Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Capacity Estimation and Box-Cox Transformation

Abstract

Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in intelligent battery management systems (BMSs). The current RUL prediction methods are mainly developed based on offline training, which are limited by sufficiency and reliability of available data. To address this problem, this paper presents a method for RUL prediction based on the capacity estimation and the Box-Cox transformation (BCT). Firstly, the effective aging features (AFs) are extracted from electrical and thermal characteristics of lithium-ion batteries and the variation in terms of the cyclic discharging voltage profiles. The random forest regression (RFR) is then employed to achieve dependable capacity estimation based on only one cell's degradation data for model training.Secondly, the BCT is exploited to transform the estimated capacity data and to construct a linear model between the transformed capacities and cycles. Next, the ridge regression algorithm (RRA) is adopted to identify the parameters of the linear model. Finally, the identified linear model based on the BCT is employed to predict the battery RUL, and the prediction uncertainties are investigated and the probability density function (PDF) is calculated through the Monte Carlo (MC) simulation. The experimental results demonstrate that the proposed method can not only estimate capacity with errors of less than 2%, but also accurately predict the battery RUL with the maximum error of 127 cycles and the maximum spans of 95% confidence of 37 cycles in the whole cycle life.

Degradation-Sensitive Health Indicator Construction for Precise Insulation Degradation Monitoring of Electromagnetic Coils

“…This ensures the same analytical methodology can be applied to various types of data, ultimately improving the accuracy of the analysis. The Box-Cox transformation can be used to transform variables with positive values that do not obey the normal distribution into variables that obey the normal distribution [ 30 , 31 ]. The assessment result of the transformed MTS is shown in Figure 10 .…”

Simple parameterization method using differential evolution algorithm to estimate state of charge for Li‐ion batteries and packs for electric vehicles

“…The proposed scheme has provided promising results to obtain certain parameters in Li-ion batteries, which encourages to analyze other important studies concerning the life-time batteries such as degradation characterization 37 and remaining useful life, [38][39][40][41] which are highly affected by noisy information. The DE approach can be a robust alternative to calculate these features with competitive accuracy.…”

Machine Learning-based Remaining Useful Life Prediction Techniques for Lithium-ion Battery Management Systems: A Comprehensive Review

“…RUL prediction based on health status monitoring was proposed by Xue et al [52] where Random forest regression (RFR) was exploited for capacity estimation and the Box-Cox transformation (BCT) method was used for a linear mapping of capacity and RUL. Besides charging/discharging data, the consideration of thermal characteristics in battery capacity estimation enhanced the accuracy of RUL prediction.…”