Essential Dynamics for Developing Models for Control of Connected and Automated Electrified Vehicles: Part A - Powertrain

Abstract 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. Even though the CAV technology is applicable to both conventional and electrified powertrains, the energy saving opportunities are more apparent when the CAVs are Hybrid Electric Vehicles (HEVs). This is because of the flexibility in the vehicle powertrain and possibility of choosing optimum powertrain modes based on the predicted traction power needs. In this paper, 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.