Simulator Assessment Theory for Remaining Useful Life Prediction of Lithium-Ion Battery Under Multiple Uncertainties

Liang Zhao,Qiang Li,Bin Suo

doi:10.1109/access.2020.2987426

Abstract

As Lithium-ion batteries become the main power source in various electronics, it is important to predict the remaining useful life (RUL) of these batteries, in order to make the maintenance strategy and avoid serious consequences caused by the failure of power supply. With the convenience in fitting field measurements, the model based methods are widely used in RUL prediction for lithium-ion batteries. However, these predictions are usually unreliable because of incomplete uncertainty quantification. This paper proposes a model update method for the RUL prediction of lithium-ion battery based on the Bayesian simulator assessment theory. With an empirical degradation model, the method quantifies the uncertainties in model parameters, model form and measurements error. It infers the reality prediction to battery failure threshold with a combination of multiple uncertainties. The main innovation of the proposed method is that it doesn't only adjust the model parameters, but also the bias function which accounts for the model form uncertainty. And a modular Markov chain Monte Carlo method is employed to implement the model update with multiple uncertain parameters. As uncertainties are considered systematically in the inference process, it can provide a reliable RUL prediction. We demonstrate the predictive capability of the method by the real life cycle dataset of lithium-ion batteries from NASA.

Highlights

With the advantages of high energy density, small memory effect and light weight, the lithium-ion batteries have been widely used in the fields of portable electronics, aerospace electronic devices and electric vehicles
In this paper, a lithium-ion battery capacity prognostic method based on Bayesian model update idea is developed
Instead of developing some new degradation models, this paper focuses on multiple uncertainties quantification in order to obtain reliable remaining useful life (RUL) estimation with the existing empirical model

Summary

INTRODUCTION

With the advantages of high energy density, small memory effect and light weight, the lithium-ion batteries have been widely used in the fields of portable electronics, aerospace electronic devices and electric vehicles. In an uncertainty expression view, the existing filter approaches and Bayesian approaches all take into account the uncertainty of model parameters and measurement error when they predict the RUL of battery. They are not enough to obtain a reliable prediction since the model form uncertainty is not considered. The model update idea based on simulator assessment theory [32]–[35] allows for multiple sources of uncertainty, including the uncertainty of model parameters, the uncertainty of model form and the uncertainty of measurements In this theory, a bias function is specified to be the difference between the true value of the real battery capacity and the degradation model output.

SIMULATOR ASSESSMENT THEORY

INSTANCE STUDY

Findings

CONCLUSION