锂离子电池充放电过程温度响应时空关联模型及温度场重构

IF 9.4 1区 工程技术 Q1 ENERGY & FUELS
Energy Pub Date : 2025-06-01 Epub Date: 2025-03-28 DOI:10.1016/j.energy.2025.135902
Tao Zhang , Guangjun Wang , Hong Chen , Zhaohui Mao , Yalan Ji
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引用次数: 0

摘要

本研究提出了一种估算锂离子电池内部温度分布的创新方法:通过建立电池温度响应时空相关模型(RSTCM),根据电池表面温度直接重构电池内部瞬态温度场。首先,建立了电池充放电过程温度的外部描述。证实了电池内各节点温度之间存在明确的时空相关性。在此基础上,利用正则化优化技术构建了温度响应的时空相关矩阵。建立了电池温度RSTCM模型,利用表面测温数据估计电池温度分布。本研究利用锂电池放电实验数据对RSTCM模型进行验证。并通过数值模拟分析了测点个数、充放电率、测量误差、模型失配等因素对重构结果的影响。在充放电过程中,重构的电池温度场的瞬时最大误差约为1.5 K。在测量噪声标准偏差为1.0 K的情况下,温度场重建的瞬时最大误差约为1.3 K。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temperature response spatiotemporal correlation model of lithium-ion battery and temperature field reconstruction during charging and discharging processes
This study introduces an innovative approach aimed at estimating the temperature distribution inside a lithium-ion battery: By establishing a temperature response spatiotemporal correlation model (RSTCM) for battery, the transient temperature field inside the battery is directly reconstructed according to the surface temperature of the battery. Firstly, an external description of the temperature at the battery charging and discharging process is established. The presence of a definitive spatiotemporal correlation among the temperatures at various nodes within the battery is confirmed. Furthermore, a spatiotemporal correlation matrix for temperature responses is constructed using regularization optimization techniques. The temperature RSTCM for battery is established, which utilizes surface temperature measurement data to estimate the temperature distribution. This study utilizes discharge experiment data from lithium battery to validate the RSTCM model. Additionally, numerical simulations are conducted to analyze the impact of various factors on the reconstruction results, including the number of measurement points, charge and discharge rates, measurement errors, and model mismatches. During the charging/discharging process, the reconstructed battery temperature field exhibits an instantaneous maximum error of roughly 1.5 K. With a measurement noise standard deviation of 1.0 K, the temperature field reconstruction exhibits an instantaneous maximum error of approximately 1.3 K.
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来源期刊
Energy
Energy 工程技术-能源与燃料
CiteScore
15.30
自引率
14.40%
发文量
0
审稿时长
14.2 weeks
期刊介绍: Energy is a multidisciplinary, international journal that publishes research and analysis in the field of energy engineering. Our aim is to become a leading peer-reviewed platform and a trusted source of information for energy-related topics. The journal covers a range of areas including mechanical engineering, thermal sciences, and energy analysis. We are particularly interested in research on energy modelling, prediction, integrated energy systems, planning, and management. Additionally, we welcome papers on energy conservation, efficiency, biomass and bioenergy, renewable energy, electricity supply and demand, energy storage, buildings, and economic and policy issues. These topics should align with our broader multidisciplinary focus.
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