Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm. (May 2023)

• Record Type: Journal Article
• Authors: Yan, Wei; Li, Mei-Jing; Mei, Na; Qu, Chun-Yan; Wang, Yuan; Liu, Li-Ping
• Is Part Of: Measurement and control. Volume 56:Number 5/6(2023)
• Page Start: 1026
• Page End: 1036
• Publication Date: 2023-05
• Subjects: Double-population adaptive mutation method -- genetic algorithm -- intelligent control strategy -- thermal management system -- EV; Automatic control -- Periodicals; Engineering instruments -- Periodicals; Production engineering -- Periodicals; 629.8
• DOI: 10.1177/00202940221105851
• Languages: English
• ISSNs: 0020-2940

Abstract

The thermal management system is one of the important assemblies that ensure the secure operation of electric vehicles (EVs). Using intelligent algorithms to optimize the control strategy of the thermal management system can reduce energy consumption under the premise of effective heat dissipation of EVs. This paper attempts to construct the control strategy of EV thermal management system by coupling the modified genetic algorithm (MGA) and support vector regression (SVR). Firstly, the double-population adaptive mutation method and a novel optimization process are adopted to obtain MGA. Afterward, the performance of MGA is verified by four benchmark functions compared with three typical algorithms, which are genetic algorithm (GA), double-population genetic algorithm (DPGA), and quantum genetic algorithm (QGA). The results demonstrate that the accuracy and stability of MGA are obviously better than the other three algorithms. Secondly, MGA is applied to modify parameters of SVR kernel function, and the accuracy of MGA-SVR algorithm is verified by the Auto-MPG and Computer Hardware data sets. The mean square deviations of the SVR algorithm test set are 0.0186 and 0.0806, respectively, and the mean square deviations of the MGA-SVR algorithm test set are 0.0099 and 0.0054, respectively, which fully shows that MGA-SVR have more accurate forecasting capabilities. Finally, the thermal management system model of EV is built by the one-dimensional simulation software KULI. Under the Chinese working condition, fan speed which meets the cooling requirements of the motor and controller is obtained from the KULI model, and the database is constructed. Then, MGA-SVR is trained by database and employed to predict fan speed under the Chinese working condition and obtain control strategy of the thermal management system. Compared with traditional control strategy, the thermal management system based on MGA-SVR control strategy can not only meet the radiating requirements, but also effectively reduce the power consumption of fans.