基于SVR-NSGA-Ⅱ算法的混合电池热仿真优化

doi:10.16085/j.issn.1000-6613.2025-066

化工进展 ›› 2025, Vol. 44 ›› Issue (8): 4795-4807.DOI: 10.16085/j.issn.1000-6613.2025-066

• 过程系统工程的模拟与仿真 • 上一篇下一篇

基于SVR-NSGA-Ⅱ算法的混合电池热仿真优化

莫文迪¹(), 王思静¹, 林伊婷¹^,²(), 练成¹^,³(), 刘洪来¹^,³

^1.华东理工大学化工学院, 化学工程联合国家重点实验室，上海 200030
^2.华东理工大学机械与动力工程学院，上海 200030
^3.华东理工大学化学与分子工程学院，上海 200030

收稿日期:2025-05-08 修回日期:2025-07-21 出版日期:2025-08-25 发布日期:2025-09-08
通讯作者: 林伊婷,练成
作者简介:莫文迪（2000—），男，硕士研究生，研究方向为电池热管理。E-mail：Y82220080@mail.ecuat.edu.cn。
基金资助:
国家自然科学基金(12447149);国家自然科学基金(22278127)

Simulation and optimization of hybrid battery thermal management based on SVR-NSGA-Ⅱ algorithm

MO Wendi¹(), WANG Sijing¹, LIN Yiting¹^,²(), LIAN Cheng¹^,³(), LIU Honglai¹^,³

^1.State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai 200030, China
^2.School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200230, China
^3.School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200030, China

Received:2025-05-08 Revised:2025-07-21 Online:2025-08-25 Published:2025-09-08
Contact: LIN Yiting, LIAN Cheng

摘要/Abstract

摘要：

锂电池热管理是确保电池热安全的关键，虽然传统的有限元分析方法被广泛应用于锂电池热管理研究，但存在计算效率低、参数设置复杂等局限性。本文提出了一种结合特征工程和有限元分析结果的机器学习模型，通过正交设计方法有效减小所需的有限元仿真数据量；利用支持向量回归（SVR）模型准确预测混合电池包的温度特征；采用非支配排序遗传算法Ⅱ（NSGA-Ⅱ）系统分析了电池结构参数与冷却策略的协同优化关系，提出了兼顾散热性能与能耗效率的最佳方案。与传统方法相比，本方法在保持预测精度的同时大幅提升了计算效率，为电池热管理系统的智能化设计提供了新思路。本研究构建的“特征提取-机器学习建模-多目标优化”技术框架，不仅能够准确预测电池温度特性，还能为不同应用场景下的热管理方案优化提供决策支持。该方法在电动汽车和储能系统等领域具有重要的工程应用价值，有助于提升电池系统的安全性与能效。

关键词: 电池热管理, 有限元分析, 支持向量回归, 非支配排序遗传算法

Abstract:

Battery thermal management is critical to ensure the thermal safety of lithium-ion batteries. Although traditional finite element analysis methods have been widely applied in battery thermal management research, they have limitations, such as low computational efficiency and complex parameter settings. This paper presented a machine learning model that combines feature engineering with finite element analysis results. By employing an orthogonal design method, the required finite element simulation data volume was effectively reduced. The support vector regression (SVR) model was used to accurately predict the temperature characteristics of a hybrid battery pack. The non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was applied to systematically analyze the synergistic optimization relationship between battery structural parameters and cooling strategies, proposing an optimal solution that balanced heat dissipation performance and energy consumption efficiency. Compared with traditional methods, the proposed approach significantly enhanced computational efficiency while maintaining prediction accuracy, providing a novel approach for the intelligent design of battery thermal management systems. The “feature extraction-machine learning modeling-multi-objective optimization” framework constructed in this study not only accurately predicteed battery temperature characteristics but also provided decision support for optimizing thermal management solutions in various application scenarios. This method has significant engineering application value in fields such as electric vehicles and energy storage systems, contributing to the improvement of battery system safety and energy efficiency.

Key words: battery thermal management, finite element analysis, support vector regression, non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)

中图分类号:

TQ-9

莫文迪, 王思静, 林伊婷, 练成, 刘洪来. 基于SVR-NSGA-Ⅱ算法的混合电池热仿真优化[J]. 化工进展, 2025, 44(8): 4795-4807.

MO Wendi, WANG Sijing, LIN Yiting, LIAN Cheng, LIU Honglai. Simulation and optimization of hybrid battery thermal management based on SVR-NSGA-Ⅱ algorithm[J]. Chemical Industry and Engineering Progress, 2025, 44(8): 4795-4807.

