Integrated Model Predictive Control of Hybrid Electric Vehicles Coupled With Aftertreatment Systems

Abstract

For hybrid electric vehicles (HEVs), the well-studied fuel economy optimization approaches will usually implement frequent engine starts/stops, which can significantly impact the catalyst temperatures and performances of the aftertreatment systems. This paper aims to simultaneously optimize fuel economy and reduce tailpipe emissions for HEVs coupled with aftertreatment systems. First, a control-oriented model is developed by systematically incorporating HEV models with aftertreatment thermal dynamic models, both of which have been experimentally validated. The integrated model is capable of predicting engine-out gas temperature and NOx emissions and simulating the temperature dynamics of the aftertreatment systems. Additionally, post injections' characteristics are investigated and modeled. An aftertreatment warm-up approach is developed by strategically enabling double post injections. Eventually, a supervisory controller is designed to optimize the post-injection ratio and the torque split ratio of HEV powertrains. The controller designed is rooted in a model predictive control (MPC) scheme, and it has an intuitive interpretation in terms of operating costs. The validation results show that the controller can significantly reduce the warm-up time and can successfully regulate the catalyst temperature to the desired range with a reasonable sacrifice of fuel economy for HEVs.