Accurate battery lifetime prediction across diverse aging conditions with deep learning

TL;DR

This paper presents a universal deep learning framework for accurate battery lifetime prediction across diverse aging conditions, leveraging inter-cell features and a comprehensive benchmark to improve robustness and reduce prediction error.

Contribution

The study introduces a novel deep learning approach that incorporates inter-cell feature differences and supports cross-condition learning, enhancing battery lifetime prediction accuracy and generalizability.

Findings

Achieved 10% prediction error using only 100 cycles.

Nearly halved prediction error in low-resource scenarios.

Demonstrated robustness across 168 cycling conditions and 5 electrode materials.

Abstract

Accurately predicting the lifetime of battery cells in early cycles holds tremendous value for battery research and development as well as numerous downstream applications. This task is rather challenging because diverse conditions, such as electrode materials, operating conditions, and working environments, collectively determine complex capacity-degradation behaviors. However, current prediction methods are developed and validated under limited aging conditions, resulting in questionable adaptability to varied aging conditions and an inability to fully benefit from historical data collected under different conditions. Here we introduce a universal deep learning approach that is capable of accommodating various aging conditions and facilitating effective learning under low-resource conditions by leveraging data from rich conditions. Our key finding is that incorporating inter-cell feature differences, rather than solely considering single-cell characteristics, significantly increases the accuracy of battery lifetime prediction and its cross-condition robustness. Accordingly, we develop a holistic learning framework accommodating both single-cell and inter-cell modeling. A comprehensive benchmark is built for evaluation, encompassing 401 battery cells utilizing 5 prevalent electrode materials across 168 cycling conditions. We demonstrate remarkable capabilities in learning across diverse aging conditions, exclusively achieving 10% prediction error using the first 100 cycles, and in facilitating low-resource learning, almost halving the error of single-cell modeling in many cases. More broadly, by breaking the learning boundaries among different aging conditions, our approach could significantly accelerate the development and optimization of lithium-ion batteries.