图/表 10

参考文献 31

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参数	数值
参数	组合1	组合2	组合3	组合4	组合5	组合6
进风温度/K	298.15	296.15	294.15	292.15	290.15	288.15
风速/m·s^-1	0.5	0.4	0.3	0.2	0.1	0.05
电池横向间距/mm	50	40	30	20	10	5
电池纵向间距/mm	50	40	30	20	10	5
放电倍率	3C	2.5C	2C	1.5C	1C	0.5C

参数	数值
参数	组合1	组合2	组合3	组合4	组合5	组合6
进风温度/K	298.15	296.15	294.15	292.15	290.15	288.15
风速/m·s^-1	0.5	0.4	0.3	0.2	0.1	0.05
电池横向间距/mm	50	40	30	20	10	5
电池纵向间距/mm	50	40	30	20	10	5
放电倍率	3C	2.5C	2C	1.5C	1C	0.5C

输入					输出
倍率	温度/K	风速/m·s^-1	横间距/m	纵间距/m	最大温度/K	温差/K	功耗/W
3C	296.15	0.3	0.03	0.03	319.26	12.47	12.07
3C	288.15	0.1	0.04	0.005	324.32	16.13	19.45
3C	294.15	0.05	0.05	0.01	327.04	13.65	7.24
3C	288.15	0.5	0.05	0.03	312.38	14.1	95.53
3C	290.15	0.1	0.005	0.02	317.55	12.34	9.41
3C	290.15	0.3	0.03	0.02	319.31	13.12	35.98
2.5C	294.15	0.2	0.005	0.05	313.54	10.64	9.61
2.5C	298.15	0.1	0.05	0.005	323.93	11.48	3.70
2.5C	290.15	0.5	0.04	0.02	312.73	10.52	67.48
2.5C	296.15	0.3	0.02	0.03	315.68	9.7	10.60
2.5C	298.15	0.05	0.05	0.01	324.69	12.22	3.60
2.5C	292.15	0.4	0.05	0.04	315.41	11.78	47.99
2C	290.15	0.4	0.01	0.05	302.42	7.11	31.07
2C	296.15	0.2	0.04	0.02	316.1	8.66	10.20
2C	296.15	0.05	0.02	0.005	318.61	8.82	4.68
2C	288.15	0.3	0.01	0.05	316.48	8.49	29.19
2C	292.15	0.2	0.04	0.01	316.48	9.17	10.20
2C	292.15	0.3	0.05	0.005	315.32	10.40	36.87
1.5C	290.15	0.4	0.03	0.05	301.94	6.88	46.81
1.5C	292.15	0.05	0.005	0.04	311.73	9.41	5.74
1.5C	288.15	0.1	0.01	0.02	304.41	7.68	12.06
1.5C	292.15	0.5	0.01	0.03	299.61	4.42	29.59
1.5C	288.15	0.05	0.02	0.04	309.47	8.44	9.01
1.5C	298.15	0.5	0.005	0.005	304.98	6.98	4.50
1C	292.15	0.4	0.005	0.05	314.82	13.32	21.42
1C	294.15	0.2	0.04	0.04	304.66	3.75	16.50
1C	298.15	0.2	0.005	0.05	303.85	3.12	3.90
1C	298.15	0.5	0.03	0.02	304.2	3.28	4.50
1C	292.15	0.4	0.01	0.04	303.49	4.59	24.38
1C	298.15	0.4	0.02	0.01	296.87	2.77	4.30
0.5C	294.15	0.5	0.02	0.03	295.11	1.37	26.15
0.5C	288.15	0.1	0.02	0.01	290.33	1.97	14.53
0.5C	298.15	0.2	0.04	0.005	292.09	2.44	3.90
0.5C	296.15	0.3	0.01	0.04	297.07	1.31	9.12
0.5C	294.15	0.1	0.03	0.03	296.4	1.98	9.01
0.5C	294.15	0.05	0.03	0.01	297.39	2.1	6.26

输入					输出
倍率	温度/K	风速/m·s^-1	横间距/m	纵间距/m	最大温度/K	温差/K	功耗/W
3C	296.15	0.3	0.03	0.03	319.26	12.47	12.07
3C	288.15	0.1	0.04	0.005	324.32	16.13	19.45
3C	294.15	0.05	0.05	0.01	327.04	13.65	7.24
3C	288.15	0.5	0.05	0.03	312.38	14.1	95.53
3C	290.15	0.1	0.005	0.02	317.55	12.34	9.41
3C	290.15	0.3	0.03	0.02	319.31	13.12	35.98
2.5C	294.15	0.2	0.005	0.05	313.54	10.64	9.61
2.5C	298.15	0.1	0.05	0.005	323.93	11.48	3.70
2.5C	290.15	0.5	0.04	0.02	312.73	10.52	67.48
2.5C	296.15	0.3	0.02	0.03	315.68	9.7	10.60
2.5C	298.15	0.05	0.05	0.01	324.69	12.22	3.60
2.5C	292.15	0.4	0.05	0.04	315.41	11.78	47.99
2C	290.15	0.4	0.01	0.05	302.42	7.11	31.07
2C	296.15	0.2	0.04	0.02	316.1	8.66	10.20
2C	296.15	0.05	0.02	0.005	318.61	8.82	4.68
2C	288.15	0.3	0.01	0.05	316.48	8.49	29.19
2C	292.15	0.2	0.04	0.01	316.48	9.17	10.20
2C	292.15	0.3	0.05	0.005	315.32	10.40	36.87
1.5C	290.15	0.4	0.03	0.05	301.94	6.88	46.81
1.5C	292.15	0.05	0.005	0.04	311.73	9.41	5.74
1.5C	288.15	0.1	0.01	0.02	304.41	7.68	12.06
1.5C	292.15	0.5	0.01	0.03	299.61	4.42	29.59
1.5C	288.15	0.05	0.02	0.04	309.47	8.44	9.01
1.5C	298.15	0.5	0.005	0.005	304.98	6.98	4.50
1C	292.15	0.4	0.005	0.05	314.82	13.32	21.42
1C	294.15	0.2	0.04	0.04	304.66	3.75	16.50
1C	298.15	0.2	0.005	0.05	303.85	3.12	3.90
1C	298.15	0.5	0.03	0.02	304.2	3.28	4.50
1C	292.15	0.4	0.01	0.04	303.49	4.59	24.38
1C	298.15	0.4	0.02	0.01	296.87	2.77	4.30
0.5C	294.15	0.5	0.02	0.03	295.11	1.37	26.15
0.5C	288.15	0.1	0.02	0.01	290.33	1.97	14.53
0.5C	298.15	0.2	0.04	0.005	292.09	2.44	3.90
0.5C	296.15	0.3	0.01	0.04	297.07	1.31	9.12
0.5C	294.15	0.1	0.03	0.03	296.4	1.98	9.01
0.5C	294.15	0.05	0.03	0.01	297.39	2.1	6.26

