Intelligent Control of DC Microgrid Involving Multiple Renewables for Fast Charging Control of Electric Vehicles

Bajpai, R. S.; Srivastava, Apoorva; Singh, Amarjeet; Kumar, Shailesh

doi:10.1080/15325008.2024.2304149

Electric Power Components and Systems

2024

DOI: 10.1080/15325008.2024.2304149

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Intelligent Control of DC Microgrid Involving Multiple Renewables for Fast Charging Control of Electric Vehicles

R. S. Bajpai,

Apoorva Srivastava,

Amarjeet Singh

et al.

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2024

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Cited by 3 publications

References 32 publications

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Unveiling the determinants of battery electric vehicle performance: A systematic review and meta-analysis

Liu,

Shafique,

Luo

2024

Communications in Transportation Research

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Unveiling the determinants of battery electric vehicle performance: A systematic review and meta-analysis

Liu,

Shafique,

Luo

2024

Communications in Transportation Research

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Quantifying delayed climate mitigation benefits in electric and hydrogen fuel cell vehicle deployment for sustainable mobility

Liu,

Shafique,

Luo

2024

Sustainable Production and Consumption

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Fast Charging System of Electric Vehicle Using Optimized Isolated Multi-Port DC-DC Converter based on Modified Coati Optimization Algorithm

Thangavel,

Krishnamoorthy

2024

IJEER

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Once clean, renewable energy sources are used to charge the batteries in electric vehicles (EVs), the vehicles can produce zero gas emissions, greatly improving the environment. EVs and other distributed energy storage devices can be used in a smart microgrid to deliver energy to the loads throughout highest times, reducing the impact of load shading and improving the quality of the electricity. To achieve these goals of energy balance between EVs, the grid, and renewable energy sources, an isolated hybrid multiport converter is required. This paper develops an optimized isolated multi-port DC-DC converter for controlling power flow in multiple directions in an EV. This converter contains a dc-dc unidirectional converter, a bidirectional dc-dc converter, a triple active bridge (TAB), and a multi-port dual active bridge converter. Additionally, the Optimal Controller (OC) is developed to manage the power between the EV and the battery. The power flow is achieved by using the Modified Coati Optimization Algorithm (MCOA). In the MCOA, the optimal gain parameter of the converter is selected. In the planned technique, a bidirectional DC-DC converter can be considered to interconnect the EV battery towards deliver bidirectional power flow ability with the battery. The proposed approach is executed in MATLAB, and presentations are assessed by considering performance measures. Moreover, it is contrasted with the traditional approaches of particle swarm optimization (PSO) and grey wolf optimization (GWO).

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Intelligent Control of DC Microgrid Involving Multiple Renewables for Fast Charging Control of Electric Vehicles

Cited by 3 publications

References 32 publications

Unveiling the determinants of battery electric vehicle performance: A systematic review and meta-analysis

Unveiling the determinants of battery electric vehicle performance: A systematic review and meta-analysis

Quantifying delayed climate mitigation benefits in electric and hydrogen fuel cell vehicle deployment for sustainable mobility

Fast Charging System of Electric Vehicle Using Optimized Isolated Multi-Port DC-DC Converter based on Modified Coati Optimization Algorithm

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