Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)

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High-Precision Light Trucks Fuel Consumption Prediction using XGBoost- Improved Arithmetic Optimization Algorithm-DeepESN

Authors
Yishuai Li1, Wei Xu2, Chengliang Liao2, Kuan Kei Hoi3, *, Liaodong Nie3, Ning Wang3, Jingming Wu2, Yiming Hu1, Dequan Zeng1
1Nanchang Automotive Institute of Intelligence & New Energy, Nanchang, 330000, China
2Jiangxi Isuzu Motors Co., Ltd., Nanchang, 330000, China
3School of Automotive Studies, Tongji University, Shanghai, 201800, China
*Corresponding author. Email: 2230015@tongji.edu.cn
Corresponding Author
Kuan Kei Hoi
Available Online 28 September 2024.
DOI
10.2991/978-94-6463-514-0_83How to use a DOI?
Keywords
Vehicle Fuel Consumption Prediction; Light Trucks; DeepESN; IPOA
Abstract

With the increasing demand for light-duty diesel trucks in urban and surrounding areas due to the growth in China’s road freight and urban distribution, the issues of fuel consumption and environmental emissions have become more severe. This paper proposes a fuel consumption prediction optimization model that combines XGBoost for selecting key fuel consumption features and the DeepESN algorithm for prediction. By incorporating Tent Chaotic Mapping, Nonlinear Weight Factor, Cauchy Mutation Strategy, and Sparrow Alarming Strategy, the Improved Arithmetic Optimization Algorithm (IPOA) is employed to optimize the hyperparameters of DeepESN. The model is validated using T-BOX data from 150 light trucks, and the results indicate that the XGBoost-IPOA-DeepESN model outperforms other comparative models in terms of prediction accuracy, providing a reference for implementing efficient energy use strategies.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
Series
Advances in Engineering Research
Publication Date
28 September 2024
ISBN
978-94-6463-514-0
ISSN
2352-5401
DOI
10.2991/978-94-6463-514-0_83How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yishuai Li
AU  - Wei Xu
AU  - Chengliang Liao
AU  - Kuan Kei Hoi
AU  - Liaodong Nie
AU  - Ning Wang
AU  - Jingming Wu
AU  - Yiming Hu
AU  - Dequan Zeng
PY  - 2024
DA  - 2024/09/28
TI  - High-Precision Light Trucks Fuel Consumption Prediction using XGBoost- Improved Arithmetic Optimization Algorithm-DeepESN
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 860
EP  - 873
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_83
DO  - 10.2991/978-94-6463-514-0_83
ID  - Li2024
ER  -