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http://dx.doi.org/10.7840/kics.2017.42.4.939

Prognostics and Health Management for Battery Remaining Useful Life Prediction Based on Electrochemistry Model: A Tutorial  

Choi, Yohwan (Sogang University Department of Electronic Engineering)
Kim, Hongseok (Sogang University Department of Electronic Engineering)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.42, no.4, 2017 , pp. 939-949 More about this Journal
Abstract
Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.
Keywords
Remaining Useful Life; Lithium-ion Battery; Prognostics and Health Management; Electrochemistry;
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Times Cited By KSCI : 3  (Citation Analysis)
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