Decision Making for Energy Acquisition of Electric Vehicle Taxi with Profit Maximization

Li Cui (), Yanping Wang, Hongquan Qu, Yiqiang Li, Mingshen Wang and Qingyuan Wang
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Li Cui: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Yanping Wang: School of Artificial Intelligence and Computer Science, North China University of Technology, Beijing 100144, China
Hongquan Qu: School of Artificial Intelligence and Computer Science, North China University of Technology, Beijing 100144, China
Yiqiang Li: Center of Engineering Training, Nanjing Institute of Technology, Nanjing 211167, China
Mingshen Wang: Center of Engineering Training, Nanjing Institute of Technology, Nanjing 211167, China
Qingyuan Wang: Center of Engineering Training, Nanjing Institute of Technology, Nanjing 211167, China

Sustainability, 2025, vol. 17, issue 11, 1-24

Abstract: With the emergence of joint business operations involving electric vehicle taxis (EVTs) and charging/swapping stations (CSSTs), a unified decision-making method has become essential for an EVT to select both the driving path and the energy acquisition mode (EAM). The decision making is influenced by energy acquisition cost and potential operation profit. The energy acquisition cost is closely related to the driving time required to reach a CSST, and existing prediction methods for driving time ignore the spatial–temporal interactions of traffic flows on different roads and fail to account for traffic congestion differences across various sections of a road. Existing estimation methods for potential operation income ignore the distributions of taxi orders in different areas. To address these issues, a traffic flow prediction model is first proposed based on the long short-term memory–generative adversarial network (LSTM-GAN) deep learning algorithm. A refined driving time model is developed by segmenting a road into different sections. Then, an expected operation income model is developed considering the distributions of origins and destinations of taxi orders in different areas. Then, a decision-making method for path planning and the charging/swapping mode is proposed, aiming to maximize the total profit of EVTs. Finally, the effectiveness of the proposed decision-making method for EVTs is validated with a city’s traffic network.

Keywords: charging/swapping station (CSST); decision making; energy acquisition; electric vehicle taxi; path planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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