基于电池老化效应的电池热管理系统性能分析与优化

史文伯; 刘敏学; 刘雪涛; 闫龙超; 郭曾嘉

doi:10.19799/j.cnki.2095-4239.2025.0957

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基于电池老化效应的电池热管理系统性能分析与优化

储能测试与评价 | 更新时间：2026-07-01

- 基于电池老化效应的电池热管理系统性能分析与优化
- Analysis and optimization of battery thermal management system based on battery aging effect
- 储能科学与技术 2026年15卷第4期页码：1438-1450
- 作者机构：
  
  青岛海信网络能源股份有限公司，山东青岛 266104
- 作者简介：
  
  史文伯（1972—），男，高级工程师，研究方向为电池热管理系统，E-mail： shiwenbo@hisense.com；
  郭曾嘉，高级工程师，研究方向为电池热管理系统优化设计、锂电池电化学性能与寿命优化设计，E-mail： gzj1106486578@163.com。
- 基金信息：
  
  山东省博士后创新项目(SDCX-ZG-202503144);青岛市博士后应用项目资助(QDBSH20240202029)
- DOI：10.19799/j.cnki.2095-4239.2025.0957
  中图分类号： TK 02
- 收稿：2025-10-24，
  
  修回：2025-12-05，
  
  纸质出版：2026-04-28
- 稿件说明：
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史文伯, 刘敏学, 刘雪涛, 等. 基于电池老化效应的电池热管理系统性能分析与优化[J]. 储能科学与技术, 2026, 15(4): 1438-1450.

SHI Wenbo, LIU Minxue, LIU Xuetao, et al. Analysis and optimization of battery thermal management system based on battery aging effect[J]. Energy Storage Science and Technology, 2026, 15(4): 1438-1450.
史文伯, 刘敏学, 刘雪涛, 等. 基于电池老化效应的电池热管理系统性能分析与优化[J]. 储能科学与技术, 2026, 15(4): 1438-1450. DOI： 10.19799/j.cnki.2095-4239.2025.0957.

SHI Wenbo, LIU Minxue, LIU Xuetao, et al. Analysis and optimization of battery thermal management system based on battery aging effect[J]. Energy Storage Science and Technology, 2026, 15(4): 1438-1450. DOI： 10.19799/j.cnki.2095-4239.2025.0957.

摘要

为实现电池组安全、高效与长久的运行，本研究开发了一个电池热管理系统(BTMS)多物理场耦合数值模型，并对液冷BTMS在不同运行周期下电池组温度特性与电化学特性进行了分析。研究发现：老化电池组内部固体电解质界面膜(SEI)的形成会引发更高的电池放热率。其中，电池内部的可逆热会因为SEI膜的形成与Li

的降低而逐渐减少，但是电池组内部的不可逆热会随着电池组循环次数的增加而增加，且不可逆热的上升幅度远大于可逆热的降低幅度。最终，电池组的放热率会随着电池工作循环次数的增加而上升。因此，当电池组持续运行1000个充放电循环后，电池组温度与温差分别上升了2.54 K、2.15 K、1.93 K和2.34 K、2.04 K、1.85 K。因电池老化效应而引起的电池组温度与温差的偏差，势必会显著影响BTMS的设计。结果表明，入口流速为0.05 m/s时，BTMS即可满足未老化电池组对温度与温差的需求。然而，此方案无法满足老化电池组对换热量的需求。因此，本研究针对BTMS提出了两种优化方案，以确保电池组的温度与温差在长期循环过程中仍然可以得到有效的控制。研究发现：在BTMS冷却液中添加氧化铝(Al

)球形纳米颗粒可以显著增强BTMS换热性能，并且随着纳米颗粒体积分数的增加，BTMS的换热性能也逐渐上升。当电池组运行1000次充放电循环后，采用1% Al

纳米流体，电池最高温度分别降低1.24 K、0.98 K和0.86 K，最大温差分别降低1.09 K、0.88 K和0.79 K；采用3% Al

纳米流体时，电池最高温度分别降低1.92 K、1.56 K和1.36 K，最大温差分别降低1.63 K、1.52 K和1.27 K；而采用5% Al

纳米流体时，电池最高温度分别降低2.64 K、2.20 K和1.94 K，最大温差分别降低2.29 K、2.02 K和1.83 K。此外，基于电池产热特性的BTMS运行方案可以在所有工作循环条件下更加有效地控制电池热特性并减缓电池的容量衰减，并达到降低系统压降与提升电池放电电压的效果。对于老化电池组而言，采用此运行策略后，电池最高温度分别降低5.98 K、4.17 K和3.04 K，最大温差分别降低4.27 K、2.79 K和1.81 K。

Abstract

Novel model considering electrochemistry

battery aging

and heat transfer is developed for the design and optimization of battery thermal management systems (BTMS) to ensure efficient and durable battery operation. The multiphysics behaviors of BTMSs under different working cycles are analyzed and compared. Results show that solid electrolyte interphase (SEI) formation in aged battery packs leads to high heat generation rates. The reversible heat generation rate gradually decreases during cycling owing to SEI formation and Li

reduction inside the battery. By contrast

the irreversible heat generation rate increases with cycling. Meanwhile

the increase in irreversible heat generation was much higher than the decrease in reversible heat generation

causing the total heat generation rate to rise continuously during cycling. Consequently

the maximum temperature and maximum temperature difference after 1000 cycles of BTMS are higher than the initial case by 2.54

2.15

1.93 K and 2.34

2.04

1.85 K

respectively. Such significant deviations in maximum temperature and maximum temperature difference caused by capacity fade will definitely affect BTMS design. Without considering battery aging

an airflow velocity of 0.05 m/s is sufficient for BTMSs to meet the requirements for maximum temperature and maximum temperature difference. However

when capacity fade is considered

BTMSs cannot maintain battery pack temperature within the required limits after 1000 cycles under the investigated inlet velocity. Thus

optimization schemes are proposed for BTMSs to ensure effective thermal management for battery packs during long-term cycling. The addition of Al

nanoparticles at different volume fractions consistently enhances the cooling performance of BTMSs. Furthermore

increasing nanoparticle volume fraction made the nanofluid-based BTMS more effective in controlling thermal behaviors of the battery pack. After 1000 cycles

the maximum temperature and maximum temperature difference decrea

se by 1.24

0.98

0.86 K and 1.09

0.88

0.79 K for water-1% Al

; 1.92

1.56

1.36 K and 1.63

1.52

1.27 K for water-3% Al

; 2.64

2.20

1.94 K and 2.29

2.02

1.83 K for water-5% Al

respectively. For the optimized BTMS operation strategy based on battery heat generation

this method more effectively controls thermal behavior and mitigates battery capacity fade in all working cycles while significantly reducing pressure loss and increasing battery discharge potential. For the aged battery pack after 1000 cycles

the maximum temperature and maximum temperature difference decrease by 5.98

4.17

3.04 K and 4.27

2.79

1.81 K

respectively

using the optimized strategy.

关键词

Keywords

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