Development of Machine Learning Based State-of-Charge Prediction Model for Plug-in Electric Vehicle's Relocation in Sharing System

This paper presents a Plug-in electric vehicle (PEV) relocation strategy in a sharing system considering machine learning based state-of-charge (SOC) prediction model. The proposed work involves the machine learning approaches like the Adaboost algorithm and XGBoost algorithm for PEVs SOC prediction in relocation problems for the first time. Also, the low-capacity batteries of PEVs are considered as it is affordable by the common people with less charging time. It is also believed that the large number of low capacities PEVs based taxi owners in metropolitan cities will be attracted towards relocation-based sharing system considering machine learning based SOC prediction model. The customer request for sharing PEVs will be reflected in the management system at the beginning. Assuming all the PEVs with a certain level of SOC during pre-relocation are connected to the management system. The customer request for PEVs service will be assigned to the nearest PEVs depending on the predicted value of SOC by machine learning approaches. The machine learning-based predicted value of PEVs SOC will help the management system to identify the nearest PEVs for the customer sharing system. The present work steps are listed here. Firstly, the PEVs with higher predicted SOC value by machine learning have been shared with the customer from the nearest location considering descending order of their SOC. Further, the rest of the PEVs will be assigned to the nearest charging station by the PEV sharing management system. Secondly, the customer request is assigned from the station to relocate PEVs with higher SOC values in descending order. Different case studies are assumed including one practical scenario of Mumbai, India. The optimization problem of multi-depot vehicle routing problem (MDVRP) with the minimization of relocation cost has been solved by IBM CPLEX optimizer in this work. Finally, the proposed work efficacy has been demonstrated with some validated results including less relocation costs for all the case studies under consideration with lower capacity based PEVs.