Chambon, P., Deter, D., Irick, D., and Smith, D., "PHEV Cold Start Emissions Management," SAE Int. J. Alt. Power. 2(2):252-260, 2013, doi:10.4271/2013-01-0358.
Plug-in hybrid electric vehicles (PHEV) operate predominantly as electric vehicles (EV) with intermittent assist from the engine. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current conventional vehicles, the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.ORNL, in collaboration with the University of Tennessee, developed an Engine-In-the-Loop (EIL) test platform to investigate cold start emissions on a 2.0l Gasoline Turbocharged Direct Injection (GTDI) Ecotec engine coupled to a virtual series hybrid electric vehicle. The end-goal of this project is to demonstrate the benefits of coordinating engine and powertrain supervisory control strategies to minimize cold start emissions.First, this paper provides a summary of the results obtained by optimizing engine cold start strategies on their own within the context of a PHEV application where the engine can be motored up to speed and supplemented with the electric machine. These specific operating modes open up new engine calibration opportunities. This study investigated the effect of different cranking injection, post-start load, idle speed and spark timing strategies.The paper then reports on the second phase of the project which focuses on the coordination of engine control strategies and hybrid energy management strategies. Stand-alone optimization of each component's algorithms does not guarantee that the resulting hybrid powertrain will operate efficiently. Therefore cold start strategies have to be controlled and optimized as a system to minimize tailpipe emissions. Comparison results of different coordination algorithms are presented to demonstrate the benefit of system coordination and optimization.