Fuzzy based DC Fast Charging for Electric Vehicles

Electric vehicles (EVs) can serve as distributed energy storage systems, providing various regulation services to the smart grid. To make a significant impact on the grid, the approach is to manage a fleet of EVs collectively through a new role called an aggregator.The aggregator monitors each EV's availability, driving patterns, and state of charge (SOC) to determine the optimal active and/or reactive power set-points for each vehicle within a specific time frame. This paper proposes reference charging currents for each EV based on the individual set-points provided by the aggregator. It compares different control strategies for bidirectional active power management under both ideal and distorted conditions. While the charging control for each EVis decentralized, a separate central control system handles the power transfer from theAC grid to the DC bus.The power exchange does not depend on communication links between the vehicles and the charging station, and a smooth transition to vehicle-to-grid (V2G) mode is also feasible.The Grid Support Station (GSS) consists of multiple Discharging Units (DUs), which enable the transfer of both active and reactive power from the EVs’ batteries to the grid. This paper introduces a fuzzy-based control method for regulating both active and reactive power from EVs to support the grid. The power flow from the GSS to the grid is determined by the node voltage and the available energy within the GSS. Simulations in Matlab/Simulink demonstrate the performance of the proposed model, highlighting the control system’s capability for fast DC charging and vehicle-to-grid operation. The results validate the feasibility and effectiveness of the approach.

Publication Details

Journal
Computer Science Engineering and Technology
Published
2025-04-02
DOI
https://doi.org/10.46632/cset/3/1/4
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
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article

Fuzzy based DC Fast Charging for Electric Vehicles

Computer Science Engineering and Technology
Advanced Battery Technologies Research
article

Fuzzy based DC Fast Charging for Electric Vehicles

article en

Abstract

Electric vehicles (EVs) can serve as distributed energy storage systems, providing various regulation services to the smart grid. To make a significant impact on the grid, the approach is to manage a fleet of EVs collectively through a new role called an aggregator.The aggregator monitors each EV's availability, driving patterns, and state of charge (SOC) to determine the optimal active and/or reactive power set-points for each vehicle within a specific time frame. This paper proposes reference charging currents for each EV based on the individual set-points provided by the aggregator. It compares different control strategies for bidirectional active power management under both ideal and distorted conditions. While the charging control for each EVis decentralized, a separate central control system handles the power transfer from theAC grid to the DC bus.The power exchange does not depend on communication links between the vehicles and the charging station, and a smooth transition to vehicle-to-grid (V2G) mode is also feasible.The Grid Support Station (GSS) consists of multiple Discharging Units (DUs), which enable the transfer of both active and reactive power from the EVs’ batteries to the grid. This paper introduces a fuzzy-based control method for regulating both active and reactive power from EVs to support the grid. The power flow from the GSS to the grid is determined by the node voltage and the available energy within the GSS. Simulations in Matlab/Simulink demonstrate the performance of the proposed model, highlighting the control system’s capability for fast DC charging and vehicle-to-grid operation. The results validate the feasibility and effectiveness of the approach.

Computer Science Engineering and TechnologyVol. 3(1 march 2025)
Openalex Percentile: Top 94%
Advanced Battery Technologies Research
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Fuzzy based DC Fast Charging for Electric Vehicles · Computer Science Engineering and Technology (2025) | TGRS Research Map | TGRS