Real-time Thermal Runaway Prognosis of a Lithium-ion Battery via Physics-informed Latent Ensemble DeepONet with Segmented Data

Abstract

This study proposes a novel architecture of physics-informed latent ensemble deep operator network with segmented data. The proposed neural network aims to predict thermal runaway of a lithium-ion battery through prior temperature responses in real-time. The proposed neural network introduces three key features to provide an advanced control-enabling solution for battery thermal management systems (BTMS). First, the proposed neural network addresses the architecture of DeepONet as a surrogate model to effectively learn the internal temperature, chemical component concentration, and gas formation under supervision of complex and nonlinear multiphysics representing thermal runaway of lithium-ion batteries. This approach enables accurate and robust virtual sensing capability even with limited data by constraining the prognostic responses to follow the governing equation of the underlying multiphysics. Second, a dual-network architecture is introduced to extract valuable features from prior temperature responses, which inherently contain limited information in real-time scenarios. The network comprises two sub-networks; the first network extracts latent features from decomposed temporal domains across diverse local domains, and the second network ensemble these features to original features for mitigate concerns on overfitting and generalization. This approach ensures effective supervision by stiff governing equations in both local and global domains. Third, novel methods are employed to reduce the training complexity associated with integrating multiphysics equations including separate DeepONet, stan activation function, adaptive weights, and encoders. These methods enhance the expressiveness of temporal and spatial gradients that play an important role in physics-informed neural networks. Hence, this feature not only ensures convergence through a balanced learning but also improves the overall capability of the neural network. Extensive ablation studies validate the contribution of each feature, and thereby confirm the effectiveness of novel architecture and strategies in addressing failure issues in physics-informed neural networks. The proposed method enables real-time prognosis through prior thermal responses, offering a promising pathway toward artificial intelligence transformation in BMS to ensure the safety and efficiency of lithium-ion batteries.