基于NSGA-II优化的电动汽车热管理系统MPC策略开发及性能

doi:10.19799/j.cnki.2095-4239.2025.0079

储能科学与技术 ›› 2025, Vol. 14 ›› Issue (6): 2200-2214.doi: 10.19799/j.cnki.2095-4239.2025.0079

基于NSGA-II优化的电动汽车热管理系统MPC策略开发及性能

戴春江¹^,²(), 林文野¹^,²(), 李帅旗², 陈翔¹^,², 宋文吉¹^,², 冯自平¹^,², KUZNIK Frédéric³

^1.中国科学技术大学能源科学与技术学院，安徽合肥 230027
^2.中国科学院广州能源研究所，广东广州 510640
^3.里昂国立应用科学学院，法国国家科学研究中心，里昂热科学研究所，UMR 5008，法国维勒班 69621

收稿日期:2025-01-24 修回日期:2025-02-25 出版日期:2025-06-28 发布日期:2025-06-27
通讯作者: 林文野 E-mail:daicj@mail.ustc.edu.cn;linwy@ms.giec.ac.cn
作者简介:戴春江（2000—），男，硕士研究生，研究方向为电动汽车热管理，E-mail：daicj@mail.ustc.edu.cn；
基金资助:
中国科学院国际交流计划(2025PD0085);国家重点研发计划项目(2024YFE0208500);广东省项目(2023QN10L241)

NSGA-II optimization-assisted model predictive control strategy for electric vehicle thermal management systems

Chunjiang DAI¹^,²(), Wenye LIN¹^,²(), Shuaiqi LI², Xiang CHEN¹^,², Wenji SONG¹^,², Ziping FENG¹^,², Frédéric KUZNIK³

^1.School of Energy Science and Engineering, University of Science and Technology of China, Hefei 230027, Anhui, China
^2.Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, Guangdong, China
^3.INSA Lyon, CNRS, CETHIL, UMR 5008, Villeurbanne 69621, France

Received:2025-01-24 Revised:2025-02-25 Online:2025-06-28 Published:2025-06-27
Contact: Wenye LIN E-mail:daicj@mail.ustc.edu.cn;linwy@ms.giec.ac.cn

摘要/Abstract

摘要：

电动汽车热管理在保障电动汽车的安全性、提高舒适度和降低能耗等方面具有重要意义，而热管理系统的核心是优良的控制策略。本工作回顾了现有的电动汽车热管理系统控制策略的优点与不足，提出并评价了一种基于NSGA-II算法优化的模型预测控制(MPC)策略并用于电动汽车的热管理。首先建立了电动汽车热管理系统的仿真模型；随后通过融合MPC策略和NSGA-II多目标优化提出了可以实现多目标控制的电动汽车热管理策略；最后通过比较多个工况下不同控制策略对汽车热管理系统性能的影响，以验证所提出的基于NSGA-II优化的MPC策略的有效性。研究结果发现，在不同工况下，所提出的MPC策略均可有效控制乘员舱温度和电池温度，减小乘员舱温度和电池温度的波动幅度，削减汽车行驶工况剧烈变化对电池温度的影响；同时，MPC策略可有效降低热管理系统能耗，相对于开关控制策略和PID控制策略可实现可观的节能率，分别达到4%～15%和1%～6%。

关键词: 热管理, 控制优化, MPC, NSGA-II, 电动汽车

Abstract:

Thermal management systems play a significant role in the safety, comfort, and energy efficiency of electric vehicles (EVs). Effective thermal management systems for EVs require appropriate control strategies, especially when multiple conflicting control objectives are involved. Herein, we review the existing control strategies used in EV thermal management systems and propose a novel multi-objective model predictive control (MPC) strategy for the optimal operation of thermal management systems. First, we developed a comprehensive numerical model of an EV thermal management systems. Next, we established the MPC strategy enhanced by the NSGA-II algorithm to simultaneously optimize temperature control in the cabin and batteries, as well as the energy consumption. Finally, we assessed and compared the impacts of different control strategies on the performance of EV thermal management systems under various driving conditions. The results demonstrate that under different working conditions, the proposed MPC strategy can effectively control the temperatures of both the cabin and batteries, thereby reducing their fluctuations and ameliorating the effects of significant changes in driving conditions on battery temperature. In addition, the MPC strategy can effectively reduce energy consumption, achieving energy-saving rates of approximately 4%—15% and 1%—6% compared with the threshold control and PID control strategies, respectively.

Key words: thermal management, optimal control, MPC, NSGA-II, electric vehicle

中图分类号:

TK 11

戴春江, 林文野, 李帅旗, 陈翔, 宋文吉, 冯自平, KUZNIK Frédéric. 基于NSGA-II优化的电动汽车热管理系统MPC策略开发及性能[J]. 储能科学与技术, 2025, 14(6): 2200-2214.

Chunjiang DAI, Wenye LIN, Shuaiqi LI, Xiang CHEN, Wenji SONG, Ziping FENG, Frédéric KUZNIK. NSGA-II optimization-assisted model predictive control strategy for electric vehicle thermal management systems[J]. Energy Storage Science and Technology, 2025, 14(6): 2200-2214.

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部件	参数	数值	单位
压缩机	排量	161	cc
冷凝器	长×宽×高	685×475×16	mm
蒸发器	长×宽×高	295×272×38	mm
膨胀阀	横截面积	2.14	mm²
板式换热器	长×宽×高	150×76×2.5	mm
散热器	长×宽×高	320×275×16	mm

参数	值	单位
标称容量	40	Ah
长×宽×高	148×27×92	mm
重量	0.85	kg
比热容	1192	J/(kg·K)
长度方向热导率	55	W/(m·K)
宽度方向热导率	0.853	W/(m·K)
高度方向热导率	55	W/(m·K)

主要参数	符号	不确定度
电池内阻	R_o+R_p	±0.00029 Ω
电池温度	T_bat	±0.5 ℃
熵热系数	dU_ocv/dT_bat	±0.0041 mV/K
电池荷电状态	SOC	±0.00024

项目	MAE	MSE	RMSE
MPC	0.70	12.75	3.57
开关控制	2.14	20.63	4.54
PID控制	0.83	13.41	3.66

项目	MAE	MSE	RMSE
MPC	0.025	0.0028	0.053
开关控制	0.086	0.012	0.11
PID控制	0.089	0.034	0.18

基于NSGA-II优化的电动汽车热管理系统MPC策略开发及性能

NSGA-II optimization-assisted model predictive control strategy for electric vehicle thermal management systems

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参考文献 26

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项目	MAE	MSE	RMSE
MPC	0.62	11.51	3.39
开关控制	2.10	20.92	4.57
PID控制	0.82	13.54	3.68

项目	MAE	MSE	RMSE
MPC	0.0037	0.00021	0.014
开关控制	0.087	0.010	0.10
PID控制	0.020	0.00058	0.024

项目	MAE	MSE	RMSE
MPC	0.93	17.58	4.19
开关控制	2.28	24.94	4.99
PID控制	1.19	20.24	4.50

项目	MAE	MSE	RMSE
MPC	0.025	0.0015	0.039
开关控制	0.083	0.011	0.11
PID控制	0.087	0.012	0.11

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