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

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.

Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics

“…Therefore, this technology can improve the function of BMSs for EV batteries with high voltages, which can increase user and manufacturer satisfaction [10]. Research is being actively performed on technology to predict the RUL of lithium-ion batteries (LIBs) by applying prognostics and health management based on machine learning [11][12][13][14][15][16][17]. Conventional prognostics and health management detect deterioration or failure due to aging or harsh environments of various mechanical devices or electronic boards such as BMS [18].…”

“…Other methods for RUL prediction include data analysis, model-based methods, and hybrid methods that combine data analysis and models [11][12][13][14]. Recently, data analysis has been more frequently used and involves acquiring and analyzing performance data that change over time according to the unique characteristics of the battery.…”

“…Although mathematical modeling based on the analysis of the charge/discharge mechanisms can provide highly accurate and interpretable results, it is difficult to model large battery packs as opposed to single cells, and environmental variables cannot be reflected in the model even if interlock circuits and automatic control are considered [10]. RUL prediction using filtering, a statistical technique, has recently attracted research attention [13,14]. For instance, the particle filter (PF) [13], which has been primarily used in the fields of control, location tracking, and pattern recognition, has been recently used for battery RUL prediction.…”