液冷锂离子电池包运行参数优化及高倍率可行边界分析

郭喜峰; 彭梓恒; 王馨璐; 王泽镝; 宁一

doi:10.19799/j.cnki.2095-4239.2026.0449

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液冷锂离子电池包运行参数优化及高倍率可行边界分析

储能科学与技术 | 更新时间：2026-07-29

- 液冷锂离子电池包运行参数优化及高倍率可行边界分析
- Operating-parameter optimization and high-rate feasibility boundary analysis of liquid-cooled lithium-ion battery packs
- 储能科学与技术 2026年页码：1-15
- 作者机构：
  
  沈阳建筑大学电气与控制工程学院，辽宁沈阳 110168
- 作者简介：
  
  郭喜峰（1981—），男，博士，教授，研究方向为动力电池热管理，E-mail：gxf1981@163.com；
  宁一，副教授，研究方向为液冷式锂离子电池包的热管理，E-mail：vipningyi@126.com。
- 基金信息：
  
  国家自然科学基金(52577125);辽宁省基础研究项目(2025JH2/10130048)
- DOI：10.19799/j.cnki.2095-4239.2026.0449
  中图分类号： TM 911
- 收稿：2026-05-25，
  
  修回：2026-06-24，
  
  网络首发：2026-07-29，
- 稿件说明：
移动端阅览
郭喜峰, 彭梓恒, 王馨璐, 等. 液冷锂离子电池包运行参数优化及高倍率可行边界分析[J]. 储能科学与技术, XXXX, XX(XX): 1-15.

GUO Xifeng, PENG Ziheng, WANG Xinlu, et al. Operating-parameter optimization and high-rate feasibility boundary analysis of liquid-cooled lithium-ion battery packs[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-15.
郭喜峰, 彭梓恒, 王馨璐, 等. 液冷锂离子电池包运行参数优化及高倍率可行边界分析[J]. 储能科学与技术, XXXX, XX(XX): 1-15. DOI： 10.19799/j.cnki.2095-4239.2026.0449.

GUO Xifeng, PENG Ziheng, WANG Xinlu, et al. Operating-parameter optimization and high-rate feasibility boundary analysis of liquid-cooled lithium-ion battery packs[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-15. DOI： 10.19799/j.cnki.2095-4239.2026.0449.

摘要

针对液冷锂离子电池包在高倍率工况下最高温度升高、温度均匀性下降以及泵功增加的问题，提出一种主动学习Kriging辅助的运行参数多目标优化方法。首先，建立固定液冷电池包三维多物理场仿真模型，以冷却液质量流量、入口温度和放电倍率为输入变量，通过拉丁超立方采样和主动学习补样获得90组高保真样本。其次，引入以入口温度为基准的最高温升作为最高温度的去趋势化建模变量，建立最高温升、电池包最大温差和压降的Kriging代理模型。最后，在固定放电倍率下采用非支配排序遗传算法Ⅱ对冷却液流量和入口温度进行多目标优化，并根据热约束下最低模组内流道泵功准则生成运行参数离线标定点。结果表明：与直接最高温度代理模型相比，入口温度基准温升模型将最高温度预测平均绝对误差由1.192 K降至0.466 K，高入口温度区域平均绝对误差由2.119 K降至0.761 K；主动学习补样后，最高温升、电池包最大温差和压降代理模型的决定系数分别达到0.9866、0.9840和0.9936。3C、5C和7C工况下的热可行推荐点经高保真仿真验证，最高温度、电池包最大温差和泵功的平均绝对误差分别为0.48 K、0.26 K和0.27 mW。基于代理模型密集扫描得到的当前固定液冷几何估计最大可行倍率约为9.92C，且高倍率边界主要受温度均匀性约束控制。这说明该方法可为固定液冷结构下的电池包运行参数优化和高倍率边界识别提供参考。

Abstract

An active-learning Kriging-assisted multi-objective optimization method is proposed for operating-parameter optimization of liquid-cooled lithium-ion battery packs under high-rate conditions. A three-dimensional multiphysics model of a fixed-geometry liquid-cooled battery pack was first established. Coolant mass flow rate

inlet temperature

and discharge rate (C-rate) were selected as input variables

and 90 high-fidelity samples were obtained using Latin hyper

cube sampling and active-learning enrichment. To improve surrogate-model accuracy

the inlet-temperature-referenced maximum temperature rise was introduced as a detrended modeling variable for the maximum battery temperature. Kriging surrogate models were then constructed for the maximum temperature rise

battery-pack maximum temperature difference

and pressure drop. Under each fixed C-rate

the coolant mass flow rate and inlet temperature were optimized using the non-dominated sorting genetic algorithm II (NSGA-II)

and offline operating setpoints were selected according to the minimum pumping power under thermal constraints. The results show that

compared with the direct maximum-temperature surrogate model

the proposed temperature-rise model reduced the mean absolute error (MAE) of maximum-temperature prediction from 1.192 K to 0.466 K

and reduced the MAE in the high-inlet-temperature region from 2.119 K to 0.761 K. After active-learning enrichment

the coefficients of determination (

²) of the surrogate models for maximum temperature rise

maximum temperature difference

and pressure drop reached 0.9866

0.9840

and 0.9936

respectively. The thermally feasible setpoints under 3C

and 7C were verified by high-fidelity simulations

with MAE values of 0.48 K

0.26 K

and 0.27 mW for maximum temperature

maximum temperature difference

and pumping power

respectively. Dense feasibility scanning showed that no strictly feasible region existed at 10C within the studied operating range

and the surrogate-estimated maximum feasible C-rate was approximately 9.92C. The high-rate feasibility boundary was mainly governed by the temperature-uniformity constraint. These results provide a reference for operating-parameter optimization and high-rate feasibility-boundary identification of liquid-cooled battery packs with fixed cooling structures.

