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Volume 16
Issue 8
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Spatially Aware Machine Learning Method for Locating Electric Vehicle Charging Stations

by
Yanyan Huang
1,
Hangyi Ren
1,*,
Xudong Jia
2,*,
Xianyu Yu
1,
Dong Xie
3,
You Zou
1,
Daoyuan Chen
1 and
Yi Yang
1
1
School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
2
School of Engineering, Science, Technology, Central Connecticut State University, New Britain, CT 06050, USA
3
School of Urban Design, Wuhan University, Wuhan 430072, China
*
Authors to whom correspondence should be addressed.
World Electr. Veh. J. 2025, 16(8), 445; https://doi.org/10.3390/wevj16080445 (registering DOI)
Submission received: 24 June 2025 / Revised: 31 July 2025 / Accepted: 4 August 2025 / Published: 6 August 2025
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
Versions Notes

Abstract

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities.
Keywords: electric vehicle charging station (EVCS); site selection; multi-layer perceptron (MLP); spatial loss function; Shapley additive explanation (SHAP); urban planning electric vehicle charging station (EVCS); site selection; multi-layer perceptron (MLP); spatial loss function; Shapley additive explanation (SHAP); urban planning

Share and Cite

MDPI and ACS Style

Huang, Y.; Ren, H.; Jia, X.; Yu, X.; Xie, D.; Zou, Y.; Chen, D.; Yang, Y. A Spatially Aware Machine Learning Method for Locating Electric Vehicle Charging Stations. World Electr. Veh. J. 2025, 16, 445. https://doi.org/10.3390/wevj16080445

AMA Style

Huang Y, Ren H, Jia X, Yu X, Xie D, Zou Y, Chen D, Yang Y. A Spatially Aware Machine Learning Method for Locating Electric Vehicle Charging Stations. World Electric Vehicle Journal. 2025; 16(8):445. https://doi.org/10.3390/wevj16080445

Chicago/Turabian Style

Huang, Yanyan, Hangyi Ren, Xudong Jia, Xianyu Yu, Dong Xie, You Zou, Daoyuan Chen, and Yi Yang. 2025. "A Spatially Aware Machine Learning Method for Locating Electric Vehicle Charging Stations" World Electric Vehicle Journal 16, no. 8: 445. https://doi.org/10.3390/wevj16080445

APA Style

Huang, Y., Ren, H., Jia, X., Yu, X., Xie, D., Zou, Y., Chen, D., & Yang, Y. (2025). A Spatially Aware Machine Learning Method for Locating Electric Vehicle Charging Stations. World Electric Vehicle Journal, 16(8), 445. https://doi.org/10.3390/wevj16080445

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