ANN-Based Fast Charging Control Strategy for Electric Vehicles With Intelligent Battery Thermal Management Using Renewable Energy Resources

The growing concern about pollutants in the atmosphere has pushed automobile manufacturers to produce sustainable and environmentally friendly transportation. Rapid battery charging, with a target of a minimum recharging duration, is expected to speed up the widespread acceptance of battery-powered automobiles. This study proposes a prototype of a quick electric automobile charging system coupled with an independent dc microgrid powered by sustainable energy sources. In this research, a new deep neural network (DNN)-based control approach is established by substituting model predictive control (MPC) combined with ANNs rather than just using MPC, resulting in significant security features for the power demand control under fluctuating environments. An artificial neural network (NN) supervisor manages the distribution of power for quick dc charging of electric vehicles (EVs) using energy that is accessible from clean energy resources and battery backup devices. In order to ensure the security and efficiency of EV batteries during extreme operating conditions, a battery thermal control technique is suggested. The recommended ANN-MPC scheme’s outcomes are evaluated with simulation and experimental studies under various operating conditions, and the findings show that it performs better than the MPC controller.