A Hybrid Data-Driven Approach for Multistep Ahead Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries.

Autor: Ali MU; Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea., Zafar A; Department of Electrical Engineering, The Ibadat International University, Islamabad 54590, Pakistan., Masood H; Department of Electrical Engineering, University of Wah, Wah Cantt, Pakistan., Kallu KD; School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), H-12, Islamabad, Pakistan., Khan MA; Department of Computer Science, HITEC University, Taxila, Pakistan., Tariq U; College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia., Kim YJ; Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea., Chang B; Center for Computational Social Science, Hanyang University, Seoul 04763, Republic of Korea.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jun 13; Vol. 2022, pp. 1575303. Date of Electronic Publication: 2022 Jun 13 (Print Publication: 2022).
DOI: 10.1155/2022/1575303
Abstrakt: In this paper, a novel multistep ahead predictor based upon a fusion of kernel recursive least square (KRLS) and Gaussian process regression (GPR) is proposed for the accurate prediction of the state of health (SoH) and remaining useful life (RUL) of lithium-ion batteries. The empirical mode decomposition is utilized to divide the battery capacity into local regeneration (intrinsic mode functions) and global degradation (residual). The KRLS and GPR submodels are employed to track the residual and intrinsic mode functions. For RUL, the KRLS predicted residual signal is utilized. The online available experimental battery aging data are used for the evaluation of the proposed model. The comparison analysis with other methodologies (i.e., GPR, KRLS, empirical mode decomposition with GPR, and empirical mode decomposition with KRLS) reveals the distinctiveness and superiority of the proposed approach. For 1-step ahead prediction, the proposed method tracks the trajectory with the