Research on new energy vehicle control technology based on genetic algorithm and fuzzy control
Cenglin Yao
School of Artificial Intelligence and Big Data, Wuhan Business University
DOI: https://doi.org/10.59429/esta.v13i3.15548
Keywords: new energy vehicle; genetic algorithm; fuzzy PID; longitudinal control; robust speed tracking
Abstract
This study develops a genetic-algorithm-optimized fuzzy proportional-integral-derivative (GA-fuzzy PID) controller for longitudinal speed tracking of new energy vehicles. A nonlinear simulation model includes aerodynamic, rolling, and grade resistance, actuator lag, force saturation, rate limitation, and measurement noise. A two-input fuzzy system adjusts the PID gains online, while a real-coded genetic algorithm optimizes baseline gains, modulation amplitudes, and input scaling factors. The controller is compared with conventional PID, expert fuzzy PID, and fixed-gain GA-PID under nominal, disturbance, and parameter-variation conditions. In the parameter-variation case, GA-fuzzy PID reduces RMSE, maximum error, and overshoot by 8.5%, 13.2%, and 23.5% relative to GA-PID. Across 100 independent randomized simulations, mean RMSE, maximum error, and overshoot decrease by 6.4%, 13.4%, and 13.6%, respectively, and the composite score improves in 97% of cases. The mean force-variation penalty is only 0.5%. Results support fuzzy gain scheduling as a transparent robustness enhancement, but all evidence is simulation-based and requires hardware or vehicle validation.
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