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Compressed natural gas (CNG) is considered as one of the most promising alternative fuels for transportation due to its ability to reduce greenhouse gas emissions (CO2, in particular) and its abundance. An earlier study from IFP has shown that CNG has a considerable potential when used as a fuel for a dedicated downsized turbo-charged SI engine on a small urban vehicle. To take further advantage of CNG assets, this approach can be profitably extended by adding a small secondary (electrical) power source to the CNG engine, thus hybridizing the powertrain. This is precisely the focus of the new IFP project, VEHGAN, which aims to develop a mild-hybrid CNG prototype vehicle based on a MCC smart car equipped with a reversible starter-alternator and ultra-capacitors (Valeo Starter Alternator Reversible System, StARS).

In nowadays diesel engine, the turbocharger system plays a very important role in the engine functioning and any loss of the turbine efficiency can lead to driveability problems and the increment of emissions. In this paper, a VGT turbocharger fault detection system is proposed. The method is based on a physical model of the turbocharger and includes an estimation of the turbine efficiency by a nonlinear adaptive observer. A sensitivity analysis is provided in order to evaluate the impact of different sensors fault, (drift and bias), used to feed the observer, on the estimation of turbine efficiency error. By the means of this analysis a robust variable threshold is provided in order to reduce false detection alarm. Simulation results, based on co-simulation professional platform (AMEsim© and Simulink©), are provided to validate the strategy.