Performance comparison of PI and AI-based controllers for solar PV-fed fast electric vehicle battery charging systems

Apoorva  Srivastava; Vikas  Yadav; Vinit  Yadav; Tarun  Nayyar; Shailesh Kumar  Yadav; Ayush  Asthana

doi:10.59400/esc4074

Performance comparison of PI and AI-based controllers for solar PV-fed fast electric vehicle battery charging systems

Apoorva Srivastava
Electrical Engineering Department, Babu Banarasi Das Institute of Technology & Management, Lucknow 226028, India
Vikas Yadav
Electrical Engineering Department, Babu Banarasi Das Institute of Technology & Management, Lucknow 226028, India
Vinit Yadav
Electrical Engineering Department, Babu Banarasi Das Institute of Technology & Management, Lucknow 226028, India
Tarun Nayyar
Electrical Engineering Department, Babu Banarasi Das Institute of Technology & Management, Lucknow 226028, India
Shailesh Kumar Yadav
Electrical Engineering Department, Babu Banarasi Das Institute of Technology & Management, Lucknow 226028, India
Ayush Asthana
Electrical Engineering Department, Babu Banarasi Das Institute of Technology & Management, Lucknow 226028, India

Article ID: 4074

Keywords: electric vehicles, fast charging, solar PV, artificial intelligence, PI controller, MATLAB/Simulink, DC microgrid

Abstract

The rapid growth of electric vehicles (EVs) has created a strong demand for efficient and fast charging solutions. However, conventional charging methods are time-consuming and place significant stress on the power grid when deployed on large scale. To address these challenges, this study proposes a standalone solar photovoltaic (PV)-based DC microgrid for fast EV charging. The system is designed to regulate charging using a DC-DC boost converter controlled by two strategies: a conventional Proportional-Integral (PI) controller and an Artificial Neural Network (ANN)-based controller. A detailed simulation model is developed in MATLAB/Simulink, including PV system parameters, converter specifications, and a lithium-ion battery modeled using a Thevenin equivalent circuit. The ANN controller is trained using real-time operating conditions such as irradiance, temperature, and state of charge (SoC). Performance is evaluated based on transient response, overshoot, settling time, steady-state error, and total harmonic distortion (THD). Results show that the ANN controller significantly improves system performance. Voltage overshoot is reduced from 10% to 2%, current overshoot from 20% to 4%, and THD from 6.8% to 2.1%. Additionally, the settling time is improved by approximately 57% compared to the PI controller. These findings demonstrate that AI-based control strategies provide a more efficient, stable, and reliable solution for renewable energy-based EV charging systems. The ANN controller reduced voltage overshoot from 10% to 2%, current overshoot from 20% to 4%, and THD from 6.8% to 2.1%, while improving settling time by up to 57%.

Published

2026-04-17

How to Cite

Srivastava, A., Yadav, V., Yadav, V., Nayyar, T., Yadav, S. K., & Asthana, A. (2026). Performance comparison of PI and AI-based controllers for solar PV-fed fast electric vehicle battery charging systems. Energy Storage and Conversion, 4(2). https://doi.org/10.59400/esc4074

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Vol. 4 No. 2 (2026): in progress

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

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