Fuel Level Estimation in Tank of Truck in Motion
Abstract
The paper presents the results of a case study on estimating the fuel level in the tank of a motor vehicle. A method based on the concept of particle filtering of noisy measurement data is proposed. The algorithm designed using the Sequential Monte Carlo method with Sequential Importance Sampling is combined with classical digital filters used for signal filtering. In the simulations, real data obtained by measuring fuel levels in the tanks of TIR heavy trucks from one of the Polish trucking companies are used. The performance of the applied method was considered in various measurement situations, such as refueling, driving on an uneven road surface, driving on steep roads, and fading of the measurement signals.
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