Automatic control algorithm for V2G frequency response mode of electric vehicle charging station

Abstract To solve the problem of excessive fluctuation of the V2G load of the electric vehicle charging pile, this paper proposes an automatic control algorithm for the V2G frequency response mode of the electric vehicle charging pile. The FPGA is used as the core processing unit to collect and process the V2G frequency data of the charging pile; the multiple linear regression model is used to predict the charging and discharging loads, and the automated two-layer control model is established; the sparrow search algorithm is used to optimize the weights and thresholds of the BP neural network to realize the control of the V2G frequency response mode. The experimental results show that the load prediction error of this method is small. After the automatic control, the load fluctuation of the charging pile is small, and the electric vehicle charging pile’s V2G frequency response control effect is better.

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Publication Details

Journal
Journal of Physics Conference Series
Published
2024-11-01
DOI
https://doi.org/10.1088/1742-6596/2876/1/012030
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
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Automatic control algorithm for V2G frequency response mode of electric vehicle charging station

Chao Zhang, Tianxing Qi, Jiangong Wu, Terry N. Guo et al.
Journal of Physics Conference Series
Electric Vehicles and Infrastructure
article

Automatic control algorithm for V2G frequency response mode of electric vehicle charging station

Chao Zhang, Tianxing Qi, Jiangong Wu, Terry N. Guo, Yusen Zhang
article en

Abstract

Abstract To solve the problem of excessive fluctuation of the V2G load of the electric vehicle charging pile, this paper proposes an automatic control algorithm for the V2G frequency response mode of the electric vehicle charging pile. The FPGA is used as the core processing unit to collect and process the V2G frequency data of the charging pile; the multiple linear regression model is used to predict the charging and discharging loads, and the automated two-layer control model is established; the sparrow search algorithm is used to optimize the weights and thresholds of the BP neural network to realize the control of the V2G frequency response mode. The experimental results show that the load prediction error of this method is small. After the automatic control, the load fluctuation of the charging pile is small, and the electric vehicle charging pile’s V2G frequency response control effect is better.

Journal of Physics Conference SeriesVol. 2876(1)
State Grid Corporation of China (China) (CN), Shanghai Electric (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 83%
Electric Vehicles and Infrastructure
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