A Neural Network Based Method for Thermal Fault Detection in Lithium-Ion Batteries

Detecting thermal faults is critical to the safety of lithium-ion batteries. This article, therefore, proposes a neural network-based approach. The approach relies on the long short-term memory neural network, in conjunction with an alteration to the walk-forward technique, to accurately estimate the surface temperature of the cell. It also relies on a residual monitor to detect the faults in real time. This data-driven method is introduced to expand the available options in thermal fault detection. It offers an easy-to-implement option that does not require expert understanding in battery physics, complex mathematical modeling, and tedious parameter tuning processes. The experimental results demonstrate that this approach can detect thermal faults accurately. It is adaptive to different battery chemistries and form factors, and thanks to its online training capability, it can also automatically retrain itself to capture changes in the battery over time.

Automated summary

This article introduces a neural network-based approach for detecting thermal faults in lithium-ion batteries. It relies on the long short-term memory neural network and a residual monitor to accurately estimate temperature and detect faults in real time. This data-driven method is easy to implement and can adapt automatically to changes in the battery.