A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery

• Published in: Energy (Oxford) Vol. 282; p. 128306
• Main Authors: Bao, Zhengyi, Nie, Jiahao, Lin, Huipin, Jiang, Jiahao, He, Zhiwei, Gao, Mingyu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2023
• Subjects: Lithium-ion battery; Neural network; State of health; Lithium-ion battery; State of health; Neural network
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2023.128306

Accurate estimation of the state of health (SOH) of lithium-ion batteries holds significant importance in guaranteeing the stable and secure functioning of electric vehicles. However, existing neural network-based methods suffer from limitations in capturing long-term serial relationships and extracting degenerate features. In light of these challenges, we propose a novel sequence-free framework for performing the SOH estimation task. Technically, a global–local context embedding module is proposed to learn both global- and local-range information context by two convolutional streams with different depths. With this module, a discriminatory feature learning can be guided. By integrating it into the convolution neural network, a novel time series prediction network, called improved convolution neural network (ICNN) is presented, which can effectively establish the mapping relationship between battery charging/discharging curves and battery SOH. Through rigorous experimentation on the CACLE dataset and NASA dataset, we demonstrate the efficacy of our proposed method, achieving mean absolute errors (MAEs) of 0.54% and 1.20% respectively. Our findings highlight the superiority of the proposed method compared to commonly used neural network methods in the domain of battery SOH estimation. •The historical data of the LIBs are input directly into the proposed framework.•A global–local context embedding module with different depth networks is proposed.•A novel network-based sequence-free framework, improved CNN (ICNN) is presented.•The proposed framework can learn both local and long-range features.