Dynamic and Multi-Objective Optimization of Fuzzy-Pi Controller for Optimal BLDC Motor Speed Control in EVS

An innovative algorithm for developing speed control and power efficiency in electric vehicles (EVs) with brushless direct current (BLDC) motors was presented in this approach. This is accomplished by carefully fine-tuning a fuzzy-PI controller utilizing a mix of fuzzy logic optimization approaches, dynamic weighting strategies, and self-adaptive egret swarm optimization (SA-ESO). The random walk with the conventional ESO has been modified to a Cauchy random walk, and the fuzzy-PI controller utilizes a sigmoidal membership function (MF). The controller is tuned to balance energy consumption and speed accuracy when considering several road conditions. To accommodate road gradients and permit the controller to adjust to varying terrain, dynamic weighting is termed a significant innovation. However, energy efficiency, smooth acceleration, speed control, and controller gain adjustments were all optimized simultaneously by a multi-objective optimization algorithm. The best controller parameters were found by effectively exploring the solution space utilizing Red Fox Optimization (RFO). The suggested strategy accomplishes adaptive speed control, improving energy efficiency and accuracy, by incorporating real-time road gradient data. The fuzzy-PI controller’s parameters are dynamically modified using an objective function that takes speed error, smoothness criteria, and energy consumption into account. Compared to conventional PI as well as fuzzy controllers, an optimized fuzzy-PI controller exhibits better stability, quicker convergence, and a lower mean squared error (MSE) in speed control. A more effective and reliable EV control system is the outcome of this all-encompassing strategy, which improves overall performance and lessens environmental impact.