Formulation and Comparison of Two Real-Time Predictive Gear Shift Algorithms for Connected/Automated Heavy-Duty Vehicles

Journal Article · 07 June 2019 · IEEE Transactions on Vehicular Technology

DOI: https://doi.org/10.1109/TVT.2019.2921702 · OSTI ID:1556940

Xu, Chu  ^[1]; Geyer, Stephen ^[2]; Fathy, Hosam K.  ^[1]

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1. Pennsylvania State Univ., University Park, PA (United States)
2. Volvo Group North America, Washington, DC (United States)

Our work investigates the problem of predictive gear scheduling for fuel consumption minimization in connected/automated heavy trucks. The literature highlights the fuel economy benefits of such predictive scheduling, but there is a need to optimize such scheduling online, in real time. To address this need, we begin by using dynamic programming (DP) to schedule gear shifting offline, in a manner that achieves a globally optimal Pareto tradeoff between the conflicting objectives of minimizing fuel consumption and shift frequency. The computational cost of DP is unfavorable for online implementation, but we present two algorithms addressing this issue. Both algorithms rely on the fact that in the Pareto limit where fuel consumption minimization is the sole objective, DP furnishes a simple static shift map . Our first algorithm trains a recurrent neural network to prune the shift schedule generated by this map. The second algorithm performs this pruning in a direct manner tailored to reduce the schedule's rain flow count. We simulate these algorithms for different drive cycles. Both algorithms achieve a reasonable tradeoff between fuel consumption and gear shift frequency. Yet, the rain flow count algorithm is both more effective in approaching the DP-based Pareto front and more computationally efficient.

• Research Organization: Pennsylvania State Univ., University Park, PA (United States)
• Sponsoring Organization: USDOE Advanced Research Projects Agency - Energy (ARPA-E)
• Grant/Contract Number: AR0000801
• OSTI ID: 1556940
• Journal Information: IEEE Transactions on Vehicular Technology, Vol. 68, Issue 8; ISSN 0018-9545
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English