Fuel-Saving-Oriented Collaborative Driving Strategy for Commercial Vehicles Based on Driving Style Recognition

Hongqing Chu, Zongxuan Li, Jialin Wang and Jinlong Hong ()
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Hongqing Chu: School of Automotive Studies, Tongji University, Shanghai 201804, China
Zongxuan Li: School of Automotive Studies, Tongji University, Shanghai 201804, China
Jialin Wang: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
Jinlong Hong: School of Automotive Studies, Tongji University, Shanghai 201804, China

Energies, 2023, vol. 16, issue 17, 1-21

Abstract: Fuel-saving-oriented collaborative driving is a highly promising yet challenging endeavor that requires satisfying the driver’s operational intentions while surpassing the driver’s fuel-saving performance. In light of this challenge, the paper introduces an innovative collaborative driving strategy tailored to the objective of fuel conservation in the context of commercial vehicles. An enhancement to this strategy involves the development of a network prediction model for vehicle speed, leveraging insights from driver style recognition. Employing the predicted speed as a reference, a model-predictive-control-based optimal controller is designed to track the reference while optimizing fuel consumption. Furthermore, a straightforward yet effective collaborative rule is proposed to ensure alignment with the driver’s intention. Subsequently, the proposed control scheme is validated through simulation and real-world driving data, revealing that the human–machine cooperative driving controller saves 4% more fuel than human drivers.

Keywords: collaborative driving strategy; commercial vehicles; model predictive control; driving style recognition; fuel-saving (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: 2023
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