Analysis of The Remaining Amount Variation of A Lithium-ion Battery Using A Neural Network

Won-Hui Lee; JANG SUNG JIN

doi:10.34163/jkits.2020.15.3.006

Lithium-ion cells that are mounted on portable information terminals are almost impossible to find a matching cell due to various variables and unique characteristics. The method of calculating the remaining amount in a portable information terminal is an important item in terms of reliability of the terminal. In this paper, in order to measure the remaining amount of a specific lithium-ion cell, a measurement method is proposed that improves precision with a certain reference item for each element. First, the residual data value of the actual measured lithium-ion cell was trained using the error back propagation algorithm of the neural network. Second, computer simulation using Matlab was used as a type of residual quantity measurement method to make nonlinear numerical data relatively linear while reducing the error from the actual measured value for the residual information value. This method showed an unstable start in the initial state, but the result was relatively similar to the original data value as it went through the learning process of the actual measured data. This remaining amount measurement algorithm is an effective method that can be applied to portable information terminals. The analysis of the remaining amount variation of a lithium-ion battery using a neural network will be applicable to all IT devices as well as portable information terminals.

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* 2022년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

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