基于3D模型的电池电化学-热耦合数值模拟与预测分析

Analysis of electrochemical-thermal coupling and prediction of electrothermal properties of square lithium-ion batteries under multi-factor influence based on 3D model

摘要

能源短缺、环境污染推动锂离子电池广泛应用,但其充放电时温度控制难题亟待攻克,研究其电热特性具有重要意义。采用仿真模拟技术,构建150Ah的3D磷酸铁锂电池电化学-热耦合模型,模拟不同充电速率下的热行为。通过结构向量自回归模型(SVAR)探究多电化学变量与温度变量的协同影响,提出集成学习回归预测系统以提高研究效率。结果表明:充电速率提升会使电池温度升高及温差增大,高倍率充电(1.3C、1.5C)时温差超5℃,产热速率加快,可逆热增速最显著;电池电化学与热关系复杂,0.5C充电时,电压、电流密度、电池容量与最大温差显著正相关(p<0.001);1.0C放电时,电压与多数热特性变量显著正相关,不同条件下各参数间相关系数在-0.973~0.949之间。集成学习回归预测系统多指标显示预测精度高,泛化与趋势分析能力强,可为电池热管理提供支撑。

Abstract

Energy shortage and environmental pollution have driven the widespread adoption of lithium-ion batteries, however, effective temperature control during charging and discharging needs to be overcome, so it is of great significance to investigate the electrothermal characteristics of lithium-ion batteries. A 3D electrochemical-thermal coupled model of a 150Ah lithium iron phosphate battery was established using simulation technology to simulate thermal behavior under different charging rates. The synergistic effects of multiple electrochemical variables and temperature variables were investigated by the structure vector autoregressive model (SVAR), and an integrated learning regression prediction system was developed to improve the research efficiency. The results indicate that the increase of charging rate will increase the temperature and temperature difference of the battery, and the temperature difference exceeds 5℃ when charging at high rate (1.3C, 1.5C), the heat generation rate accelerates significantly, with the most pronounced increase in reversible heat. The relationship between battery electrochemistry and heat is complex. At a charging rate of 0.5C, voltage, current density, battery capacity and maximum temperature difference are significantly positively correlated with p < 0.001. During 1.0C discharge, the voltage is significantly positively correlated with most of the thermal characteristic variables, with correlation coefficients ranging from -0.973 to 0.949 under different conditions. The integrated learning regression prediction system shows high prediction accuracy and strong generalization and trend analysis capabilities, which can provide support for battery thermal management.

关键词

锂离子电池 / 电化学-热耦合 / 电热特性 / 预测

Key words

lithium-ion battery / electrochemical-thermal coupling / electric heating characteristics / forecast

参考文献

[1] 田建辉, 张成, 韩兴本, 等 . 基于电化学—热耦合模型的锂离子电池容量衰减研究[J]. 现代机械, 2023(5): 10-15.

[2] 刘邦金, 汪林威, 吴月月, 等 . 锂离子电池热管理研究进展[J]. 化工学报, 2024, 75(12): 4413-4431.

[3] 孙航宇, 李卓华, 王亚丽, 等 . 固体氧化物燃料电池直接内重整的模型研究进展[J]. 储能科学与技术, 2024, 13(4): 1277-1292.

[4] Nie P, Zhang S W, Ran A, et al. Full—cycle electrochemical—thermal coupling analysis for commercial lithium—ion batteri[J]. Applied Thermal Engineering, 2021, 184: 116258.

[5] Lin X W, Jiang Y, Yu H T, et al. A comprehensive investigation on the electrochemical and thermal inconsistencies for 280Ah energy storage lithium—ion battery[J]. Energy Conversion and Management, 2024, 315: 118750.

[6] Huang Y F, Lai X, Ren D S, et al. Thermal and stoichiometry inhomogeneity investigation of large—format lithium—ion batteries via a three—dimensional electrochemical—thermal coupling model[J]. Electrochimica Acta, 2023, 468: 143212.

[7] 闫时建, 郭锦, 同阳, 等 . 碳布用于锂离子电池三维一体化柔性正极的可行性研究[J]. 材料工程, 2024, 52(3): 192-198.

[8] Zhang Y S, Zhang W K, Kong W J . Numerical and experimental study on thermal behavior of prismatic lithium—ion battery for large—capacity energy storage[J]. Journal of Energy Storage, 2024, 83: 110620.

[9] 刘勇, 于怀汶, 刘大鹏, 等 . 基于ABC—LSTM模型的锂离子电池剩余使用寿命预测[J]. 储能科学与技术, 2025, 14(1): 331-345.

[10] 李建萱, 林琛, 周忠凯 . 基于减平均优化算法与双向长短期记忆网络的锂离子电池健康状态估算[J]. 储能科学与技术, 2025, 14(1): 358-369.

[11] 尤明星 . 基于数据驱动的锂离子电池容量估计与剩余使用寿命预测[D]. 重庆: 重庆大学, 2023: 7-9.

[12] 周文璐, 郑燕萍, 杨丞, 等 . 基于智能数模融合的锂离子电池剩余使用寿命预测[J]. 汽车技术, 2025(2): 55-62.

[13] 李志义, 孙赫, 孙海礼, 等 . 高电压正极材料LiFe_0.06Ni_0.94PO₄/C的制备及其性能研究 [J]. 现代化工, 2025, 45(1): 184-189.

[14] 邵海涛, 闫华军, 王伟, 等 . 辊压温度对锂离子电池正极微观结构及性能的影响[J]. 材料工程, 2024, 52(11): 158-165.

[15] 朱浩然, 陈自强, 杨德庆 . 基于差分热伏安法和高斯过程回归的锂离子电池健康状态估计[J]. 上海交通大学学报, 2024, 58(12): 1925-1934.

[16] 陈媛, 段文献, 何怡刚, 等 . 带降噪自编码器和门控递归混合神经网络的电池健康状态估算[J]. 电工技术学报, 2024, 39(24): 7933-7949.

[17] 陈怡沁, 许于, 周静红, 等 . 锂离子电池异构建模及内部传质机理探究: 粒径分布的影响[J]. 化工学报, 2021, 72(2): 1078-1088.

[18] 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.

[19] An Z J, Jia L, Wei L T, et al. Investigation on lithium—ion battery electrochemical and thermal characteristic based on electrochemical—thermal coupled model[J]. Applied Thermal Engineering, 2018, 137: 792-807.

[20] Li J, Cheng Y, Jia M, et al. An electrochemical—thermal model based on dynamic responses for lithium iron phosphate battery[J]. Journal of Power Sources, 2014, 255: 130-143.

[21] Wu W, Xiao X R, Huang X S . The effect of battery design parameters on heat generation and utilization in a Li—ion cell[J]. Electrochimica Acta, 2012, 83: 227-240.

[22] Lei D Y, Wang Y, Fu J F, et al. Electrochemical—thermal analysis of large—sized lithium—ion batteries: Influence of cell thickness and cooling strategy in charging[J]. Energy, 2024, 307: 132629.

[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] 杨彪, 钱禹东, 石裕怡, 等 . 改进鲸鱼算法优化多微波源功率组合的微波加热系统温度均匀性研究[J]. 中南大学学报(自然科学版), 2024, 55(11): 4367-4381.

基金资助

国家重点研发计划项目(2024YFE0106800)

山东省高等学校青创人才引育计划创新团队项目(鲁教科函〔2021〕51号)