Study on Lifetime Decline Prediction of Lithium-Ion Capacitors

Shuhui Cui, Saleem Riaz and Kai Wang ()
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Shuhui Cui: School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
Saleem Riaz: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Kai Wang: School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China

Energies, 2023, vol. 16, issue 22, 1-17

Abstract: With their high-energy density, high-power density, long life, and low self-discharge, lithium-ion capacitors are a novel form of electrochemical energy storage devices which are extensively utilized in electric vehicles, energy storage systems, and portable electronic gadgets. Li-ion capacitor aging mechanisms and life prediction techniques, however, continue to be active research areas. This paper examines the aging process for Li-ion batteries, covering the alterations in cell composition, the effect of the electrode charge state, temperature effects, and electrolyte deterioration. Additionally, this research offers approaches for predicting the lifespan of lithium-ion batteries, including those based on physical models, machine learning, and artificial intelligence. In this work, cycle life testing techniques are also discussed, including accelerated aging experiments for lithium-ion capacitors. The paper concludes by discussing future directions for the creation of aging mechanisms and lithium-ion capacitor life prediction techniques.

Keywords: lithium-ion capacitors; aging mechanism; life decline prediction; aging test (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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