Comprehensive DEA-Based Evaluation of Charging Station Operational Efficiency

This study aims to evaluate the operational efficiency of electric vehicle (EV) charging stations and explore optimization strategies to enhance resource utilization and service performance. A systematic review approach was first applied to identify the main evaluation indicators and influencing factors from existing studies. Subsequently, a super-efficiency Data Envelopment Analysis (DEA) model was used to assess the efficiency of six EV charging stations in a certain City, China. The robustness analysis was carried out, and the output variables were replaced, and the evaluation results did not change. The results show substantial disparities in efficiency across stations: C1 exhibits the highest operational efficiency, while C3 performs the lowest. The inefficiencies primarily result from supply–demand mismatches and redundant capacity investment. Based on these findings, the study proposes both overall and localized optimization strategies to improve operational performance. The results provide valuable insights for urban energy infrastructure planning and contribute to the enhancement of high-quality, low-carbon transportation development in China.

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Publication Details

Journal
World Electric Vehicle Journal
Published
2025-11-09
DOI
https://doi.org/10.3390/wevj16110613
Primary Topic
Electric Vehicles and Infrastructure
Type
article
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article

Comprehensive DEA-Based Evaluation of Charging Station Operational Efficiency

Chunhua Jin, ZhongHao ZHANG, Zhen Yang, Yang Hu et al.
World Electric Vehicle Journal
Electric Vehicles and Infrastructure
article

Comprehensive DEA-Based Evaluation of Charging Station Operational Efficiency

Chunhua Jin, ZhongHao ZHANG, Zhen Yang, Yang Hu, Houzhi Li, Jinyu Wang, Jiawen Yan
article en

Abstract

This study aims to evaluate the operational efficiency of electric vehicle (EV) charging stations and explore optimization strategies to enhance resource utilization and service performance. A systematic review approach was first applied to identify the main evaluation indicators and influencing factors from existing studies. Subsequently, a super-efficiency Data Envelopment Analysis (DEA) model was used to assess the efficiency of six EV charging stations in a certain City, China. The robustness analysis was carried out, and the output variables were replaced, and the evaluation results did not change. The results show substantial disparities in efficiency across stations: C1 exhibits the highest operational efficiency, while C3 performs the lowest. The inefficiencies primarily result from supply–demand mismatches and redundant capacity investment. Based on these findings, the study proposes both overall and localized optimization strategies to improve operational performance. The results provide valuable insights for urban energy infrastructure planning and contribute to the enhancement of high-quality, low-carbon transportation development in China.

World Electric Vehicle JournalVol. 16(11)
State Grid Corporation of China (China) (CN), Beijing Information Science & Technology University (CN)
Openalex Percentile: Top 67%
Electric Vehicles and Infrastructure
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Comprehensive DEA-Based Evaluation of Charging Station Operational Efficiency — Chunhua Jin, ZhongHao ZHANG, et al. · World Electric Vehicle Journal (2025) | TGRS Research Map | TGRS