A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves

IF 5.4 3区材料科学 Q2 CHEMISTRY, PHYSICAL

ACS Applied Energy Materials Pub Date : 2024-06-14 DOI:10.3390/batteries10060206

Mano Schmitz, Julia Kowal

引用次数: 0

Abstract

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) during operation is crucial to ensure optimal performance, prolonging battery life and preventing unexpected failure or safety hazards. This work presents a storage- and performance-optimised deep learning approach to estimate the capacity-based SOH of LIBs using raw sensor data from partial charging curves under constant current condition. The proposed model is based on a combination of a one-dimensional convolutional and long short-term memory neural network, and processes time, voltage, and incremental capacity of partial charging curves as time series. The model is cross-validated on different ageing scenarios, reaching an overall MAE = 0.418% and RMSE = 0.531%, promising an accurate SOH estimation of LIBs under varying usage and environmental conditions in a real-world application.

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利用部分恒流充电曲线在线估计锂离子电池健康状况的深度学习方法

准确估计锂离子电池（LIB）在运行期间的健康状况（SOH）对于确保最佳性能、延长电池寿命、防止意外故障或安全隐患至关重要。本研究提出了一种存储和性能优化的深度学习方法，利用恒定电流条件下部分充电曲线的原始传感器数据来估计锂离子电池基于容量的 SOH。所提出的模型基于一维卷积神经网络和长短期记忆神经网络的组合，将部分充电曲线的时间、电压和增量容量作为时间序列进行处理。该模型在不同的老化情况下进行了交叉验证，总体 MAE = 0.418%，RMSE = 0.531%，有望在实际应用中准确估计锂电池在不同使用和环境条件下的 SOH。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Energy Materials Materials Science-Materials Chemistry

CiteScore

10.30

自引率

6.20%

发文量

1368

期刊介绍： ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.