Improving SOH estimation for lithium-ion batteries using TimeGAN

IF 6.3 2区物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Machine Learning Science and Technology Pub Date : 2023-10-12 DOI:10.1088/2632-2153/acfd08

Sujin Seol, Jungeun Lee, Jaewoo Yoon, Byeongwoo Kim

引用次数: 0

Abstract

Abstract Recently, the xEV market has been expanding by strengthening regulations on fossil fuel vehicles. It is essential to ensure the safety and reliability of batteries, one of the core components of xEVs. Furthermore, estimating the battery’s state of health (SOH) is critical. There are model-based and data-based methods for SOH estimation. Model-based methods have limitations in linearly modeling the nonlinear internal state changes of batteries. In data-based methods, high-quality datasets containing large quantities of data are crucial. Since obtaining battery datasets through measurement is difficult, this paper supplements insufficient battery datasets using time-series generative adversarial network and compares the improvement rate in SOH estimation accuracy through long short-term memory and gated recurrent unit based on recurrent neural networks. According to the results, the average root mean square error of battery SOH estimation improved by approximately 25%, and the learning stability improved by approximately 40%.

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利用TimeGAN改进锂离子电池SOH估计

近年来，随着对化石燃料汽车的监管力度加大，电动汽车市场不断扩大。电池是xev的核心部件之一，确保电池的安全性和可靠性至关重要。此外，估计电池的健康状态(SOH)至关重要。SOH估计有基于模型的方法和基于数据的方法。基于模型的方法在对电池非线性内部状态变化进行线性建模时存在局限性。在基于数据的方法中，包含大量数据的高质量数据集至关重要。由于通过测量获取电池数据集较为困难，本文采用时间序列生成对抗网络对不足的电池数据集进行了补充，并比较了长短期记忆和基于递归神经网络的门控循环单元对SOH估计精度的提高率。结果表明，电池SOH估计的平均均方根误差提高了约25%，学习稳定性提高了约40%。

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

Machine Learning Science and Technology Computer Science-Artificial Intelligence

CiteScore

9.10

自引率

4.40%

发文量

审稿时长

5 weeks

期刊介绍： Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.