SOC Estimation of Mine Power Supply Based on Improved BP Neural Network Algorithm. Issue 1 (June 2020)

• Record Type: Journal Article
• Authors: Ren, Gaojian; Lin, Yin; Sun, Zhongguang; Shao, Yan
• Is Part Of: IOP conference series. Volume 526:Issue 1(2020)
• Publication Date: 2020-06
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/526/1/012112
• Languages: English
• ISSNs: 1755-1307

Abstract

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%.