Intelligent system for analyzing battery charge consumption processes

Abstract

The article develops an intelligent system of analysis and neural network forecasting of battery charge consumption for automated vehicles (AGVs). For this purpose, the types of AGV and the methods of effective forecasting of their battery charge consumption were analyzed. It is established that they are based on optimal robot control processes; application of technologies to increase capacity and extend service life. The data for the forecast was collected using the UAExpert OPC UA client, which allowed to convert the informative components of the data vector into a format suitable for further processing (csv). To eliminate outliers in the signals, a dispersion analysis of each parameter of AGV was carried out. Data for which the sigma value exceeded 1.5 were considered partialle lost and were replaced by a moving average of 12 points (the number of ANN inputs). For training, verification and testing of neural networks, parameters with high and medium positive correlation dependence were selected according to the Pearson correlation coefficient. Short-term and medium-term forecasting of battery charge consumption for AKTZ was carried out on the basis of ANN with deep learning, the model of which was tested in two modes: forecasting and prediction. The effectiveness of the developed system was investigated by testing it on the data obtained from Formica-1 AGV. The average absolute testing error was less than 1%. The highest value of the prediction error was less than 9 % when predicting such parameters as current position and X-coordinate, which are correlated with battery charge consumption for AGV. It has been established experimentally that the accuracy of the forecast of battery charge consumption for various types of AGV has been improved.