Lap Time Simulation Tool for the Development of an Electric Formula Student Car Queen's University Belfast

Format:

Book

Conference/Event

Author/Creator:

Doyle, Doyle, author.

Contributor:

Cunningham, Geoffrey

Early, Juliana

White, Gavin

Conference Name:

WCX SAE World Congress Experience (2019-04-09 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2019

Summary:

This work details the development of a lap time simulation (LTS) tool for use by Queen's University Belfast in the Formula Student UK competition. The tool provides an adaptable, user-friendly virtual test environment for the development of the team's first electric vehicle. A vehicle model was created within Simulink, and a series of events simulated to generate the performance envelope of the car in the form of maximum combined lateral/longitudinal accelerations against velocity (ggv diagram). A four-wheeled vehicle including load transfer was modelled, capturing shifts in traction between each tire, which can influence performance in vehicles where the total tractive power is split between individual wheel motors. The acceleration limits in the ggv diagram were used to simulate the acceleration and endurance events at Formula Student. These events were simulated using a MATLAB code considering a point mass, quasi-steady state model with a perfect driver. This method considering all four wheels captures performance characteristics that point mass models normally cannot, without the complexity and time required for more detailed LTS solutions including yaw movements and driver models. It also separates the vehicle model from the MATLAB code required to run the LTS, reducing the complexity of implementing future changes. The LTS was benchmarked against the freeware tool OptimumLap and validated where possible against competition results. A Latin Hypercube sampling technique was employed to generate numerous input scenarios for the simulation, and a response surface fitted to the results to perform a sensitivity analysis. Results from this analysis indicate several areas to efficiently focus future resource allocation as well as attempting to quantify trade-offs. Optimum powertrain gear ratio and battery capacity for a proposed vehicle were specified using the tool

Notes:

Vendor supplied data

Publisher Number:

2019-01-0163

Access Restriction:

Restricted for use by site license