Data-driven battery-lifetime-aware scheduling for electric bus fleets

Electric vehicles (EVs) have experienced a sensational growth in the past few years, due to the potential of mitigating global warming and energy scarcity problems. However, the high manufacturing cost of battery packs and limited battery lifetime hinder EVs from further development. Especially, electric bus, as one of the most important means of public transportation, suffers from long daily operation time and peak-hour passenger overload, which aggravate its battery degradation. To address this issue, we propose a novel data-driven battery-lifetime-aware electric bus scheduling system. Leveraging practical bus GPS and transaction datasets, we conduct a detailed analysis of passenger behaviors and design a reliable prediction model for passenger arrival rate at each station. By taking passenger waiting queue at each bus station analogous to data buffer in network systems, we apply Lyapunov optimization and obtain an electric bus scheduling strategy with reliable performance guarantee on both battery degradation rate and passengers' service quality. To verify the effectiveness of the system, we evaluate our design on a 12-month electric bus operation datasets from the city of Shenzhen. The experimental results show that, compared with two baseline methods, our system reduces the battery degradation rate by 14.3% and 21.7% under the same passenger arrival rate, while preserving good passenger service quality.