Survival analysis of electric vehicle charging behavior and the temporal evolution of feature effects

Meža, Matej (author), Strle, Gregor (author), Meža, Marko (author)

This study proposes a survival-based modeling framework that combines behavioral features with interpretable machine learning to understand and predict user churn in electric vehicle charging services. Using a dataset of 1,074 users and 107,531 charging sessions from Central European countries, we modeled time-to-churn while handling censored observations.The best-performing model, a StackedWeibull survival model based on gradient boosting, achieved a concordance index of 0.826 ± 0.041 and Integrated Brier Score of 0.078 ± 0.008 (5-fold cross-validation), with strong calibration relative to Kaplan-Meier survival estimates. Interpretability analyses identified sustained session frequency, positive engagement trends, and temporal regularity in charging behavior as key predictors of reduced churn risk.These findings highlight the potential of survival modeling integrated with behavioral analytics to predict churn risk and inform retention strategies in electric vehicle charging networks.

• Language: English
• Keywords: electric vehicle charging, machine learning, behavioral modeling, churn prediction, survival analysis
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 15 str.
• Numbering: 15, art. 34897
• PID: 20.500.12556/RUL-174885
• UDC: 621.3
• ISSN on article: 2045-2322
• DOI: 10.1038/s41598-025-18771-8
• COBISS.SI-ID: 252693507
• Publication date in RUL: 10.10.2025

Record is a part of a journal

• Title: Scientific reports
• Shortened title: Sci. rep.
• Publisher: Nature Publishing Group
• ISSN: 2045-2322
• COBISS.SI-ID: 18727432

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: polnjenje električnih vozil, strojno učenje, modeliranje obnašanja, napoved odhoda uporabnikov, analiza trajanja

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0246
• Name: ICT4QoL - Informacijsko komunikacijske tehnologije za kakovostno življenje