Lithium Battery Thermal-Runaway Monitoring Based on Whole-Feature Neural Networks

Abstract

Thermal runaway monitoring and analysis has become a serious challenge to the safety of lithium-ion battery driven electric equipment. Thermal-runaway monitoring is crucial to avoid the burning and explosion of lithium batteries. This paper proposes a new type of deep neural network, known as whole-feature neural networks (WFNN), for lithium battery thermal-runaway monitoring. The neural networks learn the thermal-runaway patterns of a lithium battery from the measured temperatures, current, and voltages. WFNN is an end-to-end model for thermal-runaway monitoring of lithium batteries. An experiment on thermal-runaway monitoring of lithium batteries was carried out to evaluate the performance of the proposed WFNN. The monitoring accuracy is up to 99.48%, which is higher than those of support vector machine, kernel support vector machine, k-nearest neighbor, and fully-connected neural networks. Moreover, the computation efficiency of WFNN is high enough for real-time thermal-runaway monitoring. As a result, experimental results show that the proposed WFNN is applicable to the thermal-runaway monitoring of lithium batteries.