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With the increase in the number of automobiles on road, there is a very strong emphasis on reducing the air pollution which led to evolution of stringent emission norms. To meet these stringent emission norms, the ideal solution is to optimize the engine hardware and the combustion system to reduce the emission at source thereby reducing the dependency on exhaust after treatment system. Gasoline Direct Injection (GDI) engines are gaining popularity worldwide as they provide a balance between fun to drive and fuel efficiency. Controlling the particle emissions especially Particle Number (PN) is a challenge in GDI engines due to the nature of its combustion system. In this study, experiments were performed on a 1.2Litre 3-cylinder 250bar GDI engine to capture the effect of injection strategies on PN.

The automobile industry is going through one of the most challenging times, with increased competition in the market which is enforcing competitive prices of the products along with meeting the stringent emission norms. One such requirement for BS6 phase 2 emission norms is monitoring for partial failure of the component if the tailpipe emissions are higher than the OBD limits. Recently PM (soot) sensor is employed for partial failure monitoring of DPF in diesel passenger cars.. PM sensor detects soot leakage in case of DPF substrate failure. There is a cost factor along with extensive calibration efforts which are needed to ensure sensor works flawlessly. This paper deals with the development of an algorithm with which robust detection of DPF substrate failure is achieved without addition of any sensor in the aftertreatment system.