核函数	模型评估
核函数	MAE	RMSE	R²
Linear	0.3514	0.5928	0.7694
Polynomial，degree=3	0.5342	0.7309	0.8300
Polynomial，degree=5	0.7691	0.8770	0.8122
Polynomial，degree=10	3.571	1.890	0.7797
Sigmoid	0.5493	0.7411	0.4898
RBF	0.3455	0.5828	0.9272

基于SVR-NSGA-Ⅱ算法的混合电池热仿真优化

Simulation and optimization of hybrid battery thermal management based on SVR-NSGA-Ⅱ algorithm

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参考文献 31

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倍率	温度/K	风速/m·s^-1	横间距/m	纵间距/m	最大温度/K	温差/K	功耗/W
1C	298.1498	0.055425	0.026458	0.009157	303.1787	4.45963	3.610991
1C	297.7468	0.062086	0.01642	0.014448	302.7296	3.810187	3.873118
1C	298.1175	0.210727	0.014776	0.016516	296.6582	1.667199	3.986823
1C	297.8176	0.093002	0.02012	0.005724	301.8846	4.084352	4.021576
1C	298.1409	0.274166	0.012436	0.029493	296.8888	0.635862	4.07063
1C	298.1489	0.290794	0.026445	0.01581	295.7818	2.782723	4.085724
1C	298.0934	0.235924	0.025113	0.019412	295.9735	1.187126	4.133158
1C	298.0389	0.213434	0.01858	0.008309	296.793	2.57758	4.175372
1C	298.09	0.260833	0.022174	0.00583	295.9155	2.703202	4.199568
2C	298.1498	0.362433	0.016372	0.005804	309.4179	7.563364	4.225752
2C	298.103	0.299076	0.019612	0.024996	310.5939	6.577562	4.249028
2C	298.1244	0.433752	0.014291	0.019154	308.9184	6.922283	4.472081
2C	298.0485	0.362584	0.009417	0.005526	308.7597	7.668079	4.527704
2C	298.0679	0.371879	0.017592	0.028589	309.4752	6.37149	4.556078
2C	298.0191	0.368888	0.006092	0.005904	308.487	7.743113	4.595299
2C	297.9938	0.354572	0.009254	0.030373	308.446	6.419943	4.662256
2C	298.0307	0.395335	0.01085	0.02582	308.5971	7.649724	4.695016
2C	297.9863	0.382544	0.013409	0.01854	308.8933	7.572058	4.84145
2C	297.8083	0.30279	0.013678	0.036308	309.2721	6.251644	5.064739
2C	297.8577	0.378667	0.008252	0.025934	308.4531	7.656024	5.13563
3C	296.9683	0.483702	0.005178	0.045631	309.0392	9.668328	8.57059
3C	296.3382	0.480497	0.005712	0.026793	308.7397	9.53581	10.82466
3C	296.1516	0.455076	0.006785	0.016051	309.0937	9.55927	11.29808
3C	295.7591	0.432227	0.00574	0.028024	308.9364	9.54416	11.92551
3C	295.7285	0.444453	0.005551	0.031561	308.7052	9.486991	12.21326
3C	296.1111	0.495319	0.005166	0.005337	308.5215	9.442108	12.21513
3C	296.1333	0.474527	0.010348	0.018719	308.6024	9.432871	12.5361
3C	295.6324	0.473817	0.005477	0.024416	308.3743	9.354529	13.09863
3C	295.2564	0.46248	0.006096	0.017115	307.9464	9.240384	14.33367
3C	295.5137	0.494007	0.006928	0.01242	307.4532	9.216649	14.39757
3C	295.1991	0.47627	0.005141	0.007483	307.7789	9.189068	14.52848

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