Optimizing Electric Vehicle Charging Infrastructure through Machine Learning - A Study of Charging Patterns and Energy Consumption

Aug 5, 2026

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Rapid EV adoption requires efficient charging infrastructure, yet inconsistent demand and suboptimal station placement still limit progress toward sustainable urban transportation. This study proposes a machine-learning approach to optimize the location of electric vehicle charging stations using an energy-consumption prediction model. The dataset comprises 148,136 charging transactions from Boulder, Colorado. KNeighborsRegressor and RandomForestRegressor are applied for the placement task. Results show notable demand fluctuations during peak commute hours, with KNeighborsRegressor providing stronger predictive accuracy to support planning and allocation decisions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

Motivation and challenges

Idle time and infrastructure management

Related work

Machine learning for charging demand and patterns

Smart city and grid-aware charging networks

Proposed approach

Energy-consumption prediction for EVCS placement

Algorithms and modeling

Data and experimental setup

Dataset and study area

Results and analysis

Peak-hour demand fluctuations

Model performance comparison

Conclusion

Infrastructure planning implications

JIM International Journal of Interactive Mobile Technologies [Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org) iJIM | eISSN: 1865-7923 | Vol. 18 No. 21 (2024) | [https://doi.org/10.3991/ijim.v18i21.50843](https://doi.org/10.3991/ijim.v18i21.50843) PAPER Optimizing Electric Vehicle Charging Infrastructure through Machine Learning: A Study of Charging Patterns and Energy Consumption Ayoub Alsarhan1, Athari Alnatsheh1, Mohammad Aljaidi2(􀀍), Tuqa ALMakkawi1, Mahmoud Aljamal1, Tamam Alsarhan3 1Department of Information Technology, Faculty of Prince Al-Hussien bin Abdullah for IT The Hashemite University, Zarqa, Jordan 2Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, Jordan 3King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan [mjaidi@zu.edu.jo](mjaidi@zu.edu.jo) ABSTRACT The rapid adoption of electric vehicles (EVs) has created a pressing need for efficient charging infrastructure. but challenges such as inconsistent demand and poor placement remain. An effective distribution of sufficient EV charging stations (CSs) is one of the major obstacles preventing the market penetration of EVs and the realization of a sustainable transportation system in urban areas. In this paper, anew machine learning technique is proposed in order to optimize the placement of EV charging stations (EVCSs) in metropolitan areas based on an energy consumption prediction model. A dataset from 148,136 charging transactions in Boulder, Colorado, is used with the proposed model. Key algorithms such as KNeighborsRegressor and RandomForestRegressor were incorporated to solve the placement problem. The analysis revealed significant demand fluctuations during peak commute hours, with the KNeighborsRegressor model demonstrating superior prediction accuracy. These insights can guide more effective infrastructure planning and resource allocation, ultimately enhancing the efficiency and user experience of EV charging networks and promoting sustainable urban transportation. KEYWORDS electric vehicle (EV), charging station location analysis, machine learning (ML) 1 INTRODUCTION The rapid adoption of electric vehicles (EVs) necessitates a complementary expansion of charging infrastructure, particularly in major markets such as China, the EU, and the USA. Regional and city-level public policies are crucial for managing EV adoption and charging infrastructure. One significant challenge is the increasing idle time when an EV is connected but not charging. This issue impacts infrastructure size, costs, and availability. Accurate estimation and management of idle time through machine learning (ML) can provide valuable insights for EV users, Alsarhan, A., Alnatsheh, A., Aljaidi, M., Makkawi, T.A., Aljamal, M., Alsarhan, T. (2024) . Optimizing Electric Vehicle Charging Infrastructure through Machine Learning: A Study of Charging Patterns and Energy Consumption. International Journal of Interactive Mobile Technologies (iJIM), 18(21), pp. 149–170. [https://doi.org/10.3991/ijim.v18i21.50843](https://doi.org/10.3991/ijim.v18i21.50843) Article submitted 2024-07-02. Revision uploaded 2024-08-20. Final acceptance 2024-08-21. © 2024 by the authors of this article. Published under CC-BY. iJIM | Vol. 18 No. 21 (2024) International Journal of Interactive Mobile Technologies (iJIM) 149 Alsarhan et al. policymakers, and network owners, facilitating better infrastructure management and promoting EV adoption [1–4] . Electric vehicles are becoming integral to modern transportation, offering reduced greenhouse gas emissions and decreased reliance on fossil fuels. However, the success of EVs heavily depends on the availability and efficiency of charging infrastructure. Researchers have explored various ML approaches to understand EV charging behavior, predict charging demand, and optimize the deployment

FAQs

What problem does the paper address about EV charging infrastructure?

It targets inefficient EV charging infrastructure planning caused by inconsistent demand and poor charging-station placement, which slows EV market penetration in urban areas.

How does the proposed method optimize charging station placement?

It uses a machine-learning energy-consumption prediction model combined with placement optimization, trained on historical charging transaction data.

Which algorithms are used and which performs best?

KNeighborsRegressor and RandomForestRegressor are employed; KNeighborsRegressor shows superior prediction accuracy, especially when demand fluctuations occur during peak commute hours.

Aug 5, 2026

Upload by Levi