A novel framework for vehicle charging pattern recognition and charging duration prediction based on EA-CAE and K-means clustering

Abstract

Accurate prediction of electric vehicle (EV) charging duration is critical for improving user satisfaction and enabling efficient real-time charging management. This paper proposes a dynamic charging duration prediction framework for EVs, composed of four coordinated modules: data preprocessing, charging pattern classification, static prediction, and dynamic bias correction. First, raw charging data collected from the Battery Management System (BMS) is cleaned and normalized to address missing and abnormal values. An enhanced convolutional autoencoder (EV-CAE) is then employed to extract multi-scale temporal features, while K-Means clustering is used to identify representative charging behavior patterns. Based on the classified patterns, the static prediction module estimates the current charging duration by leveraging historical data and pattern labels. To enhance adaptability under dynamic conditions, a bias correction mechanism is designed, integrating linear, logarithmic, proportional, and deep learning-based strategies to adjust the prediction results in real time. Experimental results on real-world EV datasets demonstrate that the proposed framework significantly improves prediction accuracy. In particular, the dynamic correction module increases the coefficient of determination (R²) from 0.948 to 0.960, while maintaining robust performance under fluctuating charging behavior and low-temperature conditions. These results validate the practical applicability and engineering potential of the proposed method for real-time charging duration estimation in intelligent EV charging systems.