关键词

Keywords

references

吴超, 王罗亚, 袁子杰, 等. 液冷散热技术在电化学储能系统中的研究进展[J]. 储能科学与技术, 2024, 13(10): 3596-3612.

WU C, WANG L Y, YUAN Z J, et al. Research progress in liquid cooling and heat dissipation technologies for electrochemical energy storage systems[J]. Energy Storage Science and Technology, 2024, 13(10): 3596-3612.

GHAEMINEZHAD N, WANG Z S, OUYANG Q. A review on lithium-ion battery thermal management system techniques: A control-oriented analysis[J]. Applied Thermal Engineering, 2023, 219: 119497.

郑海, 续彦芳, 刘汉涛, 等. 基于液体介质的锂离子动力电池热管理系统实验分析[J]. 储能科学与技术, 2020, 9(3): 885-891.

ZHENG H, XU Y F, LIU H T, et al. Experimental analysis of thermal management system of lithium ion power battery based on liquid medium[J]. Energy Storage Science and Technology, 2020, 9(3): 885-891.

徐晓斌, 徐业飞, 张恒运, 等. 风冷电池模组热性能及成组效率的多目标优化[J]. 储能科学与技术, 2022, 11(2): 553-562.

XU X B, XU Y F, ZHANG H Y, et al. Multiobjective optimization of thermal performance and grouping efficiency for air cooling battery module[J]. Energy Storage Science and Technology, 2022, 11(2): 553-562.

元佳宇, 李昕光, 王文超, 等. 考虑质量流量的电池组蛇形冷却结构仿真[J]. 储能科学与技术, 2022, 11(7): 2274-2281.

YUAN J Y, LI X G, WANG W C, et al. Simulation of serpentine cooling structure of battery pack considering mass flow[J]. Energy Storage Science and Technology, 2022, 11(7): 2274-2281.

孔为, 金劲涛, 陆西坡, 等. 对称蛇形流道锂离子电池冷却性能[J]. 储能科学与技术, 2022, 11(7): 2258-2265.

KONG W, JIN J T, LU X P, et al. Study on cooling performance of lithium ion batteries with symmetrical serpentine channel[J]. Energy Storage Science and Technology, 2022, 11(7): 2258-2265.

WEI L C, ZOU Y H, CAO F, et al. An optimization study on the operating parameters of liquid cold plate for battery thermal management of electric vehicles[J]. Energies, 2022, 15(23): 9180.

胡银全, 刘和平. 基于遗传算法的电池包高效热管理流道优化[J]. 储能科学与技术, 2021, 10(4): 1446-1453.

HU Y Q, LIU H P. Optimization of efficient thermal management channel for battery pack based on genetic algorithm[J]. Energy Storage Science and Technology, 2021, 10(4): 1446-1453.

XU H W, ZHANG X, XIANG G, et al. Optimization of liquid cooling and heat dissipation system of lithium-ion battery packs of automobile[J]. Case Studies in Thermal Engineering, 2021, 26: 101012.

KHAN S A, EZE C, DONG K J, et al. Design of a new optimized U-shaped lightweight liquid-cooled battery thermal management system for electric vehicles: A machine learning approach[J]. International Communications in Heat and Mass Transfer, 2022, 136: 106209.

LI W, GARG A, XIAO M, et al. Optimization for liquid cooling cylindrical battery thermal management system based on Gaussian process model[J]. Journal of Thermal Science and Engineering Applications, 2021, 13(2): 021015.

LYU C, SONG Y K, YANG D Z, et al. Surrogate model of liquid cooling system for lithium-ion battery using extreme gradient boosting[J]. Applied Thermal Engineering, 2022, 213: 118675.

ZHAO D, CHEN M B, LV J, et al. Multi-objective optimization of battery thermal management system combining response surface analysis and NSGA-II algorithm[J]. Energy Conversion and Management, 2023, 292: 117374.

REN P Z, XIAO Y, CHANG X J, et al. A survey of deep active learning[J]. ACM Computing Surveys, 2022, 54(9): Article 180.

GUO Y, NATH P, MAHADEVAN S, et al. Active learning for adaptive surrogate model improvement in high-dimensional problems[J]. Structural and Multidisciplinary Optimization, 2024, 67(7): 122.

CASTRO-AMOEDO R, GRANACHER J, MARÉCHAL F. A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering[J]. Computers & Chemical Engineering, 2024, 181: 108517.

ZHOU X L, GUO W L, SHI X Y, et al. Machine learning assisted multi-objective design optimization for battery thermal management system[J]. Applied Thermal Engineering, 2024, 253: 123826.

YE L S, LI C S, WANG C H, et al. A multi-objective optimization approach for battery thermal management system based on the combination of BP neural network prediction and NSGA-II algorithm[J]. Journal of Energy Storage, 2024, 99: 113212.

GE X, CHEN Y, LIU W, ZHANG G, LI X, GE J, LI C. Liquid cooling system for battery modules with boron nitride based thermal conductivity silicone grease[J]. RSC Advances, 2022, 12: 15093–15103.

ZHANG C, XIA Z, WANG B, GAO H, CHEN S, ZONG S, LUO K. A Li-Ion Battery Thermal Management System Combining a Heat Pipe and Thermoelectric Cooler[J]. Energies, 2020, 13(4): 841.

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