An Adaptive Neuro-Fuzzy Inference System with the Comparative Metaheuristic Aware Renewable Energy-Based Multifunction Onboard Charger-Based Ev

DOI

10.2991/978-94-6239-654-8_20How to use a DOI?

Keywords

EV; ANFIS-PID controller; Self-Adaptive Snake Optimization algorithm; QZSC

Abstract

The integration of renewable energy sources for Electric Vehicle (EV) charging presents significant challenges, including energy intermittency, voltage fluctuations, and the need for efficient power management. Current electric vehicle (EV) charging systems often face challenges in maximizing charging efficiency, optimizing battery health, and reducing grid dependency, particularly in conditions with variable renewable energy sources. To address these challenges, this paper proposes an intelligent renewable energy-powered onboard EV charger that integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a PID controller tuned by using the self-adaptive Snake Optimization Algorithm (SA-SOA). The Snake Optimization algorithm is utilized to optimize the parameters of both the ANFIS- PID controllers, enabling the system to achieve superior performance by minimizing charging time, reducing energy losses, and improving system efficiency. The system uses a Quasi-Z Source Converter (QZSC) to maintain stable voltage regulation, ensuring optimal charging even with fluctuating input power from renewable sources. ANFIS is employed to dynamically adjust the charging parameters, allowing for adaptive decision-making. The PID controller ensures precise regulation of the charging process, improving system stability and responsiveness. The MATLAB/Simulink simulation platform is used to model and evaluate the performance of the proposed system, demonstrating its capability to optimize energy flow, stabilize voltage, and enhance charging efficiency under various conditions. The primary objective of this work is to develop a smart and energy-efficient onboard charger that maximizes the use of renewable energy, minimizes grid dependency, and ensures optimal charging conditions for EV batteries.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - G. Benedict Josly
AU  - S. Prakash
PY  - 2026
DA  - 2026/04/24
TI  - An Adaptive Neuro-Fuzzy Inference System with the Comparative Metaheuristic Aware Renewable Energy-Based Multifunction Onboard Charger-Based Ev
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 225
EP  - 242
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_20
DO  - 10.2991/978-94-6239-654-8_20
ID  - Josly2026
ER  -