Fault-Tolerant Framework for State-of-Health Prediction of Lithium Batteries

Abstract

Accurately predicting the state of health (SoH) of lithium-ion batteries remains one of the top challenges, especially in uncertain environments where feature availability is not guaranteed. The main contribution of this article is to propose a highly available feature fault-tolerant prediction framework capable of predicting the SoH of batteries with a very high accuracy under sensor data unavailability. Previously published studies mitigated the feature incompleteness issue using models that deeply depend on a specific set of features; if one of the features becomes unavailable, the prediction model fails. Unlike the existing methods, the proposed feature fault-tolerant prediction framework proposes a novel agile approach so that when one of the features is missing, the model considers the next available feature to make the prediction. Our fault-tolerant prediction framework is highly available for SoH prediction and always predicts at a very high accuracy level. A hybrid deep learning model combining long short-term memory and deep neural networks along with a meticulous feature engineering design is used to train the NASA dataset and predict the SoH of batteries with a high precision. In order to prove the efficiency of the model, the feature fault-tolerant prediction framework configuration has been validated against two other datasets: Toyota Research Institute in partnership with MIT and Stanford dataset and NASA dynamic profile datasets. The performance is evaluated using the error indicators: mean absolute error, root-mean-square error, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> square.