ANFIS-Based PWM Control for Electric Vehicle Speed Regulation Using an Intelligent ECU with ARM Processor

The main aim of this investigation is to optimize speed stability under dynamic load conditions, minimize energy usage, improve response time, and increase vehicle safety by enhanced PWM signal. The flaws of the traditional ECU will be solved with a high-performance intelligent ECU which comprises the ARM Processor with supporting Adaptive Neuro-Fuzzy Inference System algorithms. In order to optimize motor performance by the design and development of an intelligent and energy-efficient electronic control unit (ECU). The trained Neuro fuzzy inference model will provide the efficient PWM pulses according to the accelerator pedal signal of the EV by using bare metal coding. This proposed method produced a well enhanced output of 91.2% energy efficiency, Speed Regulation Accuracy 98.7 % also error will be minimized by 1.1% from 3.2% using Embedded Adaptive Neuro-Fuzzy Inference System in STM32 Controller. This proposed system enhances electric vehicles speed optimization and energy efficiency. The ANFIS algorithm uses the greater number of iteration and signal sampling which provides the optimized output and minimizing errors to enhance the promotion of sustainable EV technology development.