Throughout the history of the automobile there have been periods of intense interest in using ethanol as an alternative fuel to petroleum-based gasoline and diesel derivatives. Currently available flexible fuel vehicles (FFVs) can operate on a blend of gasoline and ethanol in any concentration of up to 85% ethanol. In all these FFVs, the engine management system relies on the estimation of the ethanol content in the fuel blend, which typically depends on the estimated changes in stoichiometry through an Exhaust Gas Oxygen (EGO) sensor. Since the output of the EGO sensor is used for the air-to-fuel ratio (AFR) regulation and the ethanol content estimation, several tuning and sensitivity problems arise. In this paper, we develop a simple phenomenological model of the AFR control process and a simple ethanol estimation law which can be representative of the currently practiced system in FFVs. Tuning difficulties and interactions of the two learning loops are then elucidated using classical control techniques. The sensitivity of the ethanol content estimation with respect to sensor and modeling errors is also demonstrated via simulations. The results point to an urgent need for model-based analysis and design of the AFR controller, the ethanol adaptation law and the fault detection issues in FFVs. Tuning and sensitivity issues are demonstrated via simulations and limitations are also discussed.

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