Wu Jie, Sun Zhongxian, Li Dingquan, He Weilin, Yang Dongchen, Wu Zhenguo, Geng Xin, Yang Hui, Wang Hailong, Hu Linyu, Tu Haiyan, He Xin
College of Electrical Engineering, Sichuan University, Chengdu, China.
Pengcheng Laboratory, Shenzhen, China.
Commun Eng. 2025 Aug 12;4(1):151. doi: 10.1038/s44172-025-00488-1.
Growing energy storage demand has solidified the dominance of lithium-ion batteries (LIBs) in modern societies but intensifies recycling pressures. Precise state-of-health (SOH) assessment is crucial to grouping retired batteries from an unknown state for secondary utilization. However, batteries in the pack exhibit distinct capacity fading behaviors due to their service scenarios and working conditions. We develop a deep-learning framework for rapid, transferable SOH estimation and battery classification. This framework integrates deep neural networks with interconnected electrochemical, mechanical, and thermal features. Our model delivers optimal accuracy with a mean absolute error (MAE) of 0.822% and a root mean square error (RMSE) of 1.048% using combined features. It demonstrates robust performance across various conditions and enables SOH prediction with data from merely one previous cycle. Moreover, the well-trained model could adapt to other electrode systems with a minimal number of additional samples. This work highlights critical features for SOH estimation and enables efficient battery classification toward sustainable recycling.
不断增长的储能需求巩固了锂离子电池（LIBs）在现代社会中的主导地位，但也加剧了回收压力。精确的健康状态（SOH）评估对于将未知状态的退役电池分组以进行二次利用至关重要。然而，电池组中的电池由于其使用场景和工作条件而表现出不同的容量衰减行为。我们开发了一个用于快速、可转移的SOH估计和电池分类的深度学习框架。该框架将深度神经网络与相互关联的电化学、机械和热特征相结合。我们的模型使用组合特征时，平均绝对误差（MAE）为0.822%，均方根误差（RMSE）为1.048%，实现了最佳精度。它在各种条件下都表现出强大的性能，并且仅使用前一个周期的数据就能进行SOH预测。此外，经过良好训练的模型可以通过最少数量的额外样本适应其他电极系统。这项工作突出了SOH估计的关键特征，并实现了高效的电池分类，以促进可持续回收利用。
