What question did this study set out to answer?

To forecast electric vehicle charging behavior by analyzing price sensitivity, time preferences, and weekend habits.

June 11, 2026World Electric Vehicle JournalOpen Access

Electric Vehicle User Behavior Forecasting via Data-Driven Techniques

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Authors

YXYonghua XuHunan University of Technology XTXiangyi TangHunan University of Technology WLWei LiuHunan University of Technology

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Overview

Randomized trial assesses EV charging behavior to improve demand prediction and service management, suggesting new strategies for intelligent operation.

Key Points

To forecast electric vehicle charging behavior by analyzing price sensitivity, time preferences, and weekend habits.
Proposes a three-variable charging response framework considering price, time-of-day, and weekend preferences.
Estimates four behavioral parameters using nonlinear least squares from real charging-order data.
Applies K-means clustering to classify users into five groups based on charging behaviors.
Reduces test RMSE from 11.5 kWh to 8.3 kWh (27.8% improvement).
Decreases test MAPE from 25.3% to 18.7% (26.1% improvement).
Improves test R2 from 0.70 to 0.80.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a2a505d80c8f91e7f39cf5a https://doi.org/10.3390/wevj17060304

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June 11, 2026World Electric Vehicle JournalOpen Access

Electric Vehicle User Behavior Forecasting via Data-Driven Techniques

View Full Paper

Ask AI

Bookmark

Authors

YXYonghua XuHunan University of Technology XTXiangyi TangHunan University of Technology WLWei LiuHunan University of Technology

Discussion

Member takes

Overview

Randomized trial assesses EV charging behavior to improve demand prediction and service management, suggesting new strategies for intelligent operation.

Key Points

To forecast electric vehicle charging behavior by analyzing price sensitivity, time preferences, and weekend habits.
Proposes a three-variable charging response framework considering price, time-of-day, and weekend preferences.
Estimates four behavioral parameters using nonlinear least squares from real charging-order data.
Applies K-means clustering to classify users into five groups based on charging behaviors.
Reduces test RMSE from 11.5 kWh to 8.3 kWh (27.8% improvement).
Decreases test MAPE from 25.3% to 18.7% (26.1% improvement).
Improves test R2 from 0.70 to 0.80.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a2a505d80c8f91e7f39cf5a https://doi.org/10.3390/wevj17060304

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