Fuzzy modelling and metaheuristic to minimize the temperature of lithium-ion battery for the application in electric vehicles

Abstract

The recent progress in the electric vehicles requires developing an efficient battery that can be fast charged and having a longer lifetime. Proper thermal management of the battery systems plays a key factor in the performance and lifetime of batteries. Liquid cooling is one of the feasible methods for effective thermal management of battery systems. In this work, the optimal size of double-layer reverting channel is determined using fuzzy modelling and modern optimization. A novel application of the Slime mould algorithm (SMA) is suggested to find the best size of double-layer cooling channel that can simultaneously minimize battery's temperature, better uniform of battery's temperature, and lower energy consumption. For first time, an accurate fuzzy model of the double-layer channel in terms of four dimensions parameters (width ratio, length ratio of x axis, length ratio of y axis, and thickness of all channels) is successfully obtained. The average coefficient of determination values are 1.0 and 0.8214 respectively for training and testing. Also, the average RMSE values 9.32E-06 and 0.018 respectively for training and testing High coefficient-of-determination values and low for RMSE values both training and testing phases confirmed the accuracy of the model. Then, SMA is used to determine optimal size of the cooling channel to minimize simultaneously the temperature, surface standard deviation, and pressure drop. The results confirmed the accuracy of the proposed fuzzy model in addition to the performance improvement. The overall performance is improved by 7.8% through minimizing the maximum temperature and well thermal distribution.