Optimal performance assessment of intelligent controllers used in solar-powered electric vehicle





solar power, proportional-integral derivative controller, artificial neural network controller, fuzzy logic controller


Introduction. Increasing vehicle numbers, coupled with their increased consumption of fossil fuels, have drawn great concern about their detrimental environmental impacts. Alternative energy sources have been the subject of extensive research and development. Due to its high energy density, zero emissions, and use of sustainable fuels, the battery is widely considered one of the most promising solutions for automobile applications. A major obstacle to its commercialization is the battery's high cost and low power density. Purpose. Implementing a control system is the primary objective of this work, which is employed to change the energy sources in hybrid energy storage system about the load applied to the drive. Novelty. To meet the control objective, a speed condition-based controller is designed by considering four separate math functions and is programmed based on different speed ranges. On the other hand, the conventional/intelligent controller is also considered to develop the switching signals related to the DC-DC converter’s output and applied the actual value. Methods. According to the proposed control strategy, the adopted speed condition based controller is a combined conventional/intelligent controller to meet the control object. Practical value. In this work, three different hybrid controllers adopted speed condition based controller with artificial neural network controller, adopted speed condition based controller with fuzzy logic controller, and adopted speed condition based controller with proportional-integral derivative controller are designed and applied separately and obtain the results at different load conditions in MATLAB/Simulink environment. Three hybrid controller’s execution is assessed based on time-domain specifications.

Author Biographies

R. S. Kumar, Keshav Memorial Institute of Technology

Assistant Professor, Department of Electrical and Electronics Engineering

C. S. R. Reddy, B V Raju Institute of Technology

Professor, Department of Electrical and Electronics Engineering

B. M. Chandra, QIS College of Engineering and Technology

Professor, Department of Electrical and Electronics Engineering


Chandra Mouli G.R., Bauer P., Zeman M. System design for a solar powered electric vehicle charging station for workplaces. Applied Energy, 2016, vol. 168, pp. 434-443. doi: https://doi.org/10.1016/j.apenergy.2016.01.110.

Kydd P.H., Anstrom J.R., Heitmann P.D., Komara K.J., Crouse M.E. Vehicle-Solar-Grid Integration: Concept and Construction. IEEE Power and Energy Technology Systems Journal, 2016, vol. 3, no. 3, pp. 81-88. doi: https://doi.org/10.1109/JPETS.2016.2558471.

Kim N., Biglarbegian M., Parkhideh B. Flexible high efficiency battery-ready PV inverter for rooftop systems. 2018 IEEE Applied Power Electronics Conference and Exposition (APEC), 2018, pp. 3244-3249. doi: https://doi.org/10.1109/APEC.2018.8341567.

Amin Bambang R.T., Rohman A.S., Dronkers C.J., Ortega R., Sasongko A. Energy Management of Fuel Cell/Battery/Supercapacitor Hybrid Power Sources Using Model Predictive Control. IEEE Transactions on Industrial Informatics, 2014, vol. 10, no. 4, pp. 1992-2002. doi: https://doi.org/10.1109/TII.2014.2333873.

Katuri R., Gorantla S. Realization of prototype hardware model with a novel control technique used in electric vehicle application. Electrical Engineering, 2020, vol. 102, no. 4, pp. 2539-2551. doi: https://doi.org/10.1007/s00202-020-01052-0.

Katuri R., Gorantla S. Design and Comparative Analysis of Controllers Implemented to Hybrid Energy Storage System Based Solar-powered Electric Vehicle. IETE Journal of Research, 2021, pp. 1-23. doi: https://doi.org/10.1080/03772063.2021.1941328.

Akar F., Tavlasoglu Y., Vural B. An Energy Management Strategy for a Concept Battery/Ultracapacitor Electric Vehicle With Improved Battery Life. IEEE Transactions on Transportation Electrification, 2017, vol. 3, no. 1, pp. 191-200. doi: https://doi.org/10.1109/TTE.2016.2638640.

Kollimalla S.K., Ukil A., Beng G.H., Manandhar U., Tummuru N.R. Optimization of charge/discharge rates of a battery using a two stage rate-limit control. 2017 IEEE Power & Energy Society General Meeting, 2017, pp. 1-1. doi: https://doi.org/10.1109/PESGM.2017.8273807.

Katuri R., Gorantla S. Optimal Performance of Lithium-Ion Battery and Ultra-Capacitor with A Novel Control Technique Used In E-Vehicles. Journal of New Materials for Electrochemical Systems, 2020, vol. 23, no. 2, pp. 139-150. doi: https://doi.org/10.14447/jnmes.v23i2.a11.

Katuri R., Rao G.S. Modelling and simulation of Math function based controller combined with PID for smooth switching between the battery and ultracapacitor. Australian Journal of Electrical and Electronics Engineering, 2019, vol. 16, no. 3, pp. 163-175. doi: https://doi.org/10.1080/1448837X.2019.1640009.

Meradji M., Cecati C., Wang G., Xu D. Dynamic modeling and optimal control for hybrid electric vehicle drivetrain. 2016 IEEE International Conference on Industrial Technology (ICIT), 2016, pp. 1424-1429. doi: https://doi.org/10.1109/ICIT.2016.7474967.

Li X., Ma R., Wang L., Wang S., Hui D. Energy Management Strategy for Hybrid Energy Storage Systems with Echelon-use Power Battery. 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2020, pp. 1-2. doi: https://doi.org/10.1109/ASEMD49065.2020.9276135.

Zhou H., Zhou Y., Hu J., Yang G., Xie D., Xue Y., Nordstrom L. LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers. Journal of Modern Power Systems and Clean Energy, 2021, vol. 9, no. 5, pp. 1205-1216. doi: https://doi.org/10.35833/MPCE.2020.000501.

Byrne R.H., Nguyen T.A., Copp D.A., Chalamala B.R., Gyuk I. Energy Management and Optimization Methods for Grid Energy Storage Systems. IEEE Access, 2018, vol. 6, pp. 13231-13260. doi: https://doi.org/10.1109/ACCESS.2017.2741578.

Li X., Wang S. Energy management and operational control methods for grid battery energy storage systems. CSEE Journal of Power and Energy Systems, 2021, no. 5, pp. 1026-1040. doi: https://doi.org/10.17775/CSEEJPES.2019.00160.

Regad M., Helaimi M., Taleb R., Gabbar H., Othman A. Optimal frequency control in microgrid system using fractional order PID controller using krill herd algorithm. Electrical Engineering & Electromechanics, 2020, no. 2, pp. 68-74. doi: https://doi.org/10.20998/2074-272X.2020.2.11.

Slama F., Radjeai H., Mouassa S., Chouder A. New algorithm for energy dispatch scheduling of grid-connected solar photovoltaic system with battery storage system. Electrical Engineering & Electromechanics, 2021, no. 1, pp. 27-34. doi: https://doi.org/10.20998/2074-272X.2021.1.05.

Pakkiraiah B., Sukumar G.D. A new modified MPPT controller for improved performance of an asynchronous motor drive under variable irradiance and variable temperature. International Journal of Computers and Applications, 2016, vol. 38, no. 2-3, pp. 61-74. doi: https://doi.org/10.1080/1206212X.2016.1188586.

Pakkiraiah B., Durga Sukumar G. Enhanced Performance of an Asynchronous Motor Drive with a New Modified Adaptive Neuro-Fuzzy Inference System-Based MPPT Controller in Interfacing with dSPACE DS-1104. International Journal of Fuzzy Systems, 2017, vol. 19, no. 6, pp. 1950-1965. doi: https://doi.org/10.1007/s40815-016-0287-5.




How to Cite

Kumar, R. S., Reddy, C. S. R., & Chandra, B. M. (2023). Optimal performance assessment of intelligent controllers used in solar-powered electric vehicle. Electrical Engineering & Electromechanics, (2), 20–26. https://doi.org/10.20998/2074-272X.2023.2.04



Electrotechnical complexes and Systems