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

Diming Lou, Yinghua Zhao, Liang Fang (), Yuanzhi Tang and Caihua Zhuang
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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

Sustainability, 2022, vol. 14, issue 17, 1-21

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.

Keywords: passenger vehicle; velocity prediction; encoder–decoder; driving pattern recognition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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