MPC-Based Energy Management Strategy for an Autonomous Hybrid Electric Vehicle

IEEE Open Journal of Industry Applications

Ali Emadi¹  Saeed Amirfarhangi Bonab² 

[1] Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada;Mechanical Engineering, McMaster University, Hamilton, Canada;

关键词: Autonomous vehicle;    convex optimization;    energy management strategy;    hybrid electric vehicle;    model predictive control;    power-split powertrain;   

DOI  :  10.1109/OJIA.2020.3029969

来源: DOAJ

【 摘 要 】

Despite the current intense research on each of the subjects of electrification and autonomous driving, potential advantages as a result of the interaction of these two mainstreams in automotive have not been effectively studied yet. Autonomous vehicles generate an unprecedented amount of real-time data due to excessive use of perception sensors and processing units. In this article, we present a novel approach for improving the fuel economy of an autonomous hybrid electric vehicle by taking advantage of this qrydata. We introduce the term of autonomous-specific energy management strategy (ASEMS) and we present an example of such a strategy using model predictive control (MPC). Specifically, we show how a more fuel-optimal energy management strategy (EMS) can be achieved for the power-split powertrain of an autonomous hybrid electric vehicle using the motion planning data. We use an optimization-based motion planning approach and feed the resulting velocity profile up to the prediction horizon to the MPC-based EMS. The presented approach shows 2% to 12.81% less fuel consumption for the two extreme cases of 100 and 1000 meters as the prediction horizons, compared to a rule-based EMS. The presented EMS fuel-optimality for the 1000 meters is only 6.91% sub-optimal compared to the globally optimal results of dynamic programming.

【 授权许可】

Unknown