Remaining Useful Life Prediction of Lithium-ion Batteries Based on Particle Filter with Generalized Exponential Distribution

Xie, Xuan; Bi, Dongjie; Xie, Yongle; Tang, Shenjie; Peng, Libiao

doi:10.1109/icites56274.2022.9943657

Cited by 3 publications

(1 citation statement)

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“…Al-Mohamad et al, on the other hand, used a Zonotopic Extended Kalman Filter to determine the RUL of systems that do not necessarily have historical records of their degradation trend [184]. Similarly, Xie et al proposed a method that uses a particle filter (PF) with the generalized exponential distribution [210]. VOLUME 11, 2023…”

Section: ) State Space Modelingmentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

et al. 2023

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Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase a system's lifespan, reliability, and availability. Two main data-driven approaches are used in the literature to determine the Remaining Useful Life (RUL): direct calculation from raw data and indirect analysis by revealing the transitions from one latent state to another and highlighting degradation in a system's Health Indices. The present study discusses the state-of-theart data-driven methods introduced for RUL prediction in predictive maintenance, by looking at their capabilities, scalability, performance, and weaknesses. We will also discuss the challenges faced with the current approaches and the future directions to tackle the current limitations.

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Section: ) State Space Modelingmentioning

confidence: 99%