Vehicle battery thermal management with exergy considerations using a hybrid SHO-DRCNN approach for liquid cooling

This manuscript proposes an efficient hybrid approach for liquid cooling with a battery thermal management system (BTMS) in an electric vehicle (EV). The proposed hybrid approach is the combination of the sea-horse optimiser (SHO) and dilated residual convolutional neural network (DRCNN), known as SHO-DRCNN approach. The main purpose of the proposed method is to provide a dependable and effective thermal management system that can successfully regulate the BP's temperature in an EV. By then, the proposed technique is implemented in the MATLAB platform and compared with other existing techniques. The proposed approach maintains the system's thermal management intact while reducing errors.