Evaluates charging demand levels using connected car data to improve infrastructure planning, indicating significant implications for urban mobility.
The rapid adoption of electric vehicles (EVs) increases demand for charging infrastructure, challenging traditional planning methods that rely on static spatial data and overlook dynamic factors. We propose a vehicle-first approach using real-time connected car data (battery status and residual autonomy), live traffic, and charger availability to classify demand into low, medium, high, and critical levels. Leveraging H3 indexing, our integrated method enhances demand forecasting, alleviates congestion, and supports proactive interventions for vehicles with critical demand.
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Mishra et al. (2026) studied this question.
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