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Quality and up-time management of vehicles is today receiving much attention from vehicle manufacturers. One of the reasons is that there is a desire to avoiding on-road failures to addressing potential issues during routine maintenance intervals or at times more convenient to the operator. Forthcoming telematic platforms and advanced diagnostic algorithms can enable the possibility to proactively handle problems and minimize stops. The platforms bring the possibility of increasing knowledge of fault characteristics and making diagnostic decisions by using a population of vehicles. However, this requires real-time diagnostic algorithms that process data both onboard and offboard at a central server. The paper presents a self organizing approach for failure and deviation detection on a fleet of vehicles. The approach builds on using parametric models for encoding the characteristical relations between different sensor readings for a vehicle sub-system or component.

Operators of fleets of vehicles desire the best possible availability and usage of their vehicles. This means the preference is that maintenance of a vehicle is scheduled with as long intervals as possible. However, it is then important to be able to detect if a component in a specific vehicle is not functioning properly earlier than expected (due to e.g. manufacturing variations). This paper proposes a telematic based fault detection scheme for enabling fault detection for diagnostics by using a population of vehicles. The basic idea is that it is possible to create low-dimensional representations of a sub-system or component in a vehicle, where the representation (or model parameters) of a vehicle can be monitored for changes compared to the model parameters observed in a fleet of vehicles.

With the introduction of unleaded gasoline, special fuel agents have appeared on the market for lubricating and cleaning the valve seats. These fuel agents often contain alkali metals that have a significant impact on the ion current signal, thus affecting strategies that use the ion current for engine control and diagnosis, e.g. for estimating the location of the pressure peak. This paper introduces a method for making neural network algorithms robust to expected disturbances in the input signal and demonstrates how well this method applies to the case of disturbances to the ion current signal due to fuel additives containing Sodium. The performance of the neural estimators is compared to a Gaussian fit algorithm, which they outperform. It is also shown that using a fuel additive significantly improves the estimation of the location of the pressure peak.

This paper compares several strategies for air-fuel ratio transient control. The strategies are: A factory standard look-up table based system (a SAAB Trionic 5), a feedback PI controller with and without feed-forward throttle correction, a linear feed-forward control algorithm, and two nonlinear feed-forward algorithms based on artificial neural networks. The control strategies have been implemented and evaluated in a SAAB 9000 car during a transient driving test, consisting of an acceleration in the second gear from an engine speed of 1500 rpm to 3000 rpm. The best strategies are found to be the neural network based ones, followed by the table based factory system. The two feedback PI controllers offer the poorest performance.