Search Results

The first application in which the fuel cell will find a market in the passenger car is as an “electrochemical battery” serving the purpose the fuel cell can do best: To generate electricity for the electrical power bus with a high degree of efficiency. Such a fuel cell referred to as an APU (Auxiliary Power Unit) exceeds the power output and endurance of a battery and is able not only to supply power to all conventional electrical power-consuming items in the car, but also to provide new functions such as air conditioning when the car is at a standstill. In the long run, indeed, the fuel cell may even be able to replace the electrical alternator.

The study of oil emission is of essential interest for the engine development of modern cars, as well as for the understanding of hydrocarbon emissions especially during cold start conditions. A laser mass spectrometer has been used to measure single aromatic hydrocarbons in unconditioned exhaust gas of a H2-fueled engine at stationary and transient motor operation. These compounds represent unburned oil constituents. The measurements were accompanied by FID and GC-FID measurements of hydrocarbons which represent the burned oil constituents. The total oil consumption has been determined by measuring the oil sampled by freezing and weighing. It has been concluded that only 10 % of the oil consumption via exhaust gas has burned in the cylinders. A correlation of the emission of single oil-based components at ppb level detected with the laser mass spectrometer to the total motor oil emission has been found.

The real-time concentrations of benzene, toluene, xylene, trimethyl-benzene and naphthalene in vehicle exhaust have been monitored during the FTP-cycle with a time-resolution of 20 ms and a sensitivity of 50 ppb. Using a laser mass spectrometer, the aromatic hydrocarbons in unconditioned exhaust gas at sampling positions behind the exhaust valve, before and behind the catalytic converter have been analyzed. The comparison of the emissions sampled before and behind the catalytic converter reveals the effect of dealkylation of the aromatic hydrocarbons in the catalytic converter. Whereas most of the aromatic hydrocarbons are burned in the hot catalytic converter, however, bursts of aromatic hydrocarbons are released at transient motor operation. In these moments, which can be attributed to phases of closed throttle valve and very low engine load at gear changes, a significant part of the C1-, C2- and C3- benzenes has been converted into benzene.

In order to reduce emissions of spare ignition engines using a three way catalyst, a stoichiometric air-fuel ratio must be guaranteed in stationary and transient operation of the engine. This aim can be reached by using a specific feed-forward structure for the control of the paths of air and fuel based on identification abilities of Artificial Intelligence. As approximators for multidimensional nonlinear static functions we will use specific Neural Networks (NN) together with sophisticated stability-proven learning structures. The acquired knowledge within the NN determines our control action mainly through using feed-forward structures. Our investigations are based on the so-called mean-value-modelling approach of SI engines; it is our aim to present this strategy.

Time-resolved concentrations of formaldehyde and acetaldehyde in the exhaust gas have been investigated during transient motor operation, such as sudden change of speed and load, misfiring and switching off the fuel mixture control. To this purpose, a new laser mass spectrometer has been applied which is capable of measuring the concentrations of individual exhaust compounds with 1 ppm sensitivity at a sampling rate of 50 Hz corresponding to a sampling period of 20 ms. At sudden speed changes, high concentrations of aldehydes are observed, in particular during the phase of decreasing speed, i.e. after closing the throttle valve.

Existing electronic combustion engine control systems only guarantee a desired air-to-fuel-ratio λ in stationary operation. In order to achieve the desired λ also in in-stationary use of the engine, it is necessary to use new-technology-based control systems. Artificial Intelligence provides methods to cope with difficulties like wide operation range, unknown nonlinearities and time delay. We will propose a strategy for control of a Spark Ignition Engine to determine the mass of air inside the combustion chambers with the highest accuracy. Since Neural Networks are universal approximators for multidimensional nonlinear static functions they can be used effectively for identification and compensation purposes of unknown nonlinearities in closed control loops.