Optimal Powertrain Energy Management for Autonomous Hybrid Electric Vehicles With Flexible Driveline Power Demand Using Approximate Dynamic Programming
Mohammadali Kargar, Tohid Sardarmehni, Xingyong Song
Abstract
The increasing number of vehicles, the excessive use of fossil fuels, and the related safety and environmental issues have motivated studies on autonomous vehicles and Hybrid Electric Vehicles (HEVs). In this article, we focus on the control of the powertrain energy management for an autonomous HEV. A new powertrain control strategy is enabled by leveraging one of the uniqueness in the powertrain management of an autonomous vehicle, i.e., the instantaneous power generated by the powertrain does not need to exactly follow the power demand by the vehicle motion controller. This is referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flexible power demand</i> , which adds an extra degree of freedom to the powertrain energy management, and can lead to control design achieving better fuel economy. The powertrain control is then formulated under the Approximate Dynamic Programming (ADP) framework, and the power flexibility is incorporated in the ADP formulation. At last, an example of multiple connected HEVs following a leader vehicle operating in an off-road scenario is given to demonstrate the feasibility of the proposed method.