Intelligent recognition of structural health state of EV lithium-ion Battery using transfer learning based on X-ray computed tomography

Abstract

The lithium-ion battery (LIB) achieves wide applications on electric vehicles (EVs). The morphology of LIB electrode is highly correlated to the performance and safety of EV. Therefore, it is significant to monitor the structural health state of LIB to guarantee a reliable operation of EV. Currently, few research focused on the intelligent recognition of LIB structural health state. To bridge the gap, this paper proposes an image-based method to conduct structural health state recognition of LIB. The geometric deformation of LIB electrode is recorded by X-ray computed tomography (CT) images under different structure health state. Then a novel transfer learning network is incorporated to enhance the generalization ability of model. In the network, prior knowledge is first acquired from source domain data to initialize a convolutional autoencoder (CAE). Then the CAE is fine-tuned on CT images (target domain data), and a structural deviation index is established to reflect the structure health condition. Once the index exceeds the pre-set threshold, the alarm is triggered. The performance of the proposed method was evaluated on experimental data, and the results proved that the proposed method can perform structural health state monitoring effectively, and transfer learning can improve the generalization ability of detection model significantly.