What question did this study set out to answer?

This research aims to enhance the planning of electric vehicle charging infrastructure by integrating behavioural and predictive modelling techniques.

May 9, 2026Scientific Reports0 citationsOpen Access

Sustainable EV adoption with clustering and predictive modelling for optimal charging infrastructure in the West Midlands and North East UK

MCMuhammed CavusDurham University SWS. WangNewcastle University SDSanchari DebNewcastle University

Key Points

This research aims to enhance the planning of electric vehicle charging infrastructure by integrating behavioural and predictive modelling techniques.
Analyzed structured survey dataset from 256 EV users across North East (n=124) and West Midlands (n=132).
Utilized K-Means clustering to identify distinct user groups and developed predictive models including Random Forest and compact deep learning.
Achieved high model accuracy using an 80:20 train-test split with cross-validation.
North East predictive model attained R² = 0.9951, MSE = 0.0694, indicating a very strong fit.
West Midlands model achieved R² = 0.9548, MSE = 0.6410, showing strong predictive performance.
85% of users prefer DC fast chargers, with a majority willing to travel up to 3 km for charging.

Abstract

The rapid growth of electric vehicle (EV) adoption presents significant challenges for charging infrastructure planning and grid integration, particularly at the regional level. However, existing studies often apply machine learning techniques in isolation and lack integrated, region-specific behavioural modelling. This study introduces the Intelligent Sustainable EV Clustering and Analysis Platform (ISE-CAP), an integrated and interpretable analytical framework that advances beyond conventional ML-based EV studies by combining behavioural clustering, predictive modelling, explainable artificial intelligence (XAI), and adaptive optimisation within a regionally comparative decision-support architecture. A structured survey dataset comprising 256 EV users from the North East (n = 124) and West Midlands (n = 132) was analysed to examine charging behaviour, adoption motivations, and infrastructure preferences. K-Means clustering identified three distinct EV user groups in each region. Predictive models, including Random Forest, CatBoost, and a compact deep learning architecture, were trained using an 80: 20 train-test split with cross-validation achieved high accuracy on the available regional dataset, with the North East model attaining R^2 = 0. 9951 and MSE = 0. 0694, indicating a very strong fit to the observed data. On the held-out regional test dataset, the West Midlands model achieved R^2 = 0. 9548 and MSE = 0. 6410, indicating strong predictive performance within the analysed sample. Charging behaviour analysis indicates that 85% of users prefer DC fast chargers, with most users (66% in the West Midlands and 53% in the North East) willing to travel up to 3 km for charging. Cost savings accounted for 65% of EV purchases in the North East, while environmental concerns accounted for 30% in the West Midlands, based on regional frequency distributions. SHapley Additive exPlanations (SHAP) analysis identified charging duration, real-time station availability, and cost as the most influential factors associated with charger selection preferences. The findings highlight regional heterogeneity in charging behaviour and infrastructure needs, emphasising the importance of adaptive and interpretable modelling approaches. While results are specific to the analysed regions, the ISE-CAP framework provides a scalable decision-support architecture for sustainable EV infrastructure optimisation in smart city contexts.

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