Setting Power and Torque Limits of Regenerative Braking Using Big Data 2019-01-0362
An important engineering challenge to developing electric vehicles is balancing the regenerative braking performance of the vehicle with the vehicle cost. Power and torque powertrain system limits are important variables in balancing the regenerative braking design challenge with higher limits for both typically corresponding with higher units cost and vehicle complexity.
This study seeks to examine the relative kinetic energy available for recapture through regenerative braking by imposing different torque and power limits over energy frequency data to inform regenerative braking limit decisions. This data is produced by characterizing wheel torque and vehicle speed data from braking events on a time frequency basis, multiplying the time frequency matrix by the respective bin power to produce an energy frequency matrix, non-dimensionalizing the data to be non-vehicle specific, and processing the resulting acceleration and speed data on an energy frequency basis.
Driving data was collected from Ford hybrid vehicle and conventional vehicle drivers through OpenXC and Big Data Drive applications, broadcasting encoded CAN information over Bluetooth and WiFi networks. Braking events were isolated in the data by examining powertrain command signals. Analysis of these events was performed using a combination of Spark, Python and Matlab.
The study will describe the relative increase in percentage of energy that is available for capture at increasing power and torque system limits.
John McCormick, N Khalid Ahmed, Eric Bramson