Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries. (15th July 2021)

• Record Type: Journal Article
• Authors: Qian, Cheng; Xu, Binghui; Chang, Liang; Sun, Bo; Feng, Qiang; Yang, Dezhen; Ren, Yi; Wang, Zili
• Is Part Of: Energy. Volume 227(2021)
• Publication Date: 2021-07-15
• Subjects: Lithium-ion battery -- Capacity estimation -- One-dimensional convolutional neural network -- Random segment; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.120333
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Capacity estimation is an essential task for battery manage systems to ensure the safety and reliability of lithium-ion batteries. Considering the uncertainty of charging and discharging behavior in practical usage, this paper presents a one-dimensional convolution neural network (1D CNN)-based method that takes random segments of charging curves as inputs to perform capacity estimation for lithium-ion batteries. To improve the robustness and accuracy of the proposed 1D CNN network, a linear decreasing weighted particle swarm optimization algorithm is utilized to optimize the partial hyperparameters of neural network. Experimental data from two sets of batteries with different nominal capacities are employed for verification purpose. It is proved that the proposed method is feasible to provide accurate estimations on capacity degradation for both kinds of batteries. Furthermore, effects of length and relative position of segments on the capacity estimation are also investigated. The analysis results show that a more precise estimation of the battery capacity is prone to be obtained from the segment with a longer length or lower initial SOC. Highlights: A 1D CNN based battery capacity estimation model is developed for Li-ion batteries. Capacity of the battery can be estimated from random segmented charging curves. LDWPSO is utilized to optimize key hyperparameters of the 1D CNN model. Segments with long lengths/low initial SOCs are preferred for the 1D CNN model.