ANFIS Algorithm for E-Vehicle Power Management System

In light of the growing popularity of electric vehicles (EV) due to their environmental friendliness, studies on how to regulate their power have become more common. A pure EV uses only electric power, generated by an electric motor. The motor's performance characteristics determine the vehicle's total tractive motion. Controlling voltage and current is essential for keeping the EV system at the required power. Despite the prevalence of PID controllers in modern EVs, maintaining the necessary power can be challenging and leads to an inferior control effect. To address this issue, this paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control technique for EVs, which enhances the motor's anti-interference ability and maintain the required power. First, the motor is modelled mathematically and analysed. Second, PID control is used to realize power management of the motor. Next, the ANFIS control technique is implemented to enhance the EV's power output. MATLAB/SIMULINK was used to conduct the simulation experiments, and a variety of error metrics including Integral Square Error (ISE), Integral Time Square Error (ITSE), Integral Absolute Error (IAE), and Integral Time Absolute Error (ITAE) were introduced to show which controller worked best. The outcomes demonstrate that the ANFIS control system is more responsive and accurate in managing the EV power system.