Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Hemmati, Sadra; Yadav, Rajeshwar; Surresh, Kaushik; Robinette, Darrell; Shahbakhti, Mahdi

doi:10.1177/09544070211070738

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Journal Article · Sat Jan 08 23:00:00 EST 2022 · Proceedings of the Institution of Mechanical Engineers. Part D, Journal of Automobile Engineering

DOI:https://doi.org/10.1177/09544070211070738· OSTI ID:2418407

^[1]; Yadav, Rajeshwar ^[2]; Surresh, Kaushik ^[3]; Robinette, Darrell ^[4]; Shahbakhti, Mahdi ^[5]

Villanova University, Villanova, PA, USA; OSTI
GKN Driveline, Detroit, MI, USA
Cummins, Detroit, MI, USA
Michigan Technological University, Houghton, MI, USA
University of Alberta, Edmonton, AB, Canada

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Research Organization:: Michigan Technological Univ., Houghton, MI (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

DOE Contract Number:: AR0000788

OSTI ID:: 2418407

Journal Information:: Proceedings of the Institution of Mechanical Engineers. Part D, Journal of Automobile Engineering, Journal Name: Proceedings of the Institution of Mechanical Engineers. Part D, Journal of Automobile Engineering Journal Issue: 14 Vol. 236; ISSN 0954-4070

Publisher:: SAGE

Country of Publication:: United States

Language:: English

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