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For ensuring environmental safety, strong emphasis on CO2 pollution reduction is mandated which led to evolution of miller cycle engines. However, the inherent Miller engine characteristic is the lower volumetric efficiency when compared to otto engines because of which small turbo chargers with variable geometry turbines are used to induct air into the engine. With miller engine and VGT turbo charger combination arises the challenges of charge controllability because of lower inertia and reduced vane control area. With conventional turbo charger control methods, the response time is slow thereby leading to turbo lag or severe over boosting, this is overcome by accurate engine modelling and using the same as input for charger control.

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.

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.

Precise control over mixture formation withhigh fuel pressure and multiple injections allows Gasoline Direct Injection (GDI) engines to be operated satisfactorily at extreme conditions wherePort Fuel Injection (PFI) engines wouldnormally struggle due to combustion instability issues. Catalyst heating phase is one such important condition which is initiated after a cold engine start to improve the effectiveness of the three-way catalyst (TWC). For a given TWC specification, fast light-offof TWC is achieved in the catalyst heating phase by increasing the exhaust gas temperature with higher exhaust mass flow. The duration of this phase must be as short as possible, as it is a trade-off between achieving sufficient TWC light off performance and fuel efficiency.

Most of the energy consumed in today's mobility industry is derived from fossil fuels. The demand for clean, renewable and affordable alternative energy is forcing the automotive industry to look beyond the conventional fossil fuels. Fuels options like liquefied petroleum gas (LPG), compressed natural gas (CNG) and ethanol blends are quickly finding widespread acceptance as alternative sources. This paper presents the results of experimental studies conducted on a 1.2-liter MPI engine with three different alternate fuels. The fuels considered for the evaluation (apart from base gasoline) are 10% ethanol-blended fuel (E10), LPG (gaseous propane: butane mix) and CNG (gaseous methane). Experiments were conducted to compare their effect on engine performance and emissions. The test results show that E10 has the lowest power drop whereas CNG has the highest power drop (12%) as compared to gasoline. The maximum power drop in LPG is 4%, which is close to the theoretical predictions.