Adaptable capacity estimation of lithium-ion battery based on short-duration random constant-current charging voltages and convolutional neural networks

• Published in: Energy (Oxford) Vol. 306; p. 132541
• Main Authors: Wang, Zuolu, Zhao, Xiaoyu, Zhen, Dong, Pombo, João, Yang, Wenxian, Gu, Fengshou, Ball, Andrew
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.10.2024
• Subjects: Capacity estimation; Convolutional neural network; Lithium-ion battery; Random and short-duration charging voltages; Lithium-ion battery; Random and short-duration charging voltages; Convolutional neural network; Capacity estimation
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2024.132541

The accurate lithium-ion battery capacity estimation is vital for ensuring the safe and reliable operation of battery-powered systems. Existing data-driven methods heavily rely on fixed charging stages for feature extractions, posing significant limitations in real-world applications. This paper proposes an adaptable capacity estimation approach utilising short-duration random charging voltages during the constant-current charging stage and leveraging convolutional neural networks (CNNs). Based on the user-friendly “Vstart−tend” strategy, two health features including charging voltage and its increment are firstly extracted from random charging segments. Secondly, a feature evolution pattern analysis over the battery's lifespan is proposed to divide the charging voltage range for robust model development. An optimal combination of both the sampling interval and data length is determined for the feature extraction. Then, a two-dimensional CNN model is developed to effectively learn ageing-related knowledge from various random charging segments in a specific charging voltage range. The effectiveness of the proposed approach is ultimately verified using two distinct types of batteries across three operational temperatures. The results demonstrate that the proposed approach show much potential as a promising capacity estimation technique utilising a 600 s random charging segment sampled at a 20 s interval. •Adaptable battery capacity estimation is achieved using random short-duration charging voltages.•Feature evolution analysis is proposed to guide feature extraction and model development.•2D-CNN model is developed for diverse random inputs and accurate capacity estimation.•The model's effectiveness is validated on two types of batteries at three working conditions.