Park Seojoung, Lee Hyunjun, Scott-Nevros Zoe K, Lim Dongjun, Seo Dong-Hwa, Choi Yunseok, Lim Hankwon, Kim Donghyuk
School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, 44919, Republic of Korea.
Mater Horiz. 2023 Apr 3;10(4):1274-1281. doi: 10.1039/d3mh00013c.
Accurately estimating the state-of-health (SOH) of lithium-ion batteries is emerging as a hot topic because of the rapid increase in electric appliance usage. However, versatile applicability to various battery compositions and diverse cycling conditions, and prediction only with partial data still remain challenges. In this paper, a Deep-learning-based Graphical approach to Estimation of Lithium-ion batteries SOH (D-GELS) was developed to predict the SOH covering three cathode materials, LiFePO, LiNiCoAlO, and LiNiCOMnO. D-GELS shows an accurate performance for SOH prediction, less than 0.012 of RMSE, was predicted regardless of cathode materials, and its applicability was confirmed. Furthermore, D-GELS was capable of predicting the SOH using partially-cycled data, since less than 0.046 of RMSE was observed even with 50% of the image missing. When using partially-cycled profiles, significant economic benefits can be seen in used battery management, as the number of assessed batteries increases greatly, leading to cost savings.
由于电器使用量的迅速增加，准确估计锂离子电池的健康状态（SOH）正成为一个热门话题。然而，对于各种电池组成和不同循环条件的广泛适用性，以及仅用部分数据进行预测仍然是挑战。在本文中，开发了一种基于深度学习的锂离子电池SOH估计图形方法（D-GELS），以预测涵盖三种阴极材料LiFePO、LiNiCoAlO和LiNiCOMnO的SOH。D-GELS在SOH预测方面表现出准确的性能，无论阴极材料如何，预测的均方根误差（RMSE）小于0.012，并且其适用性得到了证实。此外，D-GELS能够使用部分循环数据预测SOH，因为即使有50%的图像缺失，观察到的RMSE也小于0.046。当使用部分循环曲线时，在废旧电池管理中可以看到显著的经济效益，因为评估电池的数量大幅增加，从而节省了成本。
