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

This paper examines 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 thi...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2019-08, Vol.68 (8), p.7498-7510
Main Authors: Xu, Chu, Geyer, Stephen, Fathy, Hosam K.
Format: Article
Language:English
Subjects:
ADVANCED PROPULSION SYSTEMS
Algorithms
Automation
Computational efficiency
Computer simulation
Consumption
Dynamic programming
Engines
Fuel consumption
Fuel economy
Fuels
gear shift optimization
Gears
Heavy duty trucks
neural network
Optimization
Prediction algorithms
Pruning
Rain
rain flow counting
Real time
Recurrent neural networks
Schedules
Scheduling
Tradeoffs
Trajectory
Trucking industry
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Summary:This paper examines 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 challenge. 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. However, the rain flow count algorithm is both more effective in approaching the DP-based Pareto front and more computationally efficient.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2019.2921702