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


Vol. 42,  No. 4, pp. 939-949, Apr.  2017


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  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.

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  Cite this article

[IEEE Style]

Y. Choi and H. Kim, "Prognostics and Health Management for Battery Remaining Useful Life Prediction Based on Electrochemistry Model: A Tutorial," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 4, pp. 939-949, 2017. DOI: .

[ACM Style]

Yohwan Choi and Hongseok Kim. 2017. Prognostics and Health Management for Battery Remaining Useful Life Prediction Based on Electrochemistry Model: A Tutorial. The Journal of Korean Institute of Communications and Information Sciences, 42, 4, (2017), 939-949. DOI: .

[KICS Style]

Yohwan Choi and Hongseok Kim, "Prognostics and Health Management for Battery Remaining Useful Life Prediction Based on Electrochemistry Model: A Tutorial," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 4, pp. 939-949, 4. 2017.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880