Enhancing EV Efficiency with Adaptive Neuro Fuzzy Inference Systems

The swift expansion of electric vehicles (EVs) underscores the need for advanced energy management systems to optimize performance and mitigate range limitations. This paper introduces an Energy Management System (EMS) employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to enhance the efficiency of electrical energy utilization in EVs. The primary goals are to improve overall energy efficiency, extend driving range, and bolster EV reliability. The system takes advantage of ANFIS's adaptive and self-learning abilities to dynamically manage power distribution among key components—such as the battery, fuel cell, and motor—tailored to varying driving conditions. Simulation outcomes and comparative studies highlight the effectiveness of the ANFIS-based EMS in enhancing EV performance metrics. This novel approach aims to achieve greater efficiency, longer driving ranges, and enhanced reliability, thereby encouraging the adoption of sustainable transportation solutions.