Abstract
In order to be improve the mobility of goods, energy-efficiency is critical for economics and platooning is a critical enabler to improves the productivity of freight movement. The project builds on earlier projects, PISES I-III, to build advanced simulation capabilities for understanding platooning in real-world environments. In particular, in this project, we will focus on estimating the overall power draw of the platoon using a platooning aware drag coefficient. In order to develop a model for platoon-aware drag coefficient over a wide range of operating conditions, the team will perform a series of computation fluid dynamics (CFD) simulations of platoons of trucks in single vehicle and platoon configurations. This will guide energy-estimation in real-world conditions which will be compared against real-world data obtained through deployment partner, Locomation.
Description
The project involves three tasks that build on our previous analysis from PISES I-III.
Task 1 will be to create a high-fidelity vehicle power model where the drag coefficient of the vehicle depends on a surrogate function, developed in Task 2, that will incorporate factors such as vehicle spacing, wind velocity, vehicle velocity, etc.
Task 2 will be to create a surrogate model, trained on high-fidelity drag coefficient data obtained from CFD, that will accurately provide drag coefficient values given the vehicle spacing of the platoon, vehicle velocity, wind velocity, wind heading, vehicle heading, the number of trucks in the platoon and the lateral offset.
Task 2.1 will be to create an adaptive sampling methodology that will ease the amount of CFD simulations needed to effectively train the model.
Task 2.2 will be to create the surrogate model and test the viable functional forms using model selection criterion such as the Bayesian Information Criterion.
Task 3 will be to incorporate the surrogate model into the Incepts platform to run high-fidelity platooning case studies for strategically chosen routes.
Task 3.1 will be to analyze the sensitivity of the results from Incepts to the different input parameters of the surrogate model.
A detailed description of the project is included as a Supplemental document.
Timeline
Task 1 - High Fidelity Vehicle Model with Functionality for the Drag Coefficient Surrogate Model: 1-3 months
Task 2 - Development of the Drag Coefficient Surrogate Model: 2-7 months
Task 2.1 - Adaptive Sampling Methodology for the Drag Coefficient Surrogate Model: 2-7 months
Task 2.2 - Creation and Testing of the Functional Form for the Drag Coefficient Surrogate Model: 2-5 months
Task 3 - Detailed Platooning Case Studies Using the Drag Coefficient Surrogate Model: 4-12 months
Task 3.1 - Sensitivity Analysis of Inputs to the Drag Coefficient Surrogate Model: 6-12 months
Strategic Description / RD&T
Deployment Plan
The project leverages our continued collaboration with Locomation.
* The platoon simulations will be compared against Locomation real-world data.
* Building on several of our previous projects, we have a robust dissemination plan.
* The findings will be published in Policy Forum articles and beyond.
* Findings from this study will be used to improve the Incepts software platform.
Expected Outcomes/Impacts
Similar to that described in projects #179, #285 and #309, two distinct tangible outcomes from the computation front are:
1. High-fidelity energy-efficiency maps for platoons incorporating platoon-offset, vehicle velocity, wind velocity, relative heading, spacing and number of vehicles.
2. A refined safety-energy reduction to velocity smoothing for the platooning vehicle.
On the deployment front:
1. Validation of predicted energy-efficiency of platoon configuration results using Locomation testing.
2. A continued longer-term goal will be to deploy and demonstrate results for groups/fleets of electric vs diesel semi-trucks.
Expected Outputs
TRID
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| mguttenb@andrew.cmu.edu |
Guttenberg, Matthew |
CMU |
Other |
Student - PhD |
| varunshankar@cmu.edu |
Shankar, Varun |
CMU |
Other |
Student - PhD |
| atalotta@andrew.cmu.edu |
Talotta, Anthony |
CMU |
Other |
Staff - Business Manager |
| venkvis@cmu.edu |
Viswanathan, Venkat |
CMU |
PI |
Faculty - Untenured, Tenure Track |
| awadell@andrew.cmu.edu |
Wadell, Alexius |
CMU |
Other |
Student - PhD |
Budget
Amount of UTC Funds Awarded
$100000.00
Total Project Budget (from all funding sources)
$100000.00
Documents
| Type |
Name |
Uploaded |
| Data Management Plan |
DMP_2021-2022.docx |
Nov. 17, 2021, 8:29 p.m. |
| Presentation |
389_-_PP.pptx |
June 2, 2022, 7:11 p.m. |
| Publication |
Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map to Addressing the Infrastructure Gap |
March 30, 2023, 5:39 a.m. |
| Publication |
Method for determining optimal placement of electric vehicle chargers |
April 10, 2023, 8:48 p.m. |
| Publication |
Method for determining optimal placement of electric vehicle chargers |
April 10, 2023, 8:48 p.m. |
| Publication |
INCEPTS: Software for high-fidelity electric vehicle en route state of charge estimation, fleet analysis and charger deployment |
April 10, 2023, 8:49 p.m. |
| Publication |
Universal battery performance and degradation model for electric aircraft |
April 10, 2023, 8:50 p.m. |
| Publication |
Evaluating the potential of platooning in lowering the required performance metrics of li-ion batteries to enable practical electric semi-trucks |
April 10, 2023, 8:50 p.m. |
| Publication |
Rapid Spatiotemporal Turbulence Modeling with Convolutional Neural ODEs |
April 10, 2023, 8:51 p.m. |
| Final Report |
Final_Report_-_389.pdf |
Oct. 4, 2023, 10:45 a.m. |
Match Sources
No match sources!
Partners
| Name |
Type |
| Locomation |
Deployment Partner Deployment Partner |
