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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.01555 |
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| _version_ | 1866916640740868096 |
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| author | Ma, Kang Liu, Xiulan Chen, Xi Liu, Xiaohu Zhao, Wei Peng, Lisha Huang, Songling Li, Shisong |
| author_facet | Ma, Kang Liu, Xiulan Chen, Xi Liu, Xiaohu Zhao, Wei Peng, Lisha Huang, Songling Li, Shisong |
| contents | Accurate electric energy metering (EEM) of fast charging stations (FCSs), serving as critical infrastructure in the electric vehicle (EV) industry and as significant carriers of vehicle-to-grid (V2G) technology, is the cornerstone for ensuring fair electric energy transactions. Traditional on-site verification methods, constrained by their high costs and low efficiency, struggle to keep pace with the rapid global expansion of FCSs. In response, this paper adopts a data-driven approach and proposes the measuring performance comparison (MPC) method. By utilizing the estimation value of state-of-charge (SOC) as a medium, MPC establishes comparison chains of EEM performance of multiple FCSs. Therefore, the estimation of EEM errors for FCSs with high efficiency is enabled. Moreover, this paper summarizes the interfering factors of estimation results and establishes corresponding error models and uncertainty models. Also, a method for discriminating whether there are EEM performance defects in FCSs is proposed. Finally, the feasibility of MPC method is validated, with results indicating that for FCSs with an accuracy grade of 2\%, the discriminative accuracy exceeds 95\%. The MPC provides a viable approach for the online monitoring of EEM performance for FCSs, laying a foundation for a fair and just electricity trading market. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Metering Error Estimation of Fast-Charging Stations Using Charging Data Analytics Ma, Kang Liu, Xiulan Chen, Xi Liu, Xiaohu Zhao, Wei Peng, Lisha Huang, Songling Li, Shisong Signal Processing Accurate electric energy metering (EEM) of fast charging stations (FCSs), serving as critical infrastructure in the electric vehicle (EV) industry and as significant carriers of vehicle-to-grid (V2G) technology, is the cornerstone for ensuring fair electric energy transactions. Traditional on-site verification methods, constrained by their high costs and low efficiency, struggle to keep pace with the rapid global expansion of FCSs. In response, this paper adopts a data-driven approach and proposes the measuring performance comparison (MPC) method. By utilizing the estimation value of state-of-charge (SOC) as a medium, MPC establishes comparison chains of EEM performance of multiple FCSs. Therefore, the estimation of EEM errors for FCSs with high efficiency is enabled. Moreover, this paper summarizes the interfering factors of estimation results and establishes corresponding error models and uncertainty models. Also, a method for discriminating whether there are EEM performance defects in FCSs is proposed. Finally, the feasibility of MPC method is validated, with results indicating that for FCSs with an accuracy grade of 2\%, the discriminative accuracy exceeds 95\%. The MPC provides a viable approach for the online monitoring of EEM performance for FCSs, laying a foundation for a fair and just electricity trading market. |
| title | Metering Error Estimation of Fast-Charging Stations Using Charging Data Analytics |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.01555 |