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

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.

Automated summary

This paper proposes a novel sequence-free framework for estimating the state of health (SOH) of lithium-ion batteries. By introducing a global-local context embedding module, both global and local-range information can be learned to establish the mapping relationship between battery charging/discharging curves and battery SOH.