Physics-Dominated Neural Network for Spatiotemporal Modeling of Battery Thermal Process

Abstract

Modeling the temperature distribution of a battery is critical to its safe operation. Data-based modeling methods are computationally efficient, but require a large number of sensors; while physics-based modeling methods have better generalization, but the unknown dynamics of the actual scene are ignored. A physics-dominated neural network is presented to integrate electric-thermal mechanism of the battery and data information through a weight adaptive function. The electric-thermal coupling equation of the battery under complex conditions is taken as the prior knowledge to update parameters of the network; while the characteristic data obtained by the unique sensor is used to compensate the unknown disturbance in the actual scene. A well-trained model can predict the temperature distribution of the battery over entire space with a single sensor, and can also provide reasonable predictions for longer periods of time under extreme conditions. Experiments show that the proposed method outperforms traditional methods that rely only on pure data or pure physics.