A K-Means Clustering-Based Optimization of IT2FL for Improved Regenerative Braking Performance

Optimizing regenerative braking systems in electric vehicles presents a complex challenge requiring precise and adaptable control. This research proposes employing K-Means Clustering to the Interval Type-2 Fuzzy Logic (IT2FL) membership functions, thereby improving overall regenerative braking efficiency and enhancing driving range. This research demonstrates that implementing a K-Means in the IT2FL control system in electric vehicles effectively recovers 35.84% of energy. By incorporating a K-means-based approach, the IT2FL controller further enhances vehicle range by 43 kilometers, achieving a total range of 108 kilometers. In comparison, an IT2FL system without K-means recovered only 26.67% of energy and achieved a total range of 93 kilometers. Notably, the IT2FL controller with K-means enabled the electric vehicle to attain an energy efficiency of 22 kilometers per kilowatt-hour. These findings underscore the significant improvement in maximum range offered by the IT2FL controller with K-means compared to alternative methods.