Hybrid gray wolf optimization method in support vector regression framework for highly precise prediction of remaining useful life of lithium-ion batteries-Reference-Cited by-同舟云学术

Hybrid gray wolf optimization method in support vector regression framework for highly precise prediction of remaining useful life of lithium-ion batteries

Published:2023-06-06 Issue:9 Volume:29 Page:3597-3607
ISSN:0947-7047
Container-title:Ionics
language:en
Short-container-title:Ionics

Author:

Zhang Mengyun,Wang Shunli,Xie Yanxin,Yang Xiao,Hao Xueyi,Fernandez Carlos

Funder

National Natural Science Foundation of China projects

Scientific Research Project of Sichuan Provincial Department of Education

Natural Science Foundation of Sichuan Province

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Engineering,General Materials Science,General Chemical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11581-023-05072-1.pdf

Reference43 articles.

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2. Wang S et al (2020) A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm. J Power Sources 471:1–13

3. Lai X et al (2022) Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects. Energy 238:1–14

4. Zhang MY et al (2022) A novel square root adaptive unscented Kalman filter combined with variable forgetting factor recursive least square method for accurate state-of-charge estimation of lithium-ion batteries. Int J Electrochem Sci 17(9):1–15

5. Wang S et al (2020) A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions. J Power Sources 450:1–14

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