A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability

Previous machine learning models for state-of-health (SOH) estimation of Li-ion batteries have relied on prescribed statistical features. However, there is little theoretical understanding of the relationships between these features and SOH degradation patterns of the batteries. This study proposes a convolutional neural network model to estimate the future SOH value of Li-ion bat-teries in the early phases of qualification tests. First, capacity degradation data are transformed into two-dimensional images using recurrence plots and Gramian angular fields, highlighting the time-series features of the data. Second, five types of convolutional neural network models are developed to estimate the SOH values of Li-ion batteries for a certain cycle. Here, class activation maps are generated to present how the models arrive at their conclusions. Finally, the perfor-mance and reliability of the developed models are assessed under various experimental condi-tions. The proposed approach has the following two advantages: it automatically extracts important temporal features from the capacity degradation data for SOH estimation, and obtains the contribution of each temporal feature with respect to the estimation process. The experi-mental results on 379Li-ion batteries confirm that the proposed approach can reduce the time required for qualification tests to 50 cycles, under a 6% mean absolute percentage error.

Automated summary

This study proposes a convolutional neural network model to estimate the future SOH value of Li-ion batteries using recurrence plots and Gramian angular fields. Five types of convolutional neural network models are developed and the contribution of each temporal feature is obtained. The experimental results confirm the proposed approach's effectiveness in reducing qualification test time and achieving accurate SOH estimation.