An Effective Fuel-Level Data Cleaning and Repairing Method for Vehicle Monitor Platform

With energy scarcity and environmental pollution becoming increasingly serious, the accurate estimation of fuel consumption of vehicles has been important in vehicle management and transportation planning toward a sustainable green transition. Fuel consumption is calculated by fuel-level data collected from high-precision fuel-level sensors. However, in the vehicle monitor platform, there are many types of error in the data collection and transmission processes, such as the noise, interference, and collision errors that are common in the high speed and dynamic vehicle environment. In this paper, an effective method for cleaning and repairing the fuel-level data is proposed, which adopts the threshold to acquire abnormal fuel data, the time quantum to identify abnormal data, and linear interpolation based algorithm to correct data errors. Specifically, a modified Gaussian mixture model (GMM) based on the synchronous iteration method is proposed to acquire the thresholds, which uses the particle swarm optimization algorithm and the steepest descent algorithm to optimize the parameters of GMM. The experiment results based on the fuel-level data of vehicles collected over one month prove that the modified GMM is superior to GMM-expectation maximization on fuel-level data, and the proposed method is effective for cleaning and repairing outliers of fuel-level data.