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Encoder–Decoder-Based Velocity Prediction Modelling for Passenger Vehicles Coupled with Driving Pattern Recognition

Author

Listed:
  • Diming Lou

    (College of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Yinghua Zhao

    (College of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Liang Fang

    (College of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Yuanzhi Tang

    (College of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Caihua Zhuang

    (Propulsion Control and Software Engineering Department, SAIC MOTOR, Shanghai 201804, China)

Abstract

To improve the performance of predictive energy management strategies for hybrid passenger vehicles, this paper proposes an Encoder–Decoder (ED)-based velocity prediction modelling system coupled with driving pattern recognition. Firstly, the driving pattern recognition (DPR) model is established by a K-means clustering algorithm and validated on test data; the driving patterns can be identified as urban, suburban, and highway. Then, by introducing the encoder–decoder structure, a DPR-ED model is designed, which enables the simultaneous input of multiple temporal features to further improve the prediction accuracy and stability. The results show that the root mean square error ( RMSE ) of the DPR-ED model on the validation set is 1.028 m/s for the long-time sequence prediction, which is 6.6% better than that of the multilayer perceptron (MLP) model. When the two models are applied to the test dataset, the proportion with a low error of 0.1~0.3 m/s is improved by 4% and the large-error proportion is filtered by the DPR-ED model. The DPR-ED model performs 5.2% better than the MLP model with respect to the average prediction accuracy. Meanwhile, the variance is decreased by 15.6%. This novel framework enables the processing of long-time sequences with multiple input dimensions, which improves the prediction accuracy under complicated driving patterns and enhances the generalization-related performance and robustness of the model.

Suggested Citation

  • Diming Lou & Yinghua Zhao & Liang Fang & Yuanzhi Tang & Caihua Zhuang, 2022. "Encoder–Decoder-Based Velocity Prediction Modelling for Passenger Vehicles Coupled with Driving Pattern Recognition," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10629-:d:898111
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    References listed on IDEAS

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    Cited by:

    1. Liu, Huimin & Lin, Cheng & Yu, Xiao & Tao, Zhenyi & Xu, Jiaqi, 2024. "Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle," Applied Energy, Elsevier, vol. 365(C).

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