Timely prediction and alert systems for identifying potential battery failure due to mechanical abuse are of utmost importance. The ongoing progress in machine learning (ML) algorithms and the evolution of extensive cloud-based models offer viable solutions for predicting and issuing early warnings for battery failure.
The utilization of multi-source signals, in conjunction with cloud-based large-scale models, has the potential to offer effective strategies for the early warning of battery failure. In this work, a cloud-based framework for battery failure prediction and early warning is presented.
Huang et al. experimentally developed a predictive model for early detection of battery failure, integrating factors such as exhaust gas dispersion and thermal runaway.
It introduces a cloud-based framework designed for the prediction and early detection of battery failure. The framework comprises three components, with the first being a model for recognizing failure modes resulting from mechanical abuse of batteries.
The final failure prediction of the batteries takes all the above analysis into account in order to make a prognostication about the system as to when is the most probable time that it fails. The results are shown for 48D and 54D batteries in Fig. 7, Fig. 8.
Our findings highlight the need for cloud-based artificial intelligence technology tailored to robustly and accurately predict battery failure in real-world applications. Data-driven prediction of automotive battery failure (A) Data generation and model training.