Nomenklatur - im ZESS

Abstract
Filter with a non-linear state space model there are Kalman-Filter algorithms that can determine
states and parameters while the system model remains linear. These algorithms have been
examined, extended and adapted to this particular problem. Comparing the different linear
adaptive algorithms with the non-linear Extended Kalman-Filter, the Maximum-Likelihood
based adaptive Kalman-Filter has proven to be the most suitable method for this problem. Basic
knowledge of the probability density function of the unknown parameters is not necessary
and the unknown parameters can be found in the complete state space model. The fact of
provable stability represents a significant aspect for a commercial use of this algorithm.
When the estimation of the air mass in the cylinder is performed by a combustion pressure signal
there is a necessity to eliminate the disturbances in the pressure signal. Therefore, a linear
Kalman-Filter for the determination of the absolute cylinder pressure value has been developed.
The algorithm is able to filter the combustion pressure signal during runtime and to determine
the variable offset provoked by the thermal shock. This procedure has been tested with an uncooled
combustion pressure sensor and compared to a watercooled one. In the vehicle, the
filter oscillates from working cycle to working cycle to any offset and follows a distinct drift
created by a change of the working conditions.
Through a sensitivity analysis of the parameters of the physically based model, the temperature
of the A/F mixture has been identified as the parameter which influences mainly the estimation
result and is difficult to determine. As a result, an adaptive Kalman-Filter algorithm has been
derived that estimates the cylinder individual air mass from measured cylinder pressure and
adapts the temperature of the A/F mixture through the lambda-sensor signal. The effect of the
time delay of the cylinder pressure information compared to conventional sensors is taken into
account by a prediction algorithm that determines the changes of the air mass during transient
conditions using throttle angle gradients.
The algorithm has been tested with measured vehicle data, implemented in a prototype engine
control unit with the following results. The deviation in the lambda-sensor signal is in the size
of the mass electronic control unit and even better. This has been achieved with a pure model
based algorithm without calibration on the testbed or in the vehicle.
The advantage of the estimation of the air mass on the basis of measured cylinder pressure
signals can be seen in the cylinder individual determination of the air mass, the exclusion of
conventional faults like air leakage and throttle faults, the high redundancy for reliability and
diagnostic concepts and by rendering the determination of the volumetric efficiency superfluous.
In addition, there are enormous possibilities for the realisation of cylinder individual engine
functions such as knock and spark angle control.
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