Signal-filtration methodology for estimation of fuel level

Abstract: Knowing how much fuel there is in a car is important for a predictable driving experience. Such knowledge will dictate how a person drives, when to refuel and how long they can drive. However unprocessed fuel level signals are highly noisy and therefore misleading.To ensure a good and predictable driving experience it is important to estimate the fuel level. The way this thesis has tackled this problem is by comparing and evaluating different filtering methods.The estimation algorithms were designed based on a saddle type tank developed by Volvo Car Corporation. The fuel level sensor consists of a floater arm and can only detect fuel levels within its maximal and minimal positions. The tank size can deviate from the standard volume and it will affect the measurement. Acceleration, angular orientation and fuel consumption are all factors that disturb fuel level estimation and therefore their relationship to the estimation problem is investigated. An experiment was devised to investigate the relationship between angular orientation, fuel volume and fuel level readings. ARX based models were made including angular orientation or acceleration. The relationship was concluded to be non-linear. The Kalman, $H_{\infty}$, Particle and Recursive Least Squares filters were compared. The Kalman and RLS filters had the most desirable traits and were therefore further developed. Both Kalman and RLS resulted in smooth estimates on the driving cycles tested.The Kalman filter provided a steadier estimate and could be easily tuned for faster convergence to zero. The Kalman filter can easily be changed to accommodate parametric uncertainties which improve its robust qualities.However the relationship between angular orientation and fuel level readings are non-linear. Therefore the RLS method was considered more robust for a reduced biased fuel reading under angular orientations. In conclusion the most desirable filter is a filter that provides the best traits from both filters.