Hybrid Fuzzy-Neural Network Approach for Energy Management in Solar-Powered Electric Vehicle Systems

Abstract

A hybrid fuzzy-neural network method for effective energy management in solar-powered electric vehicle systems is presented in this paper. The combination of artificial neural networks with fuzzy logic improves decision-making in situations with unpredictable environmental circumstances and changing energy requirements. The suggested concept cleverly balances the use of energy between battery storage, solar panels, and vehicle load. The hybrid model performs noticeably better than traditional machine learning methods like Support Vector Machine, Decision Tree, Random Forest, and standalone Artificial Neural Network, according to experimental assessment. It ensures dependable and adaptable performance by achieving excellent accuracy, precision, recall, F1-score, and AUC values. The technology guarantees sustainable power distribution, extends battery life, and efficiently minimizes energy waste. This strategy contributes to ecologically friendly and energy-efficient electric mobility solutions and is ideal for next-generation smart transportation systems.