A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system. (15th February 2022)

• Record Type: Journal Article
• Authors: Zhang, Qisong; Yang, Lin; Guo, Wenchao; Qiang, Jiaxi; Peng, Cheng; Li, Qinyi; Deng, Zhongwei
• Is Part Of: Energy. Volume 241(2022)
• Publication Date: 2022-02-15
• Subjects: Lithium-ion battery -- Cloud computing system -- Remaining useful life prediction -- Residual convolutional neural network -- Sparse segment data; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.122716
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Accurate prediction of the battery remaining useful life (RUL) at different operating conditions is critical for the battery management system to guarantee safe and efficient operation. However, because of the complicated degradation mechanisms inside the battery, it is extremely challenging to predict the battery life by measuring the external variables. Due to the sparse and random segment data in practical applications, the existing methods are difficult to be applied for online prediction. In this paper, a hybrid parallel residual convolutional neural networks (HPR CNN) model for RUL prediction is proposed. By fusing the charging data of voltage, current and temperature curves in multiple cycles, the hidden feature information of different depths is effectively extracted through the residual network. Based on the sparse data corresponding to only 20% charging capacity, combined with a cloud computing system, this method is able to achieve online prediction in various practical applications. By calculating the difference between each cycle as supplementary input data, the method is able to predict the RUL of a battery with high accuracy and reliability. Validated by a public data set and compared with other methods, the proposed method achieves a low test error of 4.15%, which is promising to be applied in the conditions of random charging process. Highlights: A method of online RUL prediction via a cloud computing system is presented. The RUL is described by sparse segment data of battery V, I and T during charging. A HPR CNN model is developed to extract and learn degradation features. The model comprehensive performance is verified by comparing with different methods. The prediction error of proposed method can be as low as 4.15% and 16.09 cycles.