Analysis and prediction of energy consumption of electric bus based on driving conditions

Chengfei Wu; Jiyao Shi; Kui Wang

doi:10.1117/12.2660007

3 February 2023 Analysis and prediction of energy consumption of electric bus based on driving conditions

Chengfei Wu, Jiyao Shi, Kui Wang

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111R (2023) https://doi.org/10.1117/12.2660007
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

For the realization of the "dual carbon target", new energy vehicles represented by electric vehicles will replace fuel vehicles to become a foregone conclusion. The reform of electric vehicles often starts with public transportation. Accurately predicting the energy consumption of pure electric buses will help the bus itinerary planning. This article takes 15 pure electric buses for two years of driving data as an example, which will be divided into two -level driving fragments. Extract and study driving behavior, vehicle factors and road traffic conditions. Train machine learning models with key factors extracted and use the model for actual energy consumption prediction. The actual stroke test results indicate that the average root error is 0.19 lower than the traditional multi -linear model, and the average absolute error is 16.7% lower than the traditional multi-linear model.

Citation Download Citation

Chengfei Wu, Jiyao Shi, and Kui Wang "Analysis and prediction of energy consumption of electric bus based on driving conditions", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111R (3 February 2023); https://doi.org/10.1117/12.2660007

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KEYWORDS

Machine learning

Roads

Atmospheric modeling

Carbon

Error analysis

Performance modeling

Data modeling

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