Data-Driven Prognosis of Multiscale and Multiphysics Complex System Anomalies: Its Application to Lithium-ion Batteries Failure Detection

Abstract

Energy systems are integral to most complex systems, and their reliability is often essential to the reliability of those systems. Therefore, monitoring the systems' health is critical to warn of any potential failures and anomalies. Meanwhile, it is pressing to solve the dilemma of balancing experimental complexity and modeling computational cost. The above challenge eventually necessitates predictive or prognostic capability for prognostics and health monitoring (PHM) of complex systems under complexly aggressive working environments. In this perspective, we review and summarize PHM predictive modeling and prognostications of the remaining useful life, anomalies, and failure detection in Lithium-ion Batteries.

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

“…Ali et al presented a novel long short-term memory (LSTM) network that achieves accurate SOC estimation by using a time step internal attention mechanism and position encoding, thereby obtaining the optimal root mean square error with an average absolute error of 0.68% and 0.91%, respectively [62]. Liu et al propose a unique data-driven prediction method (DDP) for properly modeling battery aging and capacity across several scales and physical fields, capturing dynamic deviations based on in situ data measurement, and monitoring battery SOC and health [63][64][65].…”