Research Status and Key Issues of Temperature Field Measurement, Sensing and Reconstruction for Lithium-Ion Battery System
Xu Zhicheng1, Lu Yichun1, Zhang Xian1, Bai Yaping2, Jiang Kai3
1. State Key Laboratory of Intelligent Power Distribution Equipment and System Hebei University of Technology Tianjin 300401 China; 2. Tianjin Zhichu Energy Technology Co. Ltd Tianjin 300308 China; 3. School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan 430074 China
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.
徐志成, 陆艺纯, 张献, 白亚平, 蒋凯. 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[J]. 电工技术学报, 2026, 41(17): 6053-6072.
Xu Zhicheng, Lu Yichun, Zhang Xian, Bai Yaping, Jiang Kai. Research Status and Key Issues of Temperature Field Measurement, Sensing and Reconstruction for Lithium-Ion Battery System. Transactions of China Electrotechnical Society, 2026, 41(17): 6053-6072.
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2026, Vol. 41 
