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On-line Access: 2020-03-04

Received: 2019-08-31

Revision Accepted: 2019-11-01

Crosschecked: 2019-11-15

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Ming-xin Kang


Jin-wu Gao


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.2 P.340-350


Design of an eco-gearshift control strategy under a logic system framework

Author(s):  Ming-xin Kang, Jin-wu Gao

Affiliation(s):  State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; more

Corresponding email(s):   kangmx@mail.neu.edu.cn, gaojw@jlu.edu.cn

Key Words:  Stochastic logic system, Gearshift strategy, Receding-horizon optimization, Traffic information, Eco-driving

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Ming-xin Kang, Jin-wu Gao. Design of an eco-gearshift control strategy under a logic system framework[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(2): 340-350.

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Good access to traffic information provides enormous potential for automotive powertrain control. We propose a logical control approach for the gearshift strategy, aimed at improving the fuel efficiency of vehicles. The driver power demand in a specific position usually exhibits stochastic features and can be statistically analyzed in accordance with historical driving data and instant traffic conditions; therefore, it offers opportunities for the design of a gearshift control scheme. Due to the discrete characteristics of a gearshift, the control design of the gearshift strategy can be formulated under a logic system framework. To this end, vehicle dynamics are discretized with several logic states, and then modeled as a logic system with the Markov process model. The fuel optimization problem is constructed as a receding-horizon optimal control problem under the logic system framework,and a dynamic programming algorithm with algebraic operations is applied to determine the optimal strategy online. Simulation results demonstrate that the proposed control design has better potential for fuel efficiency improvement than the conventional method.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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