for multiple interference factors[J]. IEEE Sensors Journal, 2022, 22(19): 18550-18559.
[33] Wang Jianguo, Ou Jianhua, Fan Yadong, et al.Online monitoring of electrical equipment condition based on infrared image temperature data visualization[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2022, 17(4): 583-591.
[34] Jia Shaoyu, Zhang Sa, Cai Meiyu, et al.Fiber-optic temperature sensing system with extended measurement range via Sagnac interferometer and FBG-integrated fiber ring laser[J]. Optics Communications, 2025: 132617.
[35] Huang Jiaqiang, Albero Blanquer L, Bonefacino J, et al.Operando decoding of chemical and thermal events in commercial Na(Li)-ion cells via optical sensors[J]. Nature Energy, 2020, 5(9): 674-683.
[36] Huang Jiaqiang, Han Xile, Liu Fu, et al.Monitoring battery electrolyte chemistry via in-operando tilted fiber Bragg grating sensors[J]. Energy & Environ-mental Science, 2021, 14(12): 6464-6475.
[37] Sommer L W, Raghavan A, Kiesel P, et al.Embedded fiber optic sensing for accurate state estimation in advanced battery management systems[J]. MRS Online Proceedings Library, 2014, 1681(1): 1-7.
[38] Mei Wenxin, Liu Zhi, Wang Chengdong, et al.Operando monitoring of thermal runaway in commercial lithium-ion cells via advanced lab-on-fiber technologies[J]. Nature Communications, 2023, 14: 5251.
[39] Niu Rongrong, Liu Qingwen, Deng Yuanpeng, et al.Quasi-distributed fiber-optic sensor with large measurement range for both static and dynamic signals[J]. Journal of Lightwave Technology, 2024, 43(3): 1437-1444.
[40] 梁大巍, 黄尚廉. 分布式光纤温度传感器的研究现状及发展[J]. 应用光学, 1991,12(2): 1-6.
Liang Dawei, Huang Shanglian.Research status and development of distributed optical-fiber temperature sensor[J]. Journal of Applied Optics, 1991, 12(2): 1-6.
[41] 赵明富, 谭睿, 钟年丙, 等. 准分布式光纤Bragg光栅传感器在微生物燃料电池温度场测量中的应用[J]. 激光杂志, 2015,36(2): 78-80.
Zhao Mingfu, Tan Rui, Zhong Nianbing, et al.Quasi-distributed optical fiber Bragg grating sensing system in the application of the measurement of the microbial fuel cell temperature field[J]. Laser Journal, 2015, 36(2): 78-80.
[42] Nascimento M, Ferreira M S, Pinto J L.Temperature fiber sensing of Li-ion batteries under different environmental and operating conditions[J]. Applied Thermal Engineering, 2019, 149: 1236-1243.
[43] Xia Xudong, Wu Wen, Li Zhencheng, et al.State of charge estimation for commercial Li-ion battery based on simultaneously strain and temperature monitoring over optical fiber sensors[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 2516411.
[44] Korganbayev S, De Landro M, Morra F, et al.Fiber optic sensors for distributed and quasi-distributed temperature measurement[C]//2020 IEEE SENSORS, Rotterdam, Netherlands, 2020: 1-4.
[45] 匡绍龙, 朱学斌. 分布式光纤温度传感器原理及其在变电站温度监测中的应用[J]. 电力自动化设备, 2004,24(9): 79-81.
Kuang Shaolong, Zhu Xuebin.Principle of distributed optical fiber temperature sensor and its application in substation temperature monitoring[J]. Electric Power Automation Equipment, 2004, 24(9): 79-81.
[46] Yu Yifei, Vergori E, Worwood D, et al.Distributed thermal monitoring of lithium ion batteries with optical fibre sensors[J]. Journal of Energy Storage, 2021, 39: 102560.
[47] 谢良康, 马国轩, 李炜, 等. 基于SVD的BOTDA测温系统的解调算法研究[J]. 测控技术, 2023, 42(11): 13-17, 31.
Xie Liangkang, Ma Guoxuan, Li Wei, et al.Research on demodulation algorithm of BOTDA temperature measurement system based on SVD[J]. Measurement & Control Technology, 2023, 42(11): 13-17, 31.
[48] 刘洋, 吴双, 赵永刚. 热电偶温度传感器的研究与发展现状[J]. 中国仪器仪表, 2003(11): 1-3.
