Recent Research on Battery Diagnostics, Prognostics, and Uncertainty Management

Xi, Zhimin; Jing, Rong; Lee, Cheol W.; Hayrapetyan, Mushegh

doi:10.1002/9781119060741.ch8

Cited by 3 publications

(1 citation statement)

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“…With extensive training data, the model can replicate the real-world operation conditions of the battery system with high accuracy of estimation. As a result, if the battery operation condition is beyond the limits of the training data set, the result in estimation would not be accurate [10]. Different methods bring different levels of accuracy with different levels of complexity.…”

Section: A Battery State Of Charge Estimationmentioning

confidence: 99%

A Review of Lithium-ion Batteries Diagnostics and Prognostics Challenges

Azizighalehsari

Popović

Venugopal

et al. 2021

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society

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Battery technology besides its importance and exceptional characteristics is not still a mature technology and there is a real need for research and innovation in their lifetime, charging rate, second use, etc. The dependency of our daily lives on batteries is irrefutable and they are becoming growingly ubiquitous in our daily lives. Battey performance is degrades with battery aging and therefore a battery diagnostics and prognostics tool to enhance the effective use of the battery system is necessary. This paper deals with some challenges that remain unsolved in battery diagnostic and prognostic techniques. A review of recent battery diagnostic approaches for battery state estimation is performed and their relative advantages and disadvantages are emphasized while comparing the available methods to predict the battery end of life (EOL) or remaining useful life (RUL) as a key tool in battery prognostics.

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Section: A Battery State Of Charge Estimationmentioning

confidence: 99%