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Increasingly complex hybrid electric vehicle (HEV) powertrains are being developed to address the growing stringency of emissions regulations, fuel economy standards and drivability/performance requirements. Early in their design process, it is desirable to rapidly evaluate powertrain architectures and components using simulation models before committing to costly physical prototyping. However, HEV powertrains have multiple controlled degrees of freedom, such as power split, engine on/off and gear selection, the operation of which needs to be pre-determined before a meaningful performance evaluation can be carried out. In this paper, we describe a multistage mixed integer trajectory optimization methodology that allows design engineers to rapidly perform performance analyses of complex powertrains. The methodology can generate optimal input signals for both continuous (engine torque or motor power) and discrete (engine on/off or gear selection) degrees of freedom for a given scenario.

An engine torque modulation methodology is developed for use in engines equipped with electronic throttle control, ETC. It is shown through simulation that an engine with ETC is capable of following a transmission torque modulation command that includes a linearly increasing torque during the torque phase, a torque reduction during the inertia phase and a post-shift torque increase. It is also shown that such a strategy can be used to minimize the transmission output torque variation during a shift. The impact of engine indicated mean effective pressure, IMEP, variation on vehicle longitudinal acceleration is analyzed using Monte-Carlo simulation. It is shown that the IMEP induced variation in the vehicle acceleration is largely due to end of shift engine torque output timing errors. It is then recommended that during a shift the engine IMEP variation be held to a one-sigma variation of three percent or less.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

In the paper we employ numerical optimal control techniques to define the best transient operating strategy for a turbocharger power assist system (TPAS). A TPAS is any device capable of bi-directional energy transfer to the turbocharger shaft and energy storage. When applied to turbocharged diesel engines, the TPAS results in significant reduction of the turbo-lag. The optimum transient strategy is capable of improving the vehicle acceleration performance with no deterioration in smoke emissions. These benefits can be attained even if the net energy contribution by the TPAS during the acceleration interval is zero, i.e., all energy is re-generated and returned back to the energy storage by the end of the acceleration interval. At the same time the total fuel consumption during the acceleration interval may be reduced.