FOPID TABANLI ELEKTRİKLİ ARAÇ HIZ KONTROLÜNÜN SEZGİSEL OPTİMİZASYON ALGORİTMALARIYLA KARŞILAŞTIRMALI ANALİZİ

Öz

Bu çalışmada, bir elektrikli aracın hız kontrolü için kullanılan fraksiyonel mertebeli PID (FOPID) kontrolörün parametreleri, beş farklı sezgisel optimizasyon algoritması ile optimize edilmiştir: Genetik Algoritma (GA), Gri Kurt Optimizasyonu (GWO), Harris Hawk Optimizasyonu (HHO), Parçacık Sürü Optimizasyonu (PSO) ve Salp Sürüsü Algoritması (SSA). Kontrol sistemi, MATLAB/Simulink ortamında modellenmiş ve kapalı döngüde hız kontrol performansı test edilmiştir. Optimizasyon sürecinde, performans kriterleri olarak yüzde aşım (%OS), yerleşme süresi (settling time), yükselme süresi (rise time) ve ortalama kare hatası (MSE) kullanılmıştır. Her algoritma ile elde edilen sonuçlar, belirtilen performans kriterleri açısından karşılaştırmalı olarak değerlendirilmiştir. Sonuçlar, farklı algoritmaların FOPID parametre optimizasyonundaki başarımlarının uygulamaya ve performans kriterlerine göre değişkenlik gösterdiğini ortaya koymuştur. Elde edilen bulgular, elektrikli araçlarda hız kontrol performansını artırmaya yönelik algoritma seçiminde önemli bir referans sağlamaktadır.

Anahtar Kelimeler

• Elektrikli araç
• KDPID
• Hız kontrolü
• GA
• GWO
• HHO
• PSO

Comparative Analysis of FOPID-Based Electric Vehicle Speed Control Using Heuristic Optimization Algorithms

Abstract

In this study, the parameters of a Fractional Order PID (FOPID) controller used for the speed control of an electric vehicle (EV) were optimized using five different heuristic optimization algorithms: Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA). The control system was modeled in the MATLAB/Simulink environment, and the speed control performance was tested in a closed-loop configuration. In the optimization process, performance criteria such as percentage overshoot (%OS), settling time (t_s), rise time 〖(t〗_r), and mean squared error (MSE) were used. The results obtained with each algorithm were evaluated comparatively in terms of the specified performance criteria. The results revealed that the performance of different algorithms in FOPID parameter optimization varies depending on the application and performance criteria. The findings provide an important reference for the selection of appropriate algorithms to enhance speed control performance in electric vehicles.

Keywords

• Electric vehicle
• FOPID
• Speed control
• GA
• GWO
• HHO
• PSO

Kaynakça

1. Shen, S., Zhang, M., Hao, Y., et al. Comparative review on enabling technologies, environmental impacts, interoperability and charging strategies of electric vehicles. Energy Reports. 2023; 9, 761–787.
2. Zhang, Y., Shen, D., Zhao, Z. Global trends and policy support for electric vehicle adoption: progress in battery technologies, range, and charging infrastructure. Energy Policy. 2024; 175, 117266.
3. Guo-Feng, W., Jian-Min, Z., Cun-Sheng, L. Brushless DC motor control strategies for electric vehicle applications: effect on energy efficiency and drive performance. IEEE Transactions on Vehicular Technology. 2019; 68(5), 4503–4512.
4. Podlubny, I. Fractional-order systems and PIλDμ controllers. IEEE Transactions on Automatic Control. 1999; 44(1), 208–214.
5. Idir, A., et al. Performance analysis of fractionalized order PID controller based on metaheuristic optimisation algorithms for vehicle cruise control systems. Science, Engineering and Technology. 2024; 5(1), 1–14.
6. Panda, S., Nanda, J., Padhy, N. P. A genetic algorithm based PID controller for speed control of an electric vehicle. Engineering Science and Technology. 2020; 23(3), 763–774.
7. Benaouadj, M., Boukhnifer, M., Benmahammed, K. Optimal FOPID controller design using PSO for electric vehicle speed regulation. ISA Transactions. 2021; 115, 312–322.
8. Demirtas, M., Ahmad, F. Fractional fuzzy PI controller using particle swarm optimization to improve power factor by boost converter. IJOCTA. 2023; 13(2), 205–213.

9. Özyetkin, M. M., Birdane, H. Processes with fractional order delay and PI controller design using particle swarm optimization. IJOCTA. 2023; 13(1), 81–91.
10. Precup, R. E., et al. Novel design and tuning of HHO-based fractional-order controllers for process control. IEEE Transactions on Industrial Informatics. 2022; 18(5), 3207–3216.
11. Panda, S., Padhy, N. P. Salp swarm algorithm-based optimal tuning of FOPID controllers in load frequency control. Electric Power Components and Systems. 2021; 49(10), 915–928.
12. Touhami, N., et al. Optimized fractional-order direct torque control with SVM for 2WD EVs. International Journal of Electrical and Computer Engineering. 2025; 15(5), 4409–4420.
13. Khooban, M., et al. Multi-objective fuzzy fractional-order controller for EV speed control. IET Science, Measurement and Technology. 2017; 11(3), 249–261.
14. Azzawi, H. A., et al. Performance and robustness enhancement of fractional order controller for EVs. Journal of Engineering Science and Applications. 2023; 56(5), 879–887.
15. Petráš, I. Tuning of fractional-order controllers: a survey. Proceedings of ICCC. 2025; 1–6.
16. Das, S. Functional fractional calculus for system identification and controls. Springer. 2011.
17. Ogata, K. Modern control engineering. Prentice Hall. 2010.
18. Nise, N. S. Control systems engineering. Wiley. 2011.
19. Goldberg, D. E. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. 1989.
20. Mirjalili, S., Mirjalili, S. M., Lewis, A. Grey wolf optimizer. Advances in Engineering Software. 2014; 69, 46–61.
21. Heidari, A. A., et al. Harris hawks optimization. Future Generation Computer Systems. 2019; 97, 849–872.
22. Kennedy, J., Eberhart, R. Particle swarm optimization. Proceedings of ICNN’95. 1995; 1942–1948.
23. Mirjalili, S., Gandomi, A. H. Salp swarm algorithm. Advances in Engineering Software. 2017; 114, 163–191.