A Flexible deep convolutional neural network coupled with progressive training framework for online capacity estimation of lithium-ion batteries. (15th April 2023)

• Record Type: Journal Article
• Authors: Xue, Qiao; Li, Junqiu; Xiao, Yansheng; Chai, Zhixiong; Liu, Ziming; Chen, Jianwen
• Is Part Of: Journal of cleaner production. Volume 397(2023)
• Publication Date: 2023-04-15
• Subjects: Lithium-ion battery -- Capacity estimation -- Charging voltage segment -- Deep convolutional neural network -- Progressive training framework; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2023.136575
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Machine learning-based methods have shown great application prospects on the capacity estimation of lithium-ion battery. However, most of extant approaches require complete charging and discharging profiles that rarely occurs in practical applications to extract health features matched by the cycling capacity. To address this limitation, this paper directly intercepted partial charging voltage segments from raw charging curves, evading complicated manual features extraction. A progressive training framework is proposed and integrated with the deep convolutional neural network to build the online capacity estimator. Three battery aging datasets involving 28 cells cyclic aging data are exploited for model training and validation. Thereinto, the source dataset and target dataset-1 are used to train the model parameters progressively, and the target dataset-2 is applied to validate model performance. Experimental results show that the deep convolutional neural network coupled with progressive training framework can significantly enhance the capacity estimation accuracy and reduce retraining time compared with the ordinary convolutional neural network. The comparison results of different machine learning algorithms demonstrate that the proposed method possesses high estimation precision with a maximum relative error of only 3.4% on the target dataset, which can be easily extended to different types of battery capacity estimation. Highlights: The configuration of DCNN model is meticulously devised for capacity estimation. Precise capacity estimation can be attained only using partial voltage segments. A progressive training framework is proposed to improve DCNN model performance. Capacity estimation does not require manual features extraction.