A method of battery capacity prediction based on fuzzy logic and Neural networks. Issue 5 (August 2020)

• Record Type: Journal Article
• Authors: He, Tingting; Xu, Zhipeng; Xie, Qi; Cheng, Shan; Zhang, Sheng
• Is Part Of: IOP conference series. Volume 558:Issue 5(2020)
• Publication Date: 2020-08
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/558/5/052015
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: With the wide use of lithium battery, its online monitoring and residual capacity prediction have been paid much attention. There are two methods for predicting the residual capacity of lithium batteries, namely model method and data-driven method. The traditional model method requires in-depth understanding of the material characteristics and aging mechanism of the battery. However, it is difficult to establish an accurate model due to the complex electrochemical reactions in the battery and the vulnerability to external factors. The data-driven law has been applied more widely because of its good applicability and flexibility. This paper presents a method of battery capacity prediction based on fuzzy logic and neural networks. The lithium battery data published by PCoE are selected for the test, and the results show that the prediction error of the method for the residual capacity of single battery is less than 2%, which indicates that the method has a good applicability for the complex nonlinear system of lithium battery pack, and can obtain accurate battery capacity prediction value, and it has a good application prospect.