Wheel Velocity Control for Electric Car with Kalman Filter and PID Optimization - Falaah, Fadjar Nur; Ma'arif, Alfian (Peneliti Teknologi Teknik Indonesia, 2025)
This research addresses the challenge of achieving precise rotational speed control for DC motors in electric vehicles, a critical factor for ensuring smooth operation, energy efficiency, and safety. The study integrates a Kalman Filter with a PID Controller to mitigate sensor noise and external disturbances while minimizing steady-state errors. The Kalman Filter effectively reduces noise from rotary encoder sensors, enabling accurate speed estimation with multiplier values of countPulseM1 = 20.0 and countPulseM2 = 40.9. Optimal Kalman Filter parameter ratios were identified as R = 10.0, Q = 0.0001 for motor M1 and R = 8.0, Q = 0.0001 for motor M2, which minimized noise but resulted in slower motor responses compared to lower ratio configurations. To address this limitation, the PID controller was fine-tuned, yielding optimal parameters of Kp = 1.1, Ki = 8.1, and Kd = 0.00036 for motor M1 and Kp = 0.9, Ki = 9.4, and Kd = 0.00009 for motor M2. These settings achieved a rise time of 0.13 seconds, overshoot of 8.69%, and steady-state error of -1.19%. Disturbance testing with a hall magnetic rotary encoder revealed motor M1 with a rise time of 0.27 seconds and M2 with 0.16 seconds, both showing robust responses but requiring faster recovery times for stabilization. While the combination of Kalman Filter and PID significantly enhances control accuracy, further improvements are necessary to reduce settling times and ensure greater stability under dynamic conditions. This work contributes valuable insights into advanced control techniques for electric vehicle drivetrains and robotic systems.