Gaussian Process Regression-Based Lithium-Ion Battery End-of-Life Prediction Model under Various Operating Conditions

TL;DR

This paper introduces a Gaussian process regression model with novel kernels to predict lithium-ion battery end-of-life based solely on operating conditions, achieving significant accuracy improvements.

Contribution

The study develops a simplified, accurate battery end-of-life prediction model using tailored kernels in Gaussian process regression, reducing prediction error substantially.

Findings

Prediction error reduced by 46.62% with novel kernels.

Model uses only operating conditions as inputs.

Generated 100 battery degradation datasets for validation.

Abstract

For the efficient and safe use of lithium-ion batteries, diagnosing their current state and predicting future states are crucial. Although there exist many models for the prediction of battery cycle life, they typically have very complex input structures, making it very difficult and expensive to develop such models. As an alternative, in this work, a model that predicts the nominal end-of-life using only operating conditions as input is proposed. Specifically, a total of 100 battery degradation data were generated using a pseudo two-dimensional model with three major operating conditions: charging C-rate, ambient temperature and depth-of-discharge. Then, a Gaussian process regression-based model was developed to predict the nominal end-of-life using these operating conditions as the inputs. To improve the model accuracy, novel kernels were proposed, which are tailored to each operating condition. The proposed kernels reduced the lifetime prediction error by 46.62% compared to the conventional kernels.