Abstract
The transformation of the trucking segment of the transportation fleet is imminent and the opportunity for several new innovative strategies to maximize the efficiency and effectiveness of future semi-truck powertrains is promising. One potential approach for energy efficiency enhancement of semi-trucks has been identified to be platooning, where the drag losses are minimized through improved aerodynamics. The proposed project, under the thrust area of ‘Novel Transportation’ technologies, focuses on developing the next-generation of planning, control, and monitoring of platooning for both electric and diesel semi-trucks. The overarching goal of this project is to understand the potential effects of platooning on lowering the energy requirements of electrified vehicles and to understand the trade-offs between safety and energy consumption. This project leverages an ongoing collaboration with Peloton Technologies to enable a platform for real-world deployment as well as datasets on actual energy consumption to improve the fidelity of models. The effects of platooning will be explored based on already existing experimental datasets supplemented by computational fluid dynamics-based models using the PI’s high-performance computational infrastructure.
Description
The trucking industry contributes about one-fourth of the carbonaceous emissions from the transportation sector while forming only about one-tenth of the on-road vehicle fleet. Therefore, a significant impact in decarbonizing the transportation sector could be achieved by developing approaches to reduce the energy intensity (energy consumed per mile) of the semi-truck fleet. Platooning, in the context of trucks is defined as ‘two or more vehicles arranging themselves in a particular order to travel together’, is largely inspired by swarm behavior observed in biophysical systems. A platooning configuration is defined using the total number of trucks involved and the distance between the trucks, in a successive manner or an adjacent manner or a combination of both. Such a configuration is known to have an enormous impact on the aerodynamics which effectively reduces the average energy intensity for the platoon. Furthermore, platooning improves the safety characteristics of the trucks involved in the platoon. In the proposed work, we build on our existing capabilities in electrified powertrains for freight transport to develop next-generation fleet-control frameworks for energy-efficient and safe platooning configurations of electric and diesel semi-trucks.
Task 1: Explore differences between platooning for diesel and electric semi-trucks.
1.1 Development of the powertrain models along with the acquisition of drive cycles: The nature and structure of the powertrains of diesel and electric semi-trucks are distinct, which has a strong influence on the design and aerodynamics of respective trucks. This aspect could have drastic implications for the prospect of platooning since the technique attempts to exploit the reductions in drag forces which depends on the exterior design of the truck. Hence, it is crucial to understand the key differences between the designs of the electric and diesel trucks together with the energy (or fuel) consumption characteristics in different configurations of platooning. We will develop power consumption models in conjunction with aerodynamics models which use real-world drive cycle data to provide accurate outputs over different time-scales i.e per trip or over the lifetime.
1.2 Utilization of powertrain models to understand distinctions between diesel and electric trucks: These aspects will be studied as functions of platooning configurations, thereby providing insights to study the optimal platooning configuration for each category of truck in terms of the number of trucks as well as the distance between the trucks. These insights and datasets will then be translated into the framework of vehicle (truck) control mechanisms and provide the foundation for second-order analyses where we look at variables like energy consumption and safety.
Task 2: Understand trade-offs between safety and energy consumption for platooning.
Once the powertrain models are functional, we will first use them to understand the trade-offs between safety and energy consumption for each category of vehicle.
2.1 Simplistic energy consumption map to understand energy consumption as a function of platooning configuration: The energy consumption of a platoon of trucks is a function of the inter-vehicle spacing, as shown in recent work from the PI. Inter-vehicle spacing affects the safety of the trucks and the drivers in the platoon. Furthermore, due to the differences in the design between electric and diesel trucks, the trisection of energy consumption-safety-vehicle spacing is of great interest, and understanding the trade-offs therein is of great value in efforts to design and implement control strategies for effective platooning for specific powertrains.
2.2 CFD analysis for energy consumption-safety: We will augment our preliminary studies with by leveraging computational fluid dynamics in the PI’s high-throughput and high-performance computational resources along with the powertrain (diesel and battery-electric) simulations to understand the energy consumption characteristics in conjunction with specific conditions for safety. We will derive a safety-energy consumption map which will be integrated into control systems. A critical part of this section will be the simulation of battery systems in real-world conditions which has a significant effect on the efficiency of the powertrain, here, we will utilize the PI’s capabilities and infrastructure on modeling and simulation of chemistry specific cell and battery packs.
2.3 Generation of safety-energy consumption maps for control platform integration: Energy consumption-safety maps constructed using powertrain modeling and computational fluid dynamics models will be used in identifying configurations that facilitate high energy efficiency without compromising safety. Moreover, the approach could form the basis for active controllers that can identify and implement dynamic configuration planning.
3.1 Testing and validation of the efficiency and safety maps: The computational and theoretical analyses along with the final implementable models and maps will be tested and validated through the collaboration with Peloton who currently have on-road use-cases in place for diesel semi-trucks. This will perform the function of validating the analytical and modeling effort as well as aiding in further tuning and optimization of the models and maps with the real-world data.
3.2 Feedback loop and full-scale Deployment with Peloton Technology: A feedback loop will be set up to augment the computational data with additional real-world whenever necessary from the testing and validation. This will be carried out initially for diesel and electric semi-trucks in separate fleets, and once the robust datasets are constructed, they will also be tested with fleets comprising of combinations of electric and diesel trucks.
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M. Guttenberg, S. Sripad, & V. Viswanathan. Evaluating the Potential of Platooning in Lowering the Required Performance Metrics of Li-Ion Batteries to Enable Practical Electric Semi-Trucks. ACS Energy Lett., 2, 2642-2646 (2017).
Timeline
Tabulated Timeline is appended to the Supplemental Information Document.
0.0: Project Management and Planning (Q1-Q8).
0.1: Kick-Off meeting (Q1).
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1
1.1: Powertrain modeling for diesel and electric trucks. (Q1-Q3)
1.2: Examination of platooning configurations with powertrain models. (Q2-Q3)
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2
2.1: First-order energy consumption map. (Q3-Q4)
2.2: CFD analysis to augment the models. (Q4-Q6)
2.3: Energy consumption-safety map. (Q4-Q5)
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3
3.1: Deployment of the models and maps to test and validate them. (Q6-Q7)
3.2: Feedback loop to refine and fine-tune the models and maps. (Q7-Q8)
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Note: Qi represents the 'i'th quarter of the two years proposed timeline.
Strategic Description / RD&T
Deployment Plan
Peloton has ongoing use-case projects in several geographical areas. As of January 2018, 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 Outcomes/Impacts
The two distinct tangible outcomes from the computation and theoretical 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 the results derived from the computational and theoretical work using Peloton Technology infrastructure.
2. Deployment and demonstration of the results for groups/ fleets of electric and diesel semi-trucks.
Expected Outputs
TRID
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| venkvis@cmu.edu |
Viswanathan, Venkat |
Carnegie Mellon University |
PI |
Faculty - Untenured, Tenure Track |
Budget
Amount of UTC Funds Awarded
$90000.00
Total Project Budget (from all funding sources)
$90000.00
Documents
| Type |
Name |
Uploaded |
| Data Management Plan |
Data_Management_Plan_2018_Mobility21_UTC_GGVeNrs.pdf |
Jan. 14, 2018, 11:36 a.m. |
| Progress Report |
179_Progress_Report_2018-09-30 |
Sept. 29, 2018, 8:15 a.m. |
| Presentation |
Electrification of semi-trucks |
Jan. 3, 2019, 12:12 a.m. |
| Progress Report |
179_Progress_Report_2019-03-30 |
March 29, 2019, 7:12 a.m. |
| Final Report |
179_-_Final_Report.pdf |
March 19, 2020, 5:39 a.m. |
| Progress Report |
179_Progress_Report_2019-09-30 |
Sept. 30, 2019, 3:24 p.m. |
| Progress Report |
179_Progress_Report_2020-03-30 |
March 29, 2020, 6:42 a.m. |
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Partners
| Name |
Type |
| Peloton Technologies |
Deployment Partner Deployment Partner |
