Optimization Investigation on Air Phase Change Material Based Battery Thermal Management System. Issue 7 (16th May 2021)

• Record Type: Journal Article
• Authors: Lin, Xiangwei; Zhang, Xuelai; Liu, Lu; Yang, Mai
• Is Part Of: Energy technology. Volume 9:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-16
• Subjects: air phase change material cooling -- artificial intelligence algorithm -- battery thermal management -- numerical optimizations; Energy development -- Periodicals; Power resources -- Periodicals; 333.79
• DOI: 10.1002/ente.202100060
• Languages: English
• ISSNs: 2194-4288

Abstract

Abstract : To ensure the normal operation of a battery pack, a battery thermal management system (BTMS) is required to control the temperature of batteries. Herein, the method using an artificial neural network (ANN) combined with a genetic algorithm (GA) is proposed to optimize the thermal performance of air phase change material (PCM) cooling based BTMS. The ANN is applied to describe the relationship between BTMS parameters (inlet air velocity, inlet air temperature, PCM thickness, battery unit spacing, and discharge rate) and battery pack thermal characteristics. The results show that the PCM thickness and battery unit spacing have little effect on the battery temperature. Then, the optimal parameter combinations of BTMS are solved by GA with the goal of minimizing the maximum temperature. The maximum relative error between simulation and prediction is 0.484 °C, which is only 1.3835% of the simulated value. The optimal parameter combinations help to slow down the temperature rise of the battery pack and delay the phase transition of PCM. The results indicate that the developed model can accurately describe the relationship between the BTMS parameters and battery temperature, which provides a time‐saving and efficient method for the optimal design of BTMS. Abstract : A 3D model is proposed to evaluate the effect of air phase change material hybrid battery thermal management system (BTMS). The relationship between system parameters and battery thermal characteristics is described by an artificial neutral network. A genetic algorithm is applied to obtain the optimal results. The article provides a time‐saving and efficient method for the optimal design of BTMS.