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This paper investigates an approach to modelling real-world drive cycles for the prediction of fuel economy and emission levels. It demonstrates that a steady-state engine performance data based modelling approach can be used for real-world drive cycle simulation. It identifies and demonstrates that a steady-state performance data-based approach is the only current viable approach for real-world tailpipe-out CO level predictions. It also identifies quantitatively the difference between the modal emission measurements and constant volume sampling (CVS) bag values for emission modelling validation. A systematic validation and sensitivity analysis of the modelling approach is also described.

This research introduces a new, novel approach to reverse flow particulate filter regeneration enabled by rapidly pulsed electric discharges. The discharges physically dislodge particulate matter (PM) from the filter substrate and allow a very low reverse air flow to transport it to a soot handling system. The system is operable independent of filter temperature, does not expose the filter to high thermal stresses or temperatures, has no apparent upper limit for filter PM-mass level (regeneration of a filter up to 17 g/L has been demonstrated), and does not require any catalyst. The system is inherently scalable allowing application to monolithic filters of any size or shape and can be tailored to suit specific application requirements such as limits on maximum regeneration time or power consumption. For example a light duty application would require as little as 200-500W electrical power to regenerate a filter in less than ten minutes (i.e. passenger car GPF or DPF).