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Project

#285 Platooning for Improved Safety and Efficiency of Semi-trucks [PISES - II]


Principal Investigator
Venkat Viswanathan
Status
Completed
Start Date
July 1, 2019
End Date
Dec. 1, 2020
Research Type
Applied
Grant Type
Research
Grant Program
FAST Act - Mobility National (2016 - 2022)
Grant Cycle
2019 Mobility21 UTC
Visibility
Public

Abstract

As highlighted in the Mobility21 Next-gen trucking summit, electrification and platooning are two major technology drivers that can lead to both lower costs and emissions.  The on-going project aims to identify the synergy between these two mega-trends around platooning and electrification.  In the first year of the project, we have built high-fidelity computational fluid dynamics models to estimate the effects of platooning on the aerodynamics and subsequently, the energy consumption.  In parallel, we have built vehicle dynamics models for diesel and electric semi-trucks.  In addition, the first year has led to a deep collaboration with our industrial project partner, Peleton Technologies.  Based on our collective discussion, we have now identified several key questions that will be tackled in the coming years using the simulation and testing infrastructure built up by the team.  In particular, for this year, we will evaluate issues related to (i) platooning configurations, (ii) safety and (iii) logistics.    
Description
At present, about 51 percent of freight miles in the U.S. are in states that approve commercial truck platooning.  This provides a technology that can allow drivers to link their vehicles’ active safety systems to enable vehicle following at an aerodynamic following distance.[1]   Our industrial partner, Peloton is a connected and automated vehicle technology company dedicated to improving the safety and efficiency of U.S. and global freight transportation, which is backed by ten Fortune Global 500 companies.[1]  Peloton’s flagship driver-assistive platooning system links the active safety systems of pairs of trucks and connects them to a cloud-based Network Operations Cloud (NOC) that limits platooning to appropriate roads and conditions. Peloton solutions also require best-in-class forward collision avoidance systems and other safety features, thereby incentivizing their adoption.[1]

In the first year of this project (#179), the team has built a close tight-knit collaboration with the CMU team's expertise in electrification complemented by Peleton's experience on platooning.  We have built CFD capability utilizing the PI's high-performance computing infrastructure as well as CMU's ANSYS partnership that allows exploration of platooning configurations and its effect on drag reduction.  The research goals will be carried out with the following tasks:

Task 1: Quantify the Effects of Platoon Configurations
1.1 Energy Consumption as a Function of Following Distance
We have previously shown that energy consumption is a function of following distance using existing datasets. We will continue to improve our understanding of this correlation through computational fluid dynamic (CFD) analysis, utilizing the PI’s high performance computing resources to produce high fidelity solutions for a variety of platoon configurations. Employing this insight, along with powertrain models for both diesel and electric semi-trucks, will allow development of a detailed energy consumption-vehicle spacing relation.
1.2 Understanding Impact of Mixed-Truck Platoons
Most previous work has focused on analyzing platoons of identical trucks or vehicles. We understand that on the road, homogenous platoons may not be viable, and that the rise of electric trucks will lead to an increased variety of cab and trailer types and combinations. In order to determine what impact this may have on the aerodynamics of the platoon, we will utilize CFD analysis with a range of tractor-trailer geometry to gain a better picture of how individual truck aerodynamics can affect the energy consumption of the platoon as a whole.
1.3 Analysis of Real-World Effects 
There are a number of factors that influence energy consumption and efficiency in semi-trucks. We highlight two to explore that can have a substantial impact during real-world use. From an aerodynamic perspective, strong cross- or head-winds can decrease the performance of a platoon by limiting the drag gain achieved. CFD models will be used to characterize these losses. From a thermodynamic perspective, ambient temperature will impact battery chemistry, and thus the efficiency of the powertrain. The PI has extensive proficiency in battery modeling, which will be key to properly describing this dependence.      

Task 2: Safety and Logistics
2.1 Identifying Safety Requirements and Efficiency Trade-offs
An essential metric to recognize prior to deployment is when efficiency must be compromised to make way for safety. Leveraging our deployment partner, Peloton, for real-world insights and existing data regarding truck safety, along with the efficiency maps generated from CFD simulations will provide an understanding of optimum following distances for a specific platoon configuration. This data can be supplied to adaptive cruise, cooperative adaptive cruise, or more advanced controllers to implement platoon planning on the road.
2.2 Impact of Platoons on Traffic Flow
Part of the deployment process is understanding how platoons, and especially large platoons, can disrupt or encourage traffic flow. There is evidence to suggest smaller platoons can promote traffic density and velocity, while much larger ones can obstruct passenger or other vehicles, particularly where merging or changing lanes is required, resulting in an overall lower traffic flow. Simulation of this mixed traffic will be an important tool in determining ideal platoon policy given not only optimizing for energy consumption, but also considering impact on other vehicles as well. 
2.3 Effectiveness as a Function of Operation Type, Route Statistics:
The effectiveness will depend on operation route, in particular, the influence of different routes like local, regional, inter-state travel.  This task will analyze the effectiveness under different operation types as well.

Task 3: Deployment and Validation
The tasks will seek input from our very active collaboration with the deployment partner, Peloton Technologies and we will continue to perform validation effort.  In particular, we will perform validation against real-world performance.

[1] https://peloton-tech.com/majority-of-us-freight-ton-miles-now-occur-in-platooning-approved-states/    
Timeline
_____________________________________________________________________
Task 1: Quantify the Effects of Platoon Configurations
1.1 Energy Consumption as a Function of Following Distance (Q1-Q2)
1.2 Understanding Impact of Mixed-Truck Platoons (Q2-Q3)
1.3 Analysis of Real-World Effects (Q3-Q5)

_____________________________________________________________________
Task 2: Safety and Logistics
2.1 Identifying Safety Requirements and Efficiency Trade-offs (Q3-Q5)
2.2 Impact of Platoons on Traffic Flow (Q4-Q6)
2.3 Effectiveness as a Function of Operation Type, Route Statistics: (Q4-Q6)
_____________________________________________________________________
Task 3: Deployment and Validation
3.1: Model Validation (Q1-Q8)
3.2: Deployment strategy (Q4-Q8)
_____________________________________________________________________
Note: Qi represents the 'i'th quarter of the two years proposed timeline.    
Deployment Plan
Peloton has ongoing use-case projects in several geographical areas. As of January 2019, Peloton Technology’s platooning demonstration project in Florida completed over 1,000 miles. Furthermore, over nine states in the United States, namely, Arkansas, Georgia, Michigan, Nevada, North Carolina, Ohio, South Carolina, Tennessee and Texas have permitted commercial deployment of platooning for Class 8 semi-trucks. Peloton Technology also has ongoing projects under the Federal Highway Administration (FHWA) and the US Department of Transportation (USDOT).

The computational and theoretical analysis undertaken by the PI will deliver in control maps and data that will be developed and integrated into monitoring and control algorithms. Prior to full-scale deployment, these results will be tested and validated within Peloton’s infrastructure along with a feedback loop where the maps and data will be further refined if necessary. This refining process will include real-world road conditions and driving patterns acquired through the testing and validation. The deployment phase will entail the use of the results within diesel and electric semi-trucks in separate distinct fleets and the second phase of deployment will include possible combinations of both kinds of semi-trucks.      
Expected Accomplishments and Metrics
Similar to that described in project #179, two distinct tangible outcomes from the computation front are:
1. Highly discretized efficiency maps to be used in control systems.
2. A safety-energy consumption trade-off map for improved design and implementation of the platooning. 

On the deployment front:
1. Validation of drag reduction results using Peloton testing.
2. The longer-term goal will be deploy and demonstrate results for groups/fleets of electric vs diesel semi-trucks.    

Individuals Involved

Email Name Affiliation Role Position
varunshankar@cmu.edu Shankar, Varun MechE Other Student - PhD
ssripad@andrew.cmu.edu Sripad, Shashank MechE Other Student - PhD
venkvis@cmu.edu Viswanathan, Venkat MechE PI Faculty - Untenured, Tenure Track

Budget

Amount of UTC Funds Awarded
$90000.00
Total Project Budget (from all funding sources)
$100000.00

Documents

Type Name Uploaded
Data Management Plan UTC_2019.docx Jan. 5, 2019, 12:56 a.m.
Presentation PISES-II_Viswanthan.pptx Feb. 27, 2019, 4:30 p.m.
Progress Report 285_Progress_Report_2019-09-30 Sept. 30, 2019, 3:28 p.m.
Progress Report 285_Progress_Report_2020-03-30 March 29, 2020, 6:40 a.m.
Publication Learning non-linear spatio-temporal dynamics with convolutional Neural ODEs April 9, 2021, 6:30 a.m.
Publication Trade-offs between automation and light vehicle electrification April 9, 2021, 6:30 a.m.
Final Report Final_Report_-_285.pdf June 7, 2021, 5:35 a.m.

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Partners

Name Type
Peloton Technologies Deployment Partner Deployment Partner