Predicting AGV Battery Cell Voltage Using a Neural Network Approach with Preliminary Data Analysis and Processing

Modern methods for solving the AGV battery cell voltage prediction problem include a symbiosis of probabilistic and machine learning methods. In this study, we propose to use two RNN-based approaches with preliminary data analysis and processing. Five filters were selected for smoothing: Moving average; Weighted moving average; Exponential smoothing; Smoothing with local regression; Lowess smoothing. RNN forecasting was performed on the basis of a centered stationary signal obtained by subtracting the value obtained with the five filters from the original signal.The forecast accuracy was evaluated using the following errors: MAPE, RMSE, MSPE, RMSPE, MFE, MBE, SEBIAS, ME. Adequacy of the proposed neural network model based on the determination coefficient R2. The coefficient of determination for all the centered signals obtained using the above five methods yielded adequate predictive models. Exponential smoothing (MAPE=0.002; RMSE=141.481; R2=0.94) and LS (MAPE=0.002; RMSE=124.239; R2:0.96) proved to be the most effective methods for short-term forecasts. Moving average (MAPE=0.00207; RMSE=146.589; R2=0.94) is recommended for long-term forecasts with additional smoothing of the forecasted values because of the noise in them.