Electric Vehicle Control with Integrated PID and Kalman Filtering for Improved Stability and Accuracy

Abstract

With the increasing usage of electric vehicles (EVs), the Market demand for control systems that increase travel speed is also on the rise. Conventional PID control algorithms are susceptible to sensor noise and environmental uncertainties, which degrade performance. Therefore, the PID control system can be optimized based on Kalman filtering. This study aims at putting into practice the idea of a hybrid system, engineering both the PID control and the Kalman filter, to bring vehicle performance to a higher level, especially in the matters of path planning, speed control, and vehicle stability. The fusion system is designed to take the advantage of both techniques, with the PID acting as the main controller and the Kalman filter providing the necessary information for sensor data fusion. MATLAB based Simulations underscore this hybrid technique as sharp decrease in noise, reinforced stability of the vehicle and accurate controllability of the vehicle can be observed. The results indicate the 30% noise-induced fluctuations and the enhancement of the vehicle tracking trajectory. Meanwhile, this research stands as a basis for further development in autonomous electric vehicle technologies and implies that the PID Kalman hybrid control system is an auspicious approach to obtaining more safe and accurate control in reality.