Optimizing Energy Efficiency in Electric Vehicles Through Fuzzy Logic and Neural Network Algorithms

The introduction of electric vehicles (EVs) has represented a significant step forward in the development of environmentally sustainable transportation. The study addresses the critical need to enhance energy management systems in electric vehicles (EVs) by overcoming the limitations of traditional algorithms. Existing systems, which rely on rule-based controllers and basic predictive models, have poor flexibility and precision when it comes to dynamically optimizing energy efficiency. To revolutionize energy optimization in EVs, the proposed approach uses a hybrid model that combines fuzzy logic and neural networks. It tackles limits through a comprehensive analysis, the design of a fuzzy logic-based controller, and the development of a neural network model trained on historical data. TensorFlow-implemented simulation results in Python validate the reliability of the model by closely matching predicted energy usage in a range of driving scenarios. Real-world experiments with an instrumented EV prototype highlight the proposed system's advantages even further, giving noteworthy results—92% energy efficiency, 25 mile range extension, and 95% predicted accuracy. These findings support the system's ability to greatly improve energy efficiency and range extension in electric vehicles, therefore transforming sustainable transportation.