Capturing the Impact of Speed, Grade, and Traffic on Class 8 Truck Platooning

New simulation tools are necessary to predict the fuel savings, safety, and performance benefits of connectivity-enabled control systems for Class 8 trucks. The simulation framework described here combines high fidelity vehicle and powertrain models (uniquely, and with their controllers provided by the manufacturer) with a novel production-intent platooning controller. This controller commands propulsive engine torque, engine-braking, or friction-braking to a rear vehicle in a two-truck platoon to maintain a desired following distance. Additional unique features of the framework include high fidelity road grade and traffic speed data. A comparison to published experimental platooning results is performed through simulation with the platooning trucks traveling at a constant 28.6 m/s (64 MPH) on flat ground and separated by 11 m (36 ft). Simulations of platooning trucks separated by a 16.7 m (54.8 ft) gap are also performed in steady-state operation, at different speeds and on different grades (flat, uphill, and downhill), to demonstrate how platooning affects fuel consumption and torque demand (propulsive and braking) as speed and grade are varied. For instance, while platooning trucks with the same 16.7 m gap at 28.6 m/s save the same absolute quantity of fuel on a 1% grade as on flat ground (1.00 per-mile), the trucks consume more fuel overall as grade increases, such that relative savings for the platoon average decrease from 6.90% to 4.94% for flat vs. 1% grade, respectively. Furthermore, both absolute and relative fuel savings improve during platooning as speed increases, due to increase in aerodynamic drag force with speed. There are no fuel savings during the downhill operation, regardless of speed, as the trucks are engine braking to maintain reasonable speeds and thus not consuming fuel. Results for a two-truck platoon are also shown for moderately graded I-74 in Indiana, using traffic speed from INDOT for a typical Friday at 5PM. A 16.7 m (54.8 ft) gap two-truck platoon decreases fuel consumption by 6.18% over the baseline without degradation in trip time (average speed of 28.3 m/s (63.3 MPH)). The same platooning trucks operating on aggressively graded I-69 in Indiana shows a lower platoon-average 3.71% fuel savings over baseline at a slower average speed of 24.5 m/s (54.8 MPH). The impact of speed variation over, and grade differences between, these realistic routes (I-74 & I-69) on two-truck platooning is described in detail.

[1]  Johannes Scharf,et al.  Road-to-rig-to-desktop - Virtual development using real-time engine modeling and powertrain-co-simulation , 2017 .

[2]  Dong-il Dan Cho,et al.  A multi-vehicle platoon simulator , 2000 .

[3]  James F. Bell,et al.  Aerodynamic drag of heavy vehicles (class 7-8): simulation and benchmarking , 2000 .

[4]  Zoran Filipi,et al.  Validation and Use of SIMULINK Integrated, High Fidelity, Engine-In-Vehicle Simulation of the International Class VI Truck , 2000 .

[5]  Xiao-Yun Lu,et al.  Automated Truck Platoon Control , 2011 .

[6]  Mohd Azrin Mohd Zulkefli,et al.  Evaluating Connected Vehicles and Their Applications , 2016 .

[7]  Daliang Shen,et al.  Fuel Efficient Speed Optimization for Real-World Highway Cruising , 2018 .

[8]  John McPhee,et al.  Powertrain Modeling and Model Predictive Longitudinal Dynamics Control for Hybrid Electric Vehicles , 2018 .

[9]  Sadayuki Tsugawa,et al.  Results and issues of an automated truck platoon within the energy ITS project , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[10]  Chris R. Ciesla,et al.  A Powertrain Simulation for Engine Control System Development , 1996 .

[11]  Magdy Elbahnasawy,et al.  Bias Impact Analysis and Calibration of Terrestrial Mobile LiDAR System With Several Spinning Multibeam Laser Scanners , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[12]  David M. Bevly,et al.  An Integrated CFD and Truck Simulation for 4 Vehicle Platoons , 2018 .

[13]  Bilin Aksun Güvenç,et al.  A connected and autonomous vehicle hardware-in-the-loop simulator for developing automated driving algorithms , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[14]  Mohd Azrin Mohd Zulkefli,et al.  Vehicle and Powertrain Optimization for Autonomous and Connected Vehicles , 2017 .

[15]  Cihan Bayındırlı,et al.  The Determination Of Aerodynamic Drag Coefficient Of Truck and Trailer Model By Wind Tunnel Tests , 2016 .

[16]  David M. Bevly,et al.  Results of initial test and evaluation of a Driver-Assistive Truck Platooning prototype , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[17]  Umit Ozguner,et al.  Dynamic Eco-Driving’s Fuel Saving Potential in Traffic: Multi-Vehicle Simulation Study Comparing Three Representative Methods , 2018, IEEE Transactions on Intelligent Transportation Systems.

[18]  Adam Duran,et al.  Effect of Platooning on Fuel Consumption of Class 8 Vehicles Over a Range of Speeds, Following Distances, and Mass , 2014 .