Liu Yang, Wu Shuang, Zhao Yonggang.Research and developing status on temperature sensor of thermocouple[J]. China Instrumentation, 2003(11): 1-3.
[49] Yang Yang, Raymand D, Brandell D.A cost-effective alternative to accelerating rate calorimetry: analyzing thermal runaways of lithium-ion batteries through thermocouples[J]. Journal of Power Sources, 2024, 612: 234807.
[50] Lee S J, Lee C Y, Chung M Y, et al.Lithium-ion battery module temperature monitoring by using planer home-made micro thermocouples[J]. International Journal of Electrochemical Science, 2013, 8(3): 4131-4141.
[51] Mutyala M S K, Zhao Jingzhou, Li Jianyang, et al. In-situ temperature measurement in lithium ion battery by transferable flexible thin film thermocouples[J]. Journal of Power Sources, 2014, 260: 43-49.
[52] Forgez C, Vinh Do D, Friedrich G, et al.Thermal modeling of a cylindrical LiFePO₄/graphite lithium-ion battery[J]. Journal of Power Sources, 2010, 195(9): 2961-2968.
[53] Koshkouei M J, Fereshteh Saniee N, Barai A.Thermocouple selection and its influence on temperature monitoring of lithium-ion cells[J]. Journal of Energy Storage, 2024, 92: 112072.
[54] Guo Dengji, Li Weichang, Pan Taisong, et al.Implantable flexible temperature sensor for in-operando sensing of Lithium-ion batteries[C]//2023 24th International Conference on Electronic Packaging Technology (ICEPT), Shihezi City, China, 2024: 1-4.
[55] Pan Taisong, Zheng Xunbo, Guo Dengji, et al.Operando monitoring of internal temperature in lithium-ion battery enabled by wireless implantable sensor with ultrasonic communication[J]. Journal of Energy Storage, 2024, 101: 113977.
[56] Lei Liang, Dong Bo, Hu Yuxuan, et al.High-frequency surface acoustic wave resonator with diamond/AlN/IDT/AlN/diamond multilayer structure[J]. Sensors, 2022, 22(17): 6479.
[57] 崔柏乐, 杨启明, 王文. 声表面波气体传感器研究进展[J]. 压电与声光, 2024, 46(6): 827-839.
Cui Baile, Yang Qiming, Wang Wen.Review of surface acoustic wave-based gas sensor[J]. Piezoelectrics & Acoustooptics, 2024, 46(6): 827-839.
[58] Plessky V P, Reindl L M.Review on SAW RFID tags[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2010, 57(3): 654-668.
[59] Hauser R, Fachberger R, Bruckner G, et al.A wireless SAW-based temperature sensor for harsh environment[C]//SENSORS, 2004 IEEE, Vienna, Austria, 2005: 860-863.
[60] Reindl L, Shrena I, Kenshil S, et al.Wireless measurement of temperature using surface acoustic waves sensors[C]//IEEE International Frequency Control Symposium and PDA Exhibition Jointly with the 17th European Frequency and Time Forum, 2003, Tampa, FL, USA, 2004: 935-941.
[61] Stelzer A, Scheiblhofer S, Schuster S, et al.Wireless sensor marking and temperature measurement with SAW-identification tags[J]. Measurement, 2008, 41(5): 579-588.
[62] Rodriguez L M, Gallagher D R, Gallagher M W, et al.Wireless SAW sensor temperature extraction precision[J]. IEEE Sensors Journal, 2014, 14(11): 3830-3837.
[63] Qu Yuanhang, Chen Xiang, Liu Yan, et al.Novel AlN/ScAlN composite film SAW for achieving highly sensitive temperature sensors[J]. Sensors and Actuators A: Physical, 2025, 381: 116079.
[64] Zhou Xuhang, Tan Qiulin, Liang Xiaorui, et al.Novel multilayer SAW temperature sensor for ultra-high temperature environments[J]. Micromachines, 2021, 12(6): 643.
[65] Mustafa M, Rizwan M, Kashif M, et al.LC passive wireless sensor system based on two switches for detection of triple parameters[J]. Sensors, 2022, 22(8): 3024.
[66] Zhang Guangjin, Tan Qiulin, Lin Baimao, et al.A novel temperature and pressure measuring scheme based on LC sensor for ultra-high temperature environment[J]. IEEE Access, 2019, 7: 162747-162755.
[67] De Hennis A D, Wise K D. A wireless microsystem for the remote sensing of pressure, temperature, and relative humidity[J]. Journal of Microelectromechanical Systems, 2005, 14(1): 12-22.
[68] Ren Qingying, Wang Lifeng, Huang Jianqiu, et al.Simultaneous remote sensing of temperature and humidity by LC-type passive wireless sensors[J]. Journal of Microelectromechanical Systems, 2015, 24(4): 1117-1123.
[69] Wang Ya, Jia Yi, Chen Qiushui, et al.A passive wireless temperature sensor for harsh environment applications[J]. Sensors, 2008, 8(12): 7982-7995.
[70] Rida A, Vyas R, Wu T, et al.Development and implementation of novel UHF paper-based RFID designs for anti-counterfeiting and security applications[C]//2007 International Workshop on Anti-Counterfeiting, Security and Identification (ASID), Xizmen, China, 2007: 52-56.
[71] Zhu Wenge, Zhang Qi, Matlin M, et al.Passive digital sensing method and its implementation on passive RFID temperature sensors[J]. IEEE Sensors Journal, 2021, 21(4): 4793-4800.
[72] Girbau D, Ramos Á, Lazaro A, et al.Passive wireless temperature sensor based on time-coded UWB chipless RFID tags[J]. IEEE Transactions on Microwave Theory and Techniques, 2012, 60(11): 3623-3632.
[73] Pradhan S, Qiu Lili.RTSense: passive RFID based temperature sensing[C]//Proceedings of the 18th Conference on Embedded Networked Sensor Systems, Japan, 2020: 42-55.
[74] Kang Along, Zhang Chenrui, Ji Xiaojun, et al.SAW-RFID enabled temperature sensor[J]. Sensors and Actuators A: Physical, 2013, 201: 105-113.
[75] Gao Xu, Cheng Lina, Xue Xufeng, et al.Development of wireless and passive SAW temperature sensor with very high accuracy[J]. Applied Sciences, 2021, 11(16): 7422.
[76] 王雅. 面向旋转环境测试的无线无源温度-应变双参数传感器研究[D]. 太原: 中北大学, 2021.
Wang Ya.Research on wireless passive temperature and strain dual-parameter sensor for rotating environment testing[D]. Taiyuan: North University of China, 2021.
[77] 田文卓. 一种适用于RFID标签的高精度温度传感器的分析与设计[J]. 计算机与数字工程, 2013, 41(4): 666-669.
Tian Wenzhuo.A temperature sensor with ultra-high accuracy for RFID tags[J]. Computer & Digital Engineering, 2013, 41(4): 666-669.
[78] 刘嘉豪. 多温度工况下锂离子电池电化学热耦合模型参数辨识与产热行为研究[D]. 西安: 西安理工大学, 2024.
Liu Jiahao.Parameter identification and thermal behavior research of electrochemical-thermal coupling model of Li-ion battery under multi-temperature conditions[D]. Xi’an: Xi’an University of Technology, 2024.
[79] Kumaresan K, Sikha G, White R E.Thermal model for a Li-ion cell[J]. Journal of the Electrochemical Society, 2008, 155(2): A164.
[80] Xu Meng, Zhang Zhuqian, Wang Xia, et al.Two-dimensional electrochemical-thermal coupled modeling of cylindrical LiFePO₄ batteries[J]. Journal of Power Sources, 2014, 256: 233-243.
[81] Xu Meng, Zhang Zhuqian, Wang Xia, et al.A pseudo three-dimensional electrochemical-thermal model of a prismatic LiFePO₄ battery during discharge process[J]. Energy, 2015, 80: 303-317.
[82] Wang Jingrong, Meng Jinhao, Peng Qiao, et al.An electrochemical-thermal coupling model for lithium-ion battery state-of-charge estimation with improve dual particle filter framework[J]. Journal of Energy Storage, 2024, 87: 111473.
[83] Zhao Yihang, Wei Mingshan, Dan Dan, et al.Enhancing battery electrochemical-thermal model accuracy through a hybrid parameter estimation framework[J]. Energy Storage Materials, 2024, 72: 103720.
[84] Pang Hui, Liu Jiahao, Yan Xiangping, et al.Parameterization and heat generation investigation of cylindrical lithium batteries based on a reconstructed electrochemical-thermal coupling model[J]. Applied Thermal Engineering, 2024, 257: 124328.
[85] Yuan Qingyang, Zhang Zhonggui, Zhang Bo.A thermal modeling approach for pouch lithium-ion batteries based on mode-thermal power mapping[J]. Applied Thermal Engineering, 2025, 280: 128296.
[86] Yan Xiangping, Chen Kaiqiang, Pang Hui, et al.State of temperature estimation for a lithium-ion battery cell using adaptive unsent Kalman filter[C]//2024 IEEE 7th International Electrical and Energy Conference (CIEEC), Harbin, China, 2024: 4141-4145.
[87] Wang Yong, Deng Yelin, Liu Weiwei, et al.Thermal analysis for lithium-ion battery pack based on parameter estimation based on genetic algorithm[J]. IOP Conference Series: Materials Science and Engineering, 2020, 793(1): 012015.
[88] Lin Xinfan, Perez H E, Siegel J B, et al.Online parameterization of lumped thermal dynamics in cylindrical lithium ion batteries for core temperature estimation and health monitoring[J]. IEEE Transactions on Control Systems Technology, 2013, 21(5): 1745-1755.
[89] Liu Shichuang, Sun Huanwu, Yu Haotong, et al.A framework for battery temperature estimation based on fractional electro-thermal coupling model[J]. Journal of Energy Storage, 2023, 63: 107042.
[90] Dai Haifeng, Zhu Letao, Zhu Jiangong, et al.Adaptive Kalman filtering based internal temperature estimation with an equivalent electrical network thermal model for hard-cased batteries[J]. Journal of Power Sources, 2015, 293: 351-365.
[91] 常小兵, 侯宗尚, 刘连起, 等. 基于电热耦合效应的锂电池荷电状态与温度状态联合估计[J]. 储能科学与技术, 2024, 13(4): 1142-1153.
Chang Xiaobing, Hou Zongshang, Liu Lianqi, et al.Joint estimation of the state of charge and temperature of lithium batteries based on the electric thermal coupling effect[J]. Energy Storage Science and Technology, 2024, 13(4): 1142-1153.
[92] Zhang Xingchen, Wang Yujie, Chen Zonghai.SoC-modified core temperature estimation of lithium-ion battery based on control-oriented electro-thermal model[J]. IEEE Transactions on Power Electronics, 2023, 38(9): 11642-11651.
[93] Liu Menglin, Zhou Xiaodong, Yang Lizhong, et al.A novel Kalman-filter-based battery internal temperature estimation method based on an enhanced electro-thermal coupling model[J]. Journal of Energy Storage, 2023, 71: 108241.
[94] Ma Shuai, Jiang Modi, Tao Peng, et al.Temperature effect and thermal impact in lithium-ion batteries: a review[J]. Progress in Natural Science: Materials International, 2018, 28(6): 653-666.
[95] Juang L W, Kollmeyer P J, Zhao Ruxiu, et al.The impact of DC bias current on the modeling of lithium iron phosphate and lead-acid batteries observed using electrochemical impedance spectroscopy[C]//2014 IEEE Energy Conversion Congress and Exposition (ECCE), Pittsburgh, PA, USA, 2014: 2575-2581.
[96] Fan Wenjie, Zhang Zhibin, Dong Ming, et al.On-line estimation method for internal temperature of lithium-ion battery based on electrochemical impedance spectroscopy[C]//2021 IEEE Electrical Insulation Conference (EIC), Denver, CO, USA, 2021: 247-251.
[97] Zheng Yusheng, Weinreich N A, Kulkarni A, et al.Sensorless state of temperature estimation for smart battery based on electrochemical impedance[C]//2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), Aalborg, Denmark, 2023: 1-8.
[98] 李强, 杨林, 李超凡, 等. 基于SVR和电化学阻抗谱的锂电池内部温度在线估计[J]. 电源技术, 2024, 48(9): 1738-1746.
Li Qiang, Yang Lin, Li Chaofan, et al.Online estimation of internal temperature of lithium battery based on SVR and electrochemical impedance spectroscopy[J]. Chinese Journal of Power Sources, 2024, 48(9): 1738-1746.
[99] Fang Zheng, Wang Mengyi, Hu Weifeng, et al.Temperature-field sparse-reconstruction of lithium-ion battery pack based on artificial neural network and virtual thermal sensor technology[J]. Energy Technology, 2021, 9(10): 2100258.
[100] Zhao Xinze, Sun Bingxiang, Zhang Weige, et al.Temperature field spatiotemporal modeling of lithium-ion battery pack configured sparse temperature sensors[J]. Journal of Energy Storage, 2024, 97: 113005.
[101] 苑清扬, 薛珂, 张博, 等. Gappy POD算法重构储能电池组核心温度及与BP神经网络预测能力对比[J]. 工程科学学报, 2024, 46(1): 166-177.
Yuan Qingyang, Xue Ke, Zhang Bo, et al.Gappy POD algorithm for reconstructing the core temperature of energy-storage battery packs and its comparison with BP neural network prediction ability[J]. Chinese Journal of Engineering, 2024, 46(1): 166-177.
[102] Han Jiajie, Yuan Qingyang, Li Yu, et al.Real-time temperature prediction of large-scale lithium battery module driven by data based on few measurement points[J]. Chinese Journal of Engineering, 2025, 47(5): 1103-1116.
[103] 徐晶, 丁凡, 王翔, 等. 基于BP神经元网络的锂电池温度预测[J]. 南方农机, 2022, 53(9): 42-44, 53.
Xu Jing, Ding Fan, Wang Xiang, et al.Temperature prediction of lithium battery based on BP neural network[J]. China Southern Agricultural Machinery, 2022, 53(9): 42-44, 53.
[104] Panchal S, Mathew M, Dincer I, et al.Thermal and electrical performance assessments of lithium-ion battery modules for an electric vehicle under actual drive cycles[J]. Electric Power Systems Research, 2018, 163: 18-27.
[105] 陈来恩, 曾小勇, 曾子豪, 等. 结合物理信息与深度神经网络的锂离子电池温度预测[J/OL]. 中国电力, 1-8[2024-11-14].http://kns.cnki.net/kcms/detail/11.3265.tm.20240619.1455.005.html.
Chen Lai’en, Zeng Xiaoyong, Zeng Zihao, et al. Temperature prediction of lithium-ion batteries using a combination of physical information and deep neural networks [J/OL]. China Electric Power, 1-8[2024-11-14]. http://kns.cnki.net/kcms/detail/11.3265.tm.20240619.1455.005.html.
[106] Yao Qi, Lu D D, Lei Gang.A surface temperature estimation method for lithium-ion battery using enhanced GRU-RNN[J]. IEEE Transactions on Transportation Electrification, 2023, 9(1): 1103-1112.
[107] 叶石丰, 洪朝锋, 綦晓, 等.基于EEMD-GRU-NN锂离子电池表面温度预测方法研究[J/OL]. 储能科学与技术, 1-10[2024-11-03].https://doi.org/10.19799/j.cnki.2095-4239.2024.0571.
Ye Shifeng, Hong Chaofeng, Qi Xiao, et al.research on surface temperature prediction method of EEMD-GRU-NN lithium ion battery[J/OL]. Energy Storage Science and Technology, 1-10 [2022-11-03] https://doi.org/10.19799/j.cnki.2095-4239.2024.0571.
[108] Liu Liang, Xu Guangguang, Wang Yun, et al.Battery temperature estimation at wide C-rates using the LSTM model based on polarization characteristics[J]. Journal of Energy Storage, 2024, 101: 113941.
[109] Wang Yuchen, Xiong Can, Ju Changjiang, et al.A deep transfer operator learning method for temperature field reconstruction in a lithium-ion battery pack[J]. IEEE Transactions on Industrial Informatics, 2024, 20(6): 8089-8101.
[110] Chen Hong, Hu Yuchen, Chen Zehong, et al.Direct reconstruction of the temperature field of lithium-ion battery based on mapping characteristics[J]. Measurement, 2024, 234: 114850.
[111] Tian Ning, Fang Huazhen, Wang Yebin.3-D temperature field reconstruction for a lithium-ion battery pack: a distributed Kalman filtering approach[J]. IEEE Transactions on Control Systems Technology, 2019, 27(2): 847-854.
[112] Qi Xiao, Hong Chaofeng, Gu Lijun, et al.Surface temperature field reconstruction of lithium-ion batteries toward lumped thermal model-based KF-MLP estimation algorithm[J]. IEEE Transactions on Industrial Informatics, 2025, 21(6): 4841-4851.
[113] Wang Zhongyu, Wang Guangjun, Chen Hong, et al.Temperature response correlation model between body and coolant and direct reconstruction of temperature field in lithium-ion battery pack[J]. Applied Thermal Engineering, 2025, 278: 127186.
[114] Zhang Tao, Wang Guangjun, Chen Hong, et al.Temperature response spatiotemporal correlation model of lithium-ion battery and temperature field reconstruction during charging and discharging processes[J]. Energy, 2025, 324: 135902.