Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

Xiaohua Zeng, Haoyong Cui, Dafeng Song, Nannan Yang, Tong Liu, Huiyong Chen, Yinshu Wang and Yulong Lei
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Xiaohua Zeng: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
Haoyong Cui: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
Dafeng Song: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
Nannan Yang: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
Tong Liu: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
Huiyong Chen: Zhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, China
Yinshu Wang: Zhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, China
Yulong Lei: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China

Energies, 2018, vol. 11, issue 6, 1-20

Abstract: Given its highly coupled multi-power sources with diverse dynamic response characteristics, the mode transition process of a power-split Hybrid Electric Vehicle (HEV) can easily lead to unanticipated passenger-felt jerks. Moreover, difficulties in parameter estimation, especially power-source dynamic torque estimation, result in new challenges for jerk reduction. These two aspects entangle with each other and constitute a complicated coupling problem which obstructs the realization of a valid anti-jerk method. In this study, a vehicle dynamics model with reference to a data-driven modeling method is first established, integrating a full-time artificial neural network engine dynamic model that can accurately predict engine dynamic torque. Then the essential reason for the occurrence of vehicle jerks in real driving conditions is analyzed. Finally, to smooth the mode transition process, a more practical anti-jerk strategy based on power-source torque changing rate limitation (TCRL) is proposed. Verification studies indicate that the data-driven vehicle dynamics model has enough accuracy to reflect the vehicle dynamic characteristics, and the proposed TCRL strategy could reduce the vehicle jerk by up to 85.8%, without any sacrifice of vehicle performance. This research provides a feasible method for precise modeling of vehicle dynamics and a reference for improving the riding comfort of hybrid electric vehicles.

Keywords: hybrid electric vehicle; data-driven modeling method; real vehicle data; torque changing rate limitation; anti-jerk strategy; riding comfort (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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