Optimal Model of Electric Bus Scheduling Based on Energy Consumption and Battery Loss

Yan Xing, Quanbo Fu (), Yachao Li, Hanshuo Chu and Enyi Niu
Additional contact information
Yan Xing: School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Quanbo Fu: School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Yachao Li: School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Hanshuo Chu: School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Enyi Niu: School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China

Sustainability, 2023, vol. 15, issue 12, 1-17

Abstract: The characteristics of electric buses make it difficult to estimate the energy consumption and mean that they are prone to battery loss; as such, fuel bus scheduling methods are no longer fully applicable. In current studies, the influence of these factors is ignored. This paper proposes an electric bus scheduling optimization model based on energy consumption and battery loss. Firstly, the LSTM (long short-term memory) is used to estimate trip energy consumption. Subsequently, these results are combined with the optimization objectives of minimizing the fleet size and battery loss amount. Limitations on the buses’ number, travel time, battery safety thresholds, remaining charge, and total charge are also considered. By controlling the different battery charge and discharge thresholds to minimize battery losses, the goal of sustainability is achieved. NSGA-II (non-dominated sorting genetic algorithm-II) is used to solve the model. The corresponding scheduling and charging scheme are determined. Electric bus route A is taken to validate the predictions. The results show that the annual fleet battery loss value decreases as the fleet size increases. The company has the lowest annual operating cost when the battery charge and discharge thresholds are set to [25%, 85%]. Optimizing the scheduling and charging scheme for electric bus can effectively reduce the operating cost.

Keywords: electric bus (EB); scheduling optimization; battery loss; energy consumption estimation; NSGA-II (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/12/9640/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/12/9640/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:12:p:9640-:d:1172188

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().