Optimized MIMO controller based high gain DC-DC converter for electric vehicle applications
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Pdf
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Author |
K. S. Kavin, V. Thanka Jebarsan, I. Shivasankkar and S. Ragu
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e-ISSN |
1819-6608 |
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On Pages
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1705-1720
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Volume No. |
20
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Issue No. |
19
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Issue Date |
January 10, 2026
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DOI |
https://doi.org/10.59018/1025193
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Keywords |
electric vehicles, photovoltaic, KSK converter, MIMO-PI controller GWO algorithm, MATLAB/Simulink.
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Abstract
Electric vehicles (EVs) have gained substantial attention due to their provision of eco-friendly transportation options and their contribution to the diminution of carbon emissions. The integration of photovoltaic (PV) systems to power EVs presents a promising strategy for further mitigating carbon dioxide emissions. However, the intermittent nature of PV systems leads to a decrease in voltage output, thereby representing a significant limitation of this integrated system. Therefore, this paper concentrates on the design and implementation of an optimized Multiple-Input Multiple-Output (MIMO) based Proportional Integral (PI) controller for a high gain DC-DC converter precisely customized for EV applications. The high gain converter is vital to satisfy the high voltage requirements of EVs. Hence, in order to achieve this, the proposed work develops a novel high gain KSK (K.S. Kavin) converter, which incorporates a boosting (voltage multiplier) cell before the load for enhancing the voltage gain and efficiency. Furthermore, the proposed MIMO-PI controller aims to augment the performance and effectiveness of the converter by dynamically regulating multiple input and output parameters simultaneously. The Grey Wolf Optimization (GWO) algorithm develops to optimize the parameters of the MIMO-PI controller, which offers rapid convergence speed during the optimization process. To validate the prominence of the proposed work, MATLAB/Simulink is used, and the results validate the efficacy of the proposed work in optimizing energy conversion and facilitating seamless integration of a high gain KSK converter into EV applications.
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