A Dynamic Programming-Based Real-Time Predictive Optimal Gear Shift Strategy for Conventional Heavy-Duty Vehicles

Chu Xu, Abdullah Al-Mamun, Stephen Geyer, Hosam K. Fathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper examines the problem of utilizing upcoming terrain and vehicle speed predictions for gear shift trajectory optimization in conventional heavy-duty vehicles. The paper is motivated by the fuel savings potential of such optimization, especially in connected and automated heavy-duty trucks. A key goal of this work is to develop a computationally tractable online shifting algorithm with a fuel saving potential approaching that of existing offline global optimization methods from the literature. We consider two optimization objectives, namely, fuel consumption and gear shift frequency. We use dynamic programming to navigate the Pareto tradeoff between these objectives offline, for known vehicle duty cycles. The resulting gear shift trajectories collapse to an instantaneous shift map in the Pareto limit where fuel consumption minimization is the sole objective. We construct a neural network that anticipates the upcoming Pareto-optimal gear shift decision, given a sequence of gear shifts deemed ideal by the simple, instantaneous Pareto-limit shift map. We train this neural network using mix of urban, suburban, and highway drive cycles. The neural network reduces fuel consumption by 0.43%-4.16% in simulation, compared to a benchmark rule-based gear shift strategy.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5528-5535
Number of pages8
ISBN (Print)9781538654286
DOIs
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
Volume2018-June
ISSN (Print)0743-1619

Other

Other2018 Annual American Control Conference, ACC 2018
CountryUnited States
CityMilwauke
Period6/27/186/29/18

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Xu, C., Al-Mamun, A., Geyer, S., & Fathy, H. K. (2018). A Dynamic Programming-Based Real-Time Predictive Optimal Gear Shift Strategy for Conventional Heavy-Duty Vehicles. In 2018 Annual American Control Conference, ACC 2018 (pp. 5528-5535). [8430948] (Proceedings of the American Control Conference; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2018.8430948