Allen, Cody M

Ibitayo, Ife J

Ore, Jonathan

Shaver, Greg

Taylor, Alexander H

English

Purdue E-Pubs

Enabling High-Efficiency Control Systems for Connected and Automated Class 8 TrucksPurdue University, Cummins Inc., Peloton Technology, NREL, Peterbilt, ZFPrincipal Investigator: Dr. Greg Shaver Graduate Research Assistants: Alexander H Taylor, Cody M Allen, Jonathan Ore, Ife J IbitayoProblem Statement and Goals Engine and transmission fuel efficiency improvements have remained isolated from emerging Connected and Automated Vehicle (CAV) applications Use a collaborative vehicle and powertrain solution to reduce fuel consumption and CO2 emissions by up to 20% Target $2,000-3,000 incremental vehicle cost at mass production scalesConcept 2: Cloud-Based OptimizationConnectivity-enabled, real-time, powertrain control and optimization Use off board computation power to improve control of the powertrain Enables sophisticated real-time optimization Enables long horizon MPC Expected fuel savings contribution: 5%Computationally expensive optimizations enabled by cloud-based computingMore efficient two-truck platooning using connectivity-enabled shifting coordination and lead truck predictive cruise Platooning Optimization of platooning controller integrated with engine and transmission controllers Platoon broken less frequently on grades Driver experience improved Expected fuel savings contribution: 7.25% Predictive cruise Both vehicles at SAE J3016 Level 1 Compute optimal speed profile and vehicle gaps Avoid torque saturation of rear truck Expected fuel savings contribution: 4% Coordinated Shifting Preventing inefficient (and annoying) disturbances in the platooning gap when trucks shift independently. Coordinate shifting of both trucks, to minimize gap disturbance Expected fuel savings contribution: 1%Concept 3: Improved Vehicle CoordinationThe platooning gap is hard to regulatewhen either truck is near its torque limitExperimental Validation of ConceptsPartnersCloud Based Optimization & Model Predictive Control AlgorithmsConcept 1: Calibration VariationVariable engine calibrations based on terrainAdaptive calibrations based on system configurationDynamic calibrations based on look-ahead informationConnectivity-enabled, remote powertrain calibration Tune engine calibration using connectivity-enabled information about Mission profile, terrain, traffic, weather Application Variation System-to-system variation Component aging Impacts vehicle performance and fuel consumption Two-way communication between cloud and powertrain Expected fuel savings contribution: 2.5% Demonstration of concepts via hardware-in-the-loop (HIL) engine testing and vehicle experimentationHardware-in-the-Loop (HIL) engine test cell at the Ray W. Herrick Labs at Purdue UniversityOn-vehicle concept demonstrationBox Trailer CalibrationFlatbed Trailer Calibration

Enabling High-Efficiency Control Systems for Connected and Automated Class 8 Trucks

https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=4291&amp;context=roadschool

Enabling High-Efficiency Control Systems for Connected and Automated Class 8 Trucks

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