Driving behavior analysis based on the operation data of new energy vehicle

Ling Zhong; Yuhang Wang; Yi Cao

doi:10.1117/12.2681191

23 May 2023 Driving behavior analysis based on the operation data of new energy vehicle

Ling Zhong, Yuhang Wang, Yi Cao

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126455A (2023) https://doi.org/10.1117/12.2681191
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

The purpose of this thesis is to cluster the driving behaviors by analyzing the differences in energy consumption of different drivers when driving new energy vehicles. As a result, the discrimination of excellent energy-efficient driving style or poor driving style can be achieved. Firstly, by studying a large number of parameters of the collected new energy vehicles, the original data set is calibrated for energy consumption type and correlation analysis is performed in order to extract driving parameters with high correlation. Subsequently, the extracted multiple valuable driving parameters were subjected to attribute simplification using dimensionality reduction methods based on principal component analysis and T-SNE, and the dimensionality reduction results obtained by each method were clustered using a K-means model to form an excellent driving classification model labeled by energy consumption, in order to deeply analyze the differences in different driving behavior characteristics and energy consumption differences.

Citation Download Citation

Ling Zhong, Yuhang Wang, and Yi Cao "Driving behavior analysis based on the operation data of new energy vehicle", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126455A (23 May 2023); https://doi.org/10.1117/12.2681191

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