Data-Driven Prognosis of the Failure of Lithium-Ion Batteries

Abstract

Although the electric vehicle market is witnessing an unprecedented evolution, the fast adoption of these vehicles requires a more thorough status analysis of the battery performance's functionality and reliability. Due to their rechargeable nature, Lithium-ion batteries (LIBs) operation is subject to different irreversible processes during their charging and discharging cycles and causing capacity fade due to various degradation mechanisms. These processes generally result in battery capacity degradation, which usually results in battery failure, with consequences ranging from loss of operation, reduced capability, downtime, and catastrophic malfunctions. To address the issues mentioned above, numerous studies have been dedicated to proposing proper degradation model mechanisms for improving the reliability and availability of LIBs. However, due to accuracy and computational complexity challenges, most existing remaining useful life (RUL) and health prediction models focus on special degradation effects and ignore the integrated deterioration mechanisms, which generally involve batteries' capacity fade associated with the inadequacy of current health estimation tools. Thus, these shortcomings ultimately echo the need to devise novel tools to identify the dominant criteria negatively affecting battery performance and accurately predict the system's failure. The above challenges eventually necessitate a robust and reliable predictive or prognostic capability for prognostics and health monitoring (PHM) under a complexly hostile working environment. In this context, this investigation aims at proposing a novel data-driven approach called data-driven prognosis (DDP) that estimates the relevant constitutive parameters in situ and captures deviations from the expected degradation dynamics of the LIBs in addition to precise modeling of the degradation and capacity models. This talk will present a new data-driven approach using statistical pattern recognition and machine learning tools to detect batteries' anomalies and failures.

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].…”

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

“…105 This method has been applied for the failure prediction of LIBs. 106,107 This proposal will break away from existing practices. Instead, the DDP assumes a conservation principle but does not require prior knowledge of the key mechanisms and boundary conditions.…”

“…Then, using a set of threshold criteria, the DDP estimates the possible time or step when the instability stage occurs and predicts the anomalies and failures occurred in the system. 106,107 The DDP prediction algorithm developed for the LIBs' anomalies and failure detection is briefly explained as follows: I. Work conjugate variables (voltage and current) are measured and normalized at each time instant.…”