A spatiotemporal charging recommendation strategy for electric vehicles considering grid connection mode differences

• Recommends charging time periods considering differences in electric vehicle types. • A charging station selection method considering multiple factors is proposed. • Further reduce charging costs by choosing the grid connection mode for the user. With the growing penetration of electric vehicles (EVs), there is an increasing demand for effective charging strategies to address range anxiety and charging challenges. This paper proposes a novel spatiotemporal charging recommendation strategy that considers grid connection mode differences, overcoming the limitations of conventional approaches that neglect temporal, spatial, and charging mode differences in EV charging behavior. First, this paper analyzes the passenger revenue and charging prices for each time period to determine the optimal charging time for EVs. Then, when the EV is in the recommended time period, the charging station (CS) is recommended with the goal of minimizing the overall cost by considering the time consumption, economic loss, and driving road impedance, and grid connection modes are recommended based on factors such as incentive price and battery loss after users arrive at the CS, guiding EVs to charge with the optimal grid connection mode. The effectiveness of the proposed strategy has been validated through simulations. The results demonstrate significant improvements in CS utilization balance, with individual EVs achieving up to 22.74 % reduction in charging costs and a 14.35 % decrease in overall EV costs, while simultaneously enhancing electric taxi operational revenue.

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Publication Details

Journal
International Journal of Electrical Power & Energy Systems
Published
2025-11-01
DOI
https://doi.org/10.1016/j.ijepes.2025.111345
Citations
1
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.73
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article

A spatiotemporal charging recommendation strategy for electric vehicles considering grid connection mode differences

Xu Wang, Gaiping Sun, Bin Wu, Ronghui Liu
1 citations
International Journal of Electrical Power & Energy Systems
Electric Vehicles and Infrastructure
0.73
article

A spatiotemporal charging recommendation strategy for electric vehicles considering grid connection mode differences

Xu Wang, Gaiping Sun, Bin Wu, Ronghui Liu
article en
1 citations

Abstract

• Recommends charging time periods considering differences in electric vehicle types. • A charging station selection method considering multiple factors is proposed. • Further reduce charging costs by choosing the grid connection mode for the user. With the growing penetration of electric vehicles (EVs), there is an increasing demand for effective charging strategies to address range anxiety and charging challenges. This paper proposes a novel spatiotemporal charging recommendation strategy that considers grid connection mode differences, overcoming the limitations of conventional approaches that neglect temporal, spatial, and charging mode differences in EV charging behavior. First, this paper analyzes the passenger revenue and charging prices for each time period to determine the optimal charging time for EVs. Then, when the EV is in the recommended time period, the charging station (CS) is recommended with the goal of minimizing the overall cost by considering the time consumption, economic loss, and driving road impedance, and grid connection modes are recommended based on factors such as incentive price and battery loss after users arrive at the CS, guiding EVs to charge with the optimal grid connection mode. The effectiveness of the proposed strategy has been validated through simulations. The results demonstrate significant improvements in CS utilization balance, with individual EVs achieving up to 22.74 % reduction in charging costs and a 14.35 % decrease in overall EV costs, while simultaneously enhancing electric taxi operational revenue.

International Journal of Electrical Power & Energy SystemsVol. 172
Shanghai Jiao Tong University (CN), Shanghai University of Electric Power (CN)
Openalex Percentile: Top 21%
Electric Vehicles and Infrastructure
0.73
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A spatiotemporal charging recommendation strategy for electric vehicles considering grid connection mode differences — Xu Wang, Gaiping Sun, et al. · International Journal of Electrical Power & Energy Systems (2025) | TGRS Research Map | TGRS