Forecasting battery capacity and power degradation with multi-task learning

TL;DR

This paper introduces a multi-task learning framework that accurately predicts both capacity and power degradation of lithium-ion batteries early in their life, outperforming single-task models in accuracy and efficiency.

Contribution

The paper presents a novel multi-task learning approach for simultaneous prediction of capacity and power fade in lithium-ion batteries, improving accuracy and reducing computational costs.

Findings

Average prediction errors of 2.37% for capacity and 1.24% for resistance.

Early-life degradation trajectory prediction with high accuracy.

Enhanced robustness and generalizability over single-task models.

Abstract

Lithium-ion batteries degrade due to usage and exposure to environmental conditions, which affects their capability to store energy and supply power. Accurately predicting the capacity and power fade of lithium-ion battery cells is challenging due to intrinsic manufacturing variances and coupled nonlinear ageing mechanisms. In this paper, we propose a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning. The model is able to predict the degradation trajectory of both capacity and internal resistance together with knee-points and end-of-life points accurately at early-life stage. The validation shows an average percentage error of 2.37% and 1.24% for the prediction of capacity fade and resistance rise, respectively. The model's ability to accurately predict the degradation, facing capacity and resistance estimation errors, further demonstrates the model's robustness and generalizability. Compared with single-task learning models for forecasting capacity and power degradation, the model shows a significant prediction accuracy improvement and computational cost reduction. This work presents the highlights of multi-task learning in the degradation prognostics for lithium-ion batteries.