Graph-based two-level clustering for electric vehicle usage patterns

Abstract

Electric vehicles (EVs) continue to gain popularity over internal combustion vehicles, but driving and charging an EV is a fundamentally different experience. EV usage patterns include features such as charging speed, battery state of charge, and battery depth of discharge. Understanding EV usage patterns from data is essential to optimizing the performance and management of EVs. However, extracting useful conclusions from individual EV data is computationally intensive. Traditional clustering methods offer a solution by grouping vehicles by behavior pattern, but they still pose computational challenges for long time-series data profiles. To efficiently extract EV behavior insights, we propose a scalable two-level clustering approach and test it on a dataset of 3,082 real EVs over the course of a year. We first use level one clustering to weekly data segments, revealing distinct patterns in state of charge utilization. Next, we apply level two graph-based clustering to group individual vehicles that operate similarly on a longer timescale. Our scalable and adaptable clustering approach can aid in battery lifecycle management, charge demand forecasting, and fleet energy management, all critical tasks to facilitate the continued growth of the EV market.