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<p>The Zeta converter has emerged as a power conversion topology for electric vehicle (EV) fast charging systems due to its ability to provide both step-up and step-down voltage regulation with high efficiency and reduced component stress. DC fast charging plays a crucial role in accelerating EV adoption by enhancing user convenience, economic feasibility, and environmental sustainability. To further improve the performance of Zeta converters in such demanding applications, this paper introduces advanced control strategies based on Fractional order proportional integral derivative (FOPID) and Fractional-Order Internal Model Control (FOIMC) technique, optimized using the Grey Wolf Optimizer (GWO) and a hybrid GWO–Artificial Neural Network (GWO–ANN) approach. These optimization methods are employed to fine-tune controller parameters for achieving superior voltage regulation, faster transient response, and enhanced robustness under varying operating conditions. A comprehensive simulation study is carried out on a Zeta converter model configured for DC fast charging scenarios, with results compared against conventional integer-order controllers. The analysis, based on key time-domain parameters and performance indices, demonstrates that the optimized FOPID and FOIMC controllers deliver significant improvements in efficiency, stability, and dynamic performance. The integration of GWO and GWO–ANN optimization techniques further enhances control accuracy, making the Zeta converter a more reliable and effective interface for next-generation DC fast charging systems.</p>

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A comparative study of FOPID and FOIMC for Zeta converter using GWO–artificial neural networks hybrid optimization

  • 期刊论文

  • 发表日期：

    2026年7月9日

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  • Akriti Jain,
  • Kumari Shipra,
  • Rakesh Maurya

摘要

The Zeta converter has emerged as a power conversion topology for electric vehicle (EV) fast charging systems due to its ability to provide both step-up and step-down voltage regulation with high efficiency and reduced component stress. DC fast charging plays a crucial role in accelerating EV adoption by enhancing user convenience, economic feasibility, and environmental sustainability. To further improve the performance of Zeta converters in such demanding applications, this paper introduces advanced control strategies based on Fractional order proportional integral derivative (FOPID) and Fractional-Order Internal Model Control (FOIMC) technique, optimized using the Grey Wolf Optimizer (GWO) and a hybrid GWO–Artificial Neural Network (GWO–ANN) approach. These optimization methods are employed to fine-tune controller parameters for achieving superior voltage regulation, faster transient response, and enhanced robustness under varying operating conditions. A comprehensive simulation study is carried out on a Zeta converter model configured for DC fast charging scenarios, with results compared against conventional integer-order controllers. The analysis, based on key time-domain parameters and performance indices, demonstrates that the optimized FOPID and FOIMC controllers deliver significant improvements in efficiency, stability, and dynamic performance. The integration of GWO and GWO–ANN optimization techniques further enhances control accuracy, making the Zeta converter a more reliable and effective interface for next-generation DC fast charging systems.