Optimal Scheduling of Electric Bus Fleets Using PSO-RNN for Enhanced Battery Life and Efficiency
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Abstract
This paper presents an ideal strategy for electric bus fleets (EBFs) using a Particle Swarm Optimization-Recurrent Neural Network (PSO-RNN) approach, by a focus on enhancing battery life by considering battery capacity fade. The proposed method addresses the nonlinear nature of battery degradation and formulates the EBF scheduling problem as a multi-stage decision process. The PSO-RNN model is utilized to determine the optimal scheduling strategy that reduces the sum of battery alternateson the running life of the electrical vehicle (Buses). This study is conducted using an urban public transit system as a case study, with scenarios considering five and seven different working loads. Results demonstrate that the optimal scheduling strategy significantly reduces battery capacity loss and the sum of alternates, leading to lower operational costs and extended battery life. The efficacy of the intended method is validated by comparing the battery capacity fading process and replacement frequency under both scheduled and unscheduled scenarios.
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