Fuzzy Logic Control and Neural Network Approach for Optimal Electric Vehicle Energy Management

With the growing need for environmentally friendly transportation options, electric vehicles (EVs) have emerged as a possible alternative to traditional fossil fuel-powered automobiles. However, widespread adoption of EVs presents issues in terms of energy management and efficiency. To solve these problems, this study proposes a novel energy management system (EMS) for an EV that includes a photovoltaic (PV) array, battery and ultracapacitor. The EMS uses artificial neural network (ANN) and fuzzy logic control (FLC) approaches to minimize energy consumption and improve the EV's overall performance.

The proposed EMS is intended to intelligently control energy flow between the EV’s mentioned components. The system considers parameters such as energy generation, demand, and storage capacity to ensure optimal performance under different driving situations. The ANN model predicts energy generation from the PV array and the vehicle's energy demand, whilst the FLC optimizes power distribution among the various energy sources in real time.

One of the most important aspects of the proposed EMS is its capacity to adapt to changing environmental and driving conditions. Compared to standard EMS techniques, the ANN-FLC-based system has superior energy efficiency, a longer battery life, and lower running expenses. The PV array integration also enables the EV to partially recharge its battery with solar energy, thus lowering its reliance on the grid.

The suggested EMS provides a comprehensive solution for the efficient management of energy in EVs. The system optimizes energy efficiency, improves driving performance, and reduces environmental impact by utilizing ANN and FLC technologies. In addition, it has the potential to considerably advance the subject of sustainable transportation while also promoting the mainstream adoption of EVs as a viable alternative to regular automobiles.