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In recent years more data is logged from the electronic control units on-board in commercial vehicles. Typically, the data is transferred from the vehicle at the workshop to a centralized storage for future analysis. This vast amount of data is used for debugging, as a knowledgebase for the design engineer and as a tool for service planning. Manual analysis of this data is often time consuming, due to the rich amount of information contained. However, there is an opportunity to automatically assist in the process based on knowledge discovery techniques, even directly when the trucks data is first offloaded at the workshop. One typical example of how this technique could be helpful is when two groups of trucks behave differently, e.g. one well-functioning group and one faulty group, when the two groups have the same specification. The desired information is the specific difference in the logged data, e.g. what particular sensors or signals are different.

Combustion timing control of SI engines can be improved by feedback of the peak pressure position (PPP). However, pressure sensors are costly, and therefore, nonintrusive and cheap ion-current ‘soft sensors’ have been suggested. Three different algorithms have been proposed that extract information about PPP from the ion current signal. In this paper, these approaches are compared with respect to accuracy, operational range, implementation aspects, as well as sensitivity to engine load and inlet air humidity.

In this paper a novel ion current based estimation scheme for the in-cylinder pressure peak position (PPP) is proposed. A reliable estimate is constructed by appropriate signal processing based on local curvatures of the post flame phase of the ion current. The peak-finding algorithm is simple and easy to implement in an engine control unit for feedback control of the combustion phasing. Results on real data, sampled onboard a commercial car are presented. Further, the performance of the algorithm is compared to two state of the art algorithms for PPP estimation from the ion current. The comparison shows that the algorithm presented in this paper outperforms its competitors1.

Two spark advance control systems are outlined; both based on feedback from nonlinear neural network soft sensors and ion current detection. One uses an estimate on the location of the pressure peak and the other uses an estimate of the location of the center of combustion. Both quantities are estimated from the ion current signal using neural networks. The estimates are correct within roughly two crank angle degrees when evaluated on a cycle to cycle basis, and roughly within one crank angle degree when the quantities are averaged over consecutive cycles. The pressure peak detection based control system is demonstrated on a SAAB 9000 car, equipped with a 2.3 liter low-pressure turbo charged engine, during normal highway driving.

A robust air/fuel ratio “soft sensor” is presented based on non-linear signal processing of the ion current signal using neural networks. Care is taken to make the system insensitive to amplitude variations, due to e.g. fuel additives, by suitable preprocessing of the signal. The algorithm estimates the air/fuel ratio to within 1.2% from the correct value, defined by a universal exhaust gas oxygen (UEGO) sensor, when tested on steady state test-bench data and using the raw ion current signal. Normalizing the ion current increases robustness but also increases the error by a factor of two. The neural network soft sensor is about 20 times better in the case where the ion current is not normalized, compared with a linear model. On normalized ion currents the neural network model is about 4 times better than the corresponding linear model.