Identification and Validation of an Air Mass Flow Predictor Using a Nonlinear Stochastic State Representation 2000-01-0935
The control of an optimal combustion requires an estimation of cylinder air mass (mair), and the calculation of the injected fuel mass. However, the low bandwidth of the engine process and the fuel injector impose a 2 Top Dead Center (TDC) predictive computation of mair. The elaboration of an accurate predictor requires the determination of an exact and robust « intake manifold » model. Thus, the model has to be validated and qualified experimentally, even though no mair sensor and no physical model currently exist. From a behavioural model tending toward the real « intake manifold » behaviour, this study describes the determination of a stochastic state model. The non linearity of the process is treated, as well as the « no computation » characteristic of the internal combustion engine map. The numeralization method of the state system respects the real time constraint of the engine (engine speed interval [Niddle; 7000rpm]). This model is identified and validated on a PSA engine, as a Xk=manifold pressure estimator. The identification of state noise covariance and measurement noise covariance allows definition of an acurate and fast respond time estimator. Then, a 2 step predictor is elaborated, by integration of the stochastic state and measurement noise variables: average and covariance. The identification and validation has been done on the engine, and leads to a quality equivalent to that of the estimator. Thus, the “change base” operator allows, using the model, construction of mair estimator/predictor with similar qualities.