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Fuel consumption and vehicle breakdown depend upon the driving style of the driver, for example, hard driving style leads to more wear and consequently more failures of vehicle components. Because of this, it is important to identify and classify the driver's driving style in order to give the driver feedback through a driver assistance system. The driver would then be able to detect and learn to avoid a driving style that is not appropriate. The input data is provided by different sensors installed in the vehicle, where different drivers and driving routes have been measured. The data is subjectively classified into two different driving styles: normal and hard. Hard driving style can be characterized, for example, by rapid acceleration and braking. Since it is not trivial to build a model which is able to distinguish hard driving from normal, a data mining approach has been employed.

A new approach is proposed for fault detection. It builds on using the relationships between sensor values on vehicles to detect deviating sensor readings and trends in the system performance. However, in contrast to previous approaches based on such sensor relations, our approach uses a fleet of vehicles to define the normal conditions and relations. The relationships between the sensors are also determined automatically in a self-organized way on each vehicle, i.e. no off-line modeling is required. The proposed method is the first step in a remote diagnostics and maintenance service where error detection is done automatically, followed by a download of special purpose diagnostics software for the particular subsystem where the possible fault was detected.

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.

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.

A method for robust tuning of individual cylinders air-fuel ratio is proposed. The fuel injection is adjusted so that each cylinder has the same air-fuel ratio in inner control loops, and the resulting air-fuel ratio in the exhaust pipe is controlled with an exhaust gas oxygen sensor (EGO) in an outer control loop to achieve stoichiometric air-fuel ratio. Correction factors to provide cylinder individual fuel injection timing are calculated based on measurements of the ion currents for the individual cylinders. An implementation in a production vehicle is shown with results from driving on the highway.

Estimation of combustion variability can be performed by using ion currents measured at the spark plug. A scheme is here proposed that exploits the potential of using measurements from multiple cylinders to improve the estimation accuracy of combustion variability (measured by the coefficient of variation of IMEP). This is realised by dividing combustion variability into categories and having one classifier running for each cylinder with the ion current as input signal. The final estimate of combustion variability is then formed by a majority vote among the classifiers. This scheme is shown to improve estimation accuracy by up to 15% on measurements taken from highway driving in a production vehicle.

It is desirable for an engine control system to maintain a stable combustion. A high combustion variability (typically measured by the relative variations in produced work, COV(IMEP)) can indicate the use of too much EGR or a too lean air-fuel mixture, which results in less engine efficiency (in terms of fuel and emissions) and reduced driveability. The coefficient of variation (COV) of the ion current integral has previously been shown in several papers to be correlated to the coefficient of variation of IMEP for various disturbances (e.g. AFR, EGR and fuel timing). This paper presents a cycle-to-cycle ion current based method of estimating the approximate category of IMEP (either normal burn, slow burn, partial burn or misfire) for the case of lean air-fuel ratio. The rate of appearance of the partial burn and misfire categories is then shown to be well correlated with the onset of high combustion variability (high COV(IMEP)).

This paper investigates the use of the ionization current to estimate the Coefficient of Variation for the Indicated Mean Effective Pressure, COV(IMEP), which is a common variable for combustion stability in a spark ignited engine. Stable combustion in this definition implies that the variance of the produced work, measured over a number of consecutive combustion cycles, is small compared to the mean of the produced work. The COV(IMEP) is varied experimentally either by increasing EGR flow or by changing the air-fuel ratio, in both a laboratory setting (engine in dynamometer) and in an on-road setting. The experiments show a positive correlation between COV(Ion integral), the Coefficient of Variation for the integrated Ion Current, and COV(IMEP), when measured under low load on an engine in a dynamometer, but not under high load conditions. On-road experiments show a positive correlation, but only in the EGR and the lean burn case.

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.