Vehicle Deceleration Prediction Based on Deep Neural Network at Braking Conditions

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Vehicle Deceleration Prediction Based on Deep Neural Network at Braking Conditions

Min, Kyunghan / Yeon, Kyuhwan / Jo, Yuhyeok / Sim, Gyubin / Sunwoo, Myoungho / Han, Manbae

The smart regenerative braking system in electric vehicles implements automatic control of the regeneration torque of motor to improve driver’s comfort and energy efficiency. To apply this system, the accurate prediction of the vehicle deceleration states is the preliminary step to reflect the driver’s behaviors. In this paper, we proposed a vehicle deceleration prediction model via deep neural network, which consists of a sequential recurrent neural network model with long-short term memory cell and a two-layer conventional neural network model. This model accommodates the physical constraint to designate the vehicle stop location in front of the traffic signals. The model is trained by vehicle experiment data with three drivers through the hyper-parameter optimization method. Using this model, the deceleration characteristics are characterized by two explicit parameters such that deceleration point, maximum point according to the initial slope and the shape of the braking profile. Using these two parameters as clustering variables through a K-means clustering method, the deceleration types are classified. These deceleration types to the input to the prediction model results in higher prediction accuracy of the vehicle states. The driving style of the three drivers at braking situations is analyzed according to the deceleration types as well.

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Dokumentinformationen

Titel :

Vehicle Deceleration Prediction Based on Deep Neural Network at Braking Conditions

Weitere Titelangaben:

Int.J Automot. Technol.

Beteiligte:

Min, Kyunghan (Autor:in) / Yeon, Kyuhwan (Autor:in) / Jo, Yuhyeok (Autor:in) / Sim, Gyubin (Autor:in) / Sunwoo, Myoungho (Autor:in) / Han, Manbae (Autor:in)

Erschienen in:

International Journal of Automotive Technology ; 21 , 1 ; 91-102

Erscheinungsdatum :

2020-02-01

Format / Umfang :

12 pages

ISSN :

1976-3832 , 1229-9138

DOI :

https://doi.org/10.1007/s12239-020-0010-2

Medientyp :

Aufsatz (Zeitschrift)