Optimal Design of Electric Vehicles using Deep Convolutional Neural Network

This paper proposes the importance of energy storage systems (ESS) in electric vehicles (EVs) and the challenges associated with them, such as size, cost, and weight, which limit the vehicle’s capabilities. It highlights how fuel cells (FCs) can work in conjunction with ESS to handle rapid power changes, contributing to performance and dependability. It proposes a solution using a Honey Badger Algorithm (HBA) integrated with Deep CNN technology to enhance EV power efficiency. The goal is to optimize several factors in the EV charging process, including Orderly charging efficiency, Load fluctuation management, User cost satisfaction, User convenience. The HBA method reportedly outperforms other algorithms in terms of accuracy and power optimization. To manage the power flow between different energy sources, including supercapacitors, multiple control mechanisms are suggested. This approach likely aims to address the real-time dynamic nature of power demand in EVs.