Electric vehicle car-sharing optimization relocation model combining user relocation and staff relocation

ABSTRACT

Factors including unbalanced user travel demand, vehicle charging status, and high operating cost of enterprises have restricted the development of electric vehicle car-sharing. This paper comprehensively considers the impact of multiple dynamic constraints such as user demand, state of charge of electric vehicles, and operating profit on vehicle relocation. The travel behavior of consumers is studied through Multinomial Logistic Regression method. User demand for electric vehicle car-sharing is forecast through Hidden Markov Model. A new vehicle relocation strategy combining staff relocation and user relocation is formulated. With the goal of maximizing enterprise profit, an electric vehicle car-sharing optimization relocation model in region level is finally established. Taking Anting Town as an example to verify the model, the results show that this new vehicle relocation strategy can effectively reduce the operating cost of enterprises, improve the circulation rate and utilization rate of vehicles, and reduce unnecessary waste of resources.

KEYWORDS:

• Electric vehicle car-sharing
• relocation optimization
• staff relocation
• user relocation
• integer linear programming

Acknowledgments

This research is supported by the project of Vehicle Scheduling Optimization Model for Electric Vehicle Sharing (kx0170020172681) funded by Fundamental Research Funds for the Central Universities.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Central University Basic Scientific Research Business Expenses Special Funds (CN) [kx0170020172681].