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Dec 14, 2023
Recognition of Low-Energy Consumption Driving Behavior of Electric Bus Based on Machine Learning
Authors: Tianhao Liu [email protected], Qian Han [email protected], Tiezhu Li [email protected], Haibo Chen [email protected], Ying Li [email protected], and Jian Sun [email protected]Author Affiliations
Publication: CICTP 2023
ABSTRACT
Researching the identification method of low-energy consumption driving behaviors of electric buses will help to establish energy-saving driving strategies, guide actual driving behaviors, and help bus companies reduce energy consumption costs. In this paper, the K-means algorithm is used to cluster energy consumption per unit mileage, and three types of data of low, medium, and high energy consumption are obtained. The identification model of low-energy-consumption driving behavior in four situations is established, and SHAP algorithm is used to explain the identification model. The influence mechanism of driving behavior characteristics on energy consumption is analyzed, and the low-energy-consumption driving behavior characteristics of four situations are determined. The research results show that the driving behaviors that affect low energy consumption in the two situations of road sections and departuring from a node are mainly the average accelerator pedal opening and average speed.
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Published online: Dec 14, 2023
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ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Buses
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Deformation (mechanics)
- Driver behavior
- Electric power
- Energy consumption
- Energy efficiency
- Energy engineering
- Energy methods
- Engineering fundamentals
- Engineering mechanics
- Highway transportation
- Infrastructure
- Mathematics
- Solid mechanics
- Structural mechanics
- Traffic engineering
- Transportation engineering
- Vehicles
Authors
Affiliations
1School of Transportation, Southeast Univ., Nanjing, China. Email: [email protected]
2School of Transportation, Southeast Univ., Nanjing, China. Email: [email protected]
3School of Transportation, Southeast Univ., Nanjing, China. Email: [email protected]
4Institute for Transport Studies, Univ. of Leeds, Leeds, UK. Email: [email protected]
5Dynnoteq Limited, International House, London, UK. Email: [email protected]
6Nanjing Golden Dragon Bus Co., Ltd., Nanjing, China. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.