A Data-Driven Analysis to Determine the Electrical Needs of a Hybrid Powertrain System for Small, Hyper-Optimized, Track-Day Vehicles

In an effort to maximize the performance of RUSH Auto Work’s RUSH SR racecar, a hybrid powertrain system was designed and evaluated to estimate the performance gains from imple- menting such a system. An extensive Python program was developed to analyze real-world race data for the RUSH SR, determining energy losses while braking, the vehicle’s current acceleration capabilities, as well as the vehicle’s limitations. This ultimately quantified the vehicle’s current performance values/capabilities, and provided a strong foundation for the analyses that determined the anticipated implications of adding a hybrid powertrain system to the car. Despite the mass additions associated with adding an electric motor, battery pack, and additional components to control the system, the power gains from the system yielded a net greater power-to-weight ratio than the original vehicle without the hybrid sys- tem. An analysis of energy recuperation through regenerative braking demonstrated the potential to reduce the size of the battery pack (which decreases the mass of the system) without compromising on the power requirements and capabilities of the system. During periods of heavy braking, it was found that a significant portion of the battery could be recharged, allowing for significant reductions in the capacity of the battery pack.

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Asa, Henry J.

Advisor dc:contributor.advisor

• Winter, Amos G.

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156621

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156621