Adaptive ANN-Controlled DC-Coupled Fast EV Charging Station with Wind–Solar Hybrid Energy Storage System

Engineering notes

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Chinese explanation / 中文解读

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Original abstract

This paper presents an adaptive artificial neural network (ANN)-controlled hierarchical strategy for a dc-coupled fast electric vehicle (EV) charging station integrated with a hybrid renewable energy system. The proposed system combines solar photovoltaic (PV), wind energy, and battery energy storage to enhance power reliability and reduce dependency on the utility grid. Unlike conventional approaches that rely on proportional–integral (PI) controllers, the proposed method employs an adaptive ANN-based controller to regulate the dc-bus voltage and dynamically coordinate power flow among multiple energy sources, EV loads, and the grid. The integration of wind energy alongside solar generation improves system reliability by compensating for the intermittency of solar power, while the battery storage system ensures energy balance and supports continuous operation under varying conditions. The ANN-based control strategy offers superior adaptability and fast dynamic response, enabling effective handling of uncertainties such as fluctuations in renewable generation, changes in EV charging demand, and variations in grid conditions. The proposed system supports bidirectional power flow, facilitating both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations, along with grid-supportive services such as voltage regulation and stability enhancement. The hierarchical control framework ensures optimal utilization of available renewable energy while minimizing grid power consumption. Simulation results demonstrate that the proposed ANN-controlled system achieves improved dc-bus voltage regulation, enhanced stability, reduced grid dependency, and better overall performance compared to conventional PI-controlled EV charging systems. The proposed approach provides a robust and intelligent solution for next-generation fast EV charging infrastructure.

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