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September 19, 2025IET Intelligent Transport SystemsOpen Access

Understanding User Behaviour and Predicting Charging Costs: A Machine Learning Approach to Support Electric Vehicle Adoption Decisions

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Authors

MCMuhammed CavusHAHuseyin AyanMBMC Bell

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Overview

Analysis reveals charging behaviour patterns and costs in electric vehicle users, suggesting strategies for infrastructure development.

Key Points

  • The gradient boosting model accurately predicts EV charging costs, achieving a mean squared error of 0.041.
  • User behaviour analysis shows peak charging times are from 6:00 PM to 9:00 PM, primarily on weekdays.
  • Electric vehicle users prefer charging stations within a 10-mile radius, emphasizing the need for strategic infrastructure planning.
  • Integrating predictive modelling with user behaviour enhances satisfaction and supports efficient EV infrastructure deployment.

Cite This Study

Cavus et al. (2025) studied this question.

synapsesocial.com/papers/68d464ff31b076d99fa6497ehttps://doi.org/10.1049/itr2.70088
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Analysis and Predictive Modelling of EV Charging Patterns and User Behaviour2026
  2. 2An integrated machine learning framework for EV charging management2026
  3. 3Insights into Household Electric Vehicle Charging Behavior: Analysis and Predictive Modeling2024 · 3 citations
  4. 4Optimizing Electric Vehicle Charging Costs Using Machine Learning2024 · 1 citations
  5. 5Electric Vehicle User Behavior Forecasting via Data-Driven Techniques2026