SOC Estimation of Mine Power Supply Based on Improved BP Neural Network Algorithm

Abstract

Ni-MH, with high electric energy density and high stability, is the main backup battery of mine power supply. To improve the estimation accuracy of battery SOC (state of charge) and prolong its service life, this paper takes the single Ni-MH battery as the experimental object, and adopts an improved BP neural network that adds an adaptive rate momentum term to increase the discharged amount of electricity. The input characteristics of the network are the same as temperature, current and terminal voltage. According to the variance of the error between the expected output and the actual output of the training sample, the momentum term is adjusted; The adaptive learning rate is increased according to the change direction of the back propagation error. By comparing the standard BP neural network algorithm, the convergence speed is increased by 70%, and the generalization error is within 4%.

Evaluation of Ideological and Political Education under Deep Learning Neural Network

“…If the difference between the error and the set target exceeds a certain range, it indicates that the correction is excessive at this time, and the step size should be reduced. The learning rate is multiplied by the decrement factor K dec to reduce the learning rate while undoing the previous correction process [ 21 ].…”