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Optimized Data-Driven Model for Predicting Commercial Vehicle Energy Consumption
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Shuaiyu LI1, Guodong SHI1, Mingmao HU1, 2, Aihong GONG1, Qingshan GONG1, Jian FANG3, Hao TAN3
Chinese Journal of Automotive Engineering | 2025, 15(2) : 164 - 176
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Chinese Journal of Automotive Engineering | 2025, 15(2): 164-176
• Green and Low-Carbon Technologies Section •
Optimized Data-Driven Model for Predicting Commercial Vehicle Energy Consumption
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Shuaiyu LI1, Guodong SHI1, Mingmao HU1, 2, Aihong GONG1, Qingshan GONG1, Jian FANG3, Hao TAN3
Affiliations
  • 1 School of Mechanical Engineering，Hubei University of Automotive Technology，Shiyan 442002，Hubei，China
  • 2 Hubei Key Laboratory of Automotive Power Transmission and Electronic Control，Shiyan 442002，Hubei，China
  • 3 Dongfeng Commercial Vehicle Co.，Ltd.，Shiyan 442002，Hubei，China
Published: 2025-03-20 doi: 10.3969/j.issn.2095‒1469.2025.02.05
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Taking a domestic fuel commercial vehicle as an example, an energy consumption optimization prediction model suitable for commercial vehicles was constructed using the Internet of Vehicles big data platform and a neural network model. Firstly, the historical vehicle operation data was preprocessed to analyze the correlation between different vehicle operation characteristic data. Secondly, an adaptive weight attention mechanism was introduced based on Bi-directional Long Short-Term Memory (BiLSTM) and the characteristics of vehicle data. The Improved Whale Optimization Algorithm (IWOA) was used to optimize the network hyperparameters of the model, leading to the construction of the IWOA-BilSTM-Attention commercial vehicle energy consumption optimization prediction model. Finally, the prediction performance of multiple models under different driving conditions were compared and analyzed. The results show that under actual driving conditions, the root mean square error and the mean absolute error of the optimized model are reduced by approximately 26.73% and 20.0%, respectively, compared with the original model. This verifies the feasibility of the optimized model for predicting the energy consumption of commercial vehicles.

commercial vehicle energy consumption prediction  /  time series  /  neural network  /  improved whale optimization algorithm
Shuaiyu LI, Guodong SHI, Mingmao HU, Aihong GONG, Qingshan GONG, Jian FANG, Hao TAN. Optimized Data-Driven Model for Predicting Commercial Vehicle Energy Consumption[J]. Chinese Journal of Automotive Engineering, 2025 , 15 (2) : 164 -176 . DOI: 10.3969/j.issn.2095‒1469.2025.02.05
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Article Info
doi: 10.3969/j.issn.2095‒1469.2025.02.05
  • Receive Date：2024-03-19
  • Online Date：2025-07-20
  • Published：2025-03-20
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History
  • Received：2024-03-19
  • Revised：2024-05-14
Funding
Affiliations
    1 School of Mechanical Engineering，Hubei University of Automotive Technology，Shiyan 442002，Hubei，China
    2 Hubei Key Laboratory of Automotive Power Transmission and Electronic Control，Shiyan 442002，Hubei，China
    3 Dongfeng Commercial Vehicle Co.，Ltd.，Shiyan 442002，Hubei，China
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https://castjournals.cast.org.cn/joweb/qcgcxb/EN/10.3969/j.issn.2095‒1469.2025.02.05
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