In this paper, a novel idea of charging the vehicle on the go while using the solar panels on the roof of the vehicle is introduced. The use of electric vehicles has increased among people as the vehicles are affordable. Electric vehicle charging is one of the major problems faced by most manufacturers today. The PV panels take the energy from sunlight, and it can charge the vehicle battery. When the vehicle is moving on the road, the power extraction for charging may not be proper due to the partial shaded condition. To extract sufficient power for charging, a hybrid optimization algorithm has been introduced. In this paper, an electric vehicle model that uses the hybrid optimization algorithm of grey wolf optimization and cuckoo search algorithm (GWO-CSA) is developed and compared with the conventional particle swarm optimization (PSO) algorithm. The extraction of maximum power and the performance of the vehicle are analysed using MATLAB/Simulink, and the simulation results are discussed. To test the effectiveness of the algorithm, it is compared with the other three algorithms.

In this decade, <span>electric car technology has advanced at a breakneck pace. People are also using electric vehicles more since they are more inexpensive. Electric car charging is one of the issues that most sectors confront, as there are many cities in India where charging stations have yet to be established. In this paper, an innovative approach for charging a vehicle while on the move is presented, utilising the solar panels on the vehicle's roof. The panels collect energy from the sun and use it to charge the vehicle's battery. Even when the vehicle is driving down the road, this happens. Partial shading is a concern for solar panels when travelling on the road. In this paper, a new hybrid optimization technique combining grey wolf optimization and crow search algorithms (GWO-CSA) is employed to compare an electric car model to the traditional particle swarm optimization (PSO) approach. The MATLAB simulation results demonstrate the vehicle's performance and tracking efficiency.</span>