Driving-Pattern-Based Ensemble Clustering for SOC Prediction in Battery-Swapping Electric Two-Wheeled Vehicles

The growing interest in electric vehicles, driven by the climate crisis, has underscored the potential of battery-swapping electric two-wheeled vehicles (BSE2WVs) in overcoming the limitations of conventional rechargeable systems, expanding the global battery-as-a-service (BaaS) market. Accurate state-of-charge (SoC) prediction is essential for BaaS providers to efficiently manage battery swapping. However, most existing studies focus on four-wheeled vehicles, overlooking the unique driving patterns and dynamics of two-wheelers. Moreover, despite the adoption of machine-learning-based approaches, the cold start problem remains a challenge due to data scarcity. This study analyzes the driving patterns of BSE2WVs and proposes an SoC prediction framework tailored to these patterns. Ensemble clustering techniques identify driving patterns while considering risks and illegal behaviors, incorporating lateral movement characteristics. Training models on driver-specific patterns enhance prediction accuracy, while a data interpolation method extracts representative SoC consumption trends to mitigate data scarcity. Additionally, a skip connection method utilizing initial window data captures battery consumption variations based on the current SoC. Experiments using real-world BSE2WV data from a BaaS provider in Seoul, South Korea, demonstrate that the proposed framework outperforms existing models, achieving high prediction accuracy even under limited data conditions, with an average improvement of 29.25% through reduced MSE.