Artificial Neural Networks Based Power Management for a Battery/Supercapacitor and Integrated Photovoltaic Hybrid Storage System for Electric Vehicles

Integrating solar energy in electric vehicles (EV) is expected to play a dominant role in the decarbonization of the transportation sector as well as reducing the charging costs. These integrated photovoltaic automobiles are particularly adapted for urban driving that is to say the driving range is limited. On that account, the use of a feasible energy storage system is necessary to boost the driving mileage. This paper uses a hybrid energy system, containing a battery as the main storage device and a supercapacitor (SC) as a backup. This combination was suggested in order to negate the former’s deficiencies. The use of a hybrid energy source leads to the necessity of introducing a robust power management strategy to guarantee the optimal power flow in electric vehicle components. An artificial neural network is then trained with model calculation for the power management approach for the traction chain. The results of the simulation indicate that the proposed system is efficient in improving the energy management of the vehicle. Moreover, the system's simplicity could potentially make it easier to implement in real-time using a DSP or a DSPACE platform.