Open Access

ARTICLE

Measurement Error Estimation Method for DC Charging Stations of Electric Vehicles Based on Constrained Optimization Model

Quanquan Yu^1,*, Zhenhua Li^1,2, Zhenxing Li^1,2, Yanchun Xu¹, Xiaozhen Zhao³, Lin Wu⁴
1 College of Electrical Engineering & New Energy, China Three Gorges University, Yichang, China
2 Hubei Provincial Engineering Research Center of Intelligent Energy Technology, China Three Gorges University, Yichang, China
3 State Key Laboratory of Smart Power Distribution Equipment and System, Hebei University of Technology, Tianjin, China
4 State Grid Hubei Electric Power Company Technology and Training Center, Wuhan, China
* Corresponding Author: Quanquan Yu. Email:
(This article belongs to the Special Issue: Advances in Artificial Intelligence and Machine Learning for Next-Generation Energy Forecasting)

Energy Engineering https://doi.org/10.32604/ee.2026.079852

Received 29 January 2026; Accepted 15 April 2026; Published online 11 May 2026

Abstract

Electric vehicle (EV) charging piles serve as the critical link between operators and EV users, and their metering verification results are essential for safeguarding the legitimate rights and interests of both parties. With the ever-increasing number of charging piles, traditional on-site verification methods are becoming prohibitively costly and inefficient, making comprehensive coverage impractical. To address this issue, this paper proposes a constrained optimization-based method for estimating the metering operation error of DC charging piles. First, leveraging the topological structure of DC charging stations and the law of energy conservation, an analytical model linking station-level and pile-level metering errors is established. Considering that the AC/DC conversion efficiency within a charging pile is influenced by multiple factors, the model formulates a system of linear equations with the metering operation errors and AC/DC conversion efficiency as unknowns. Based on the type of charging piles and historical electricity consumption information, upper and lower bounds for both the metering errors and the conversion efficiency are determined. The resulting constrained optimization problem is then solved using the primal-dual interior point algorithm to obtain estimated metering errors for individual charging piles. Finally, the effectiveness of the proposed method is validated using real-world operational data from a DC charging station. This approach facilitates a paradigm shift in the metrological supervision of DC charging piles, transitioning from periodic batch testing to targeted anomaly detection.

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Keywords

Electric vehicle; DC charging infrastructure; metering operation error; error estimation; primal-dual interior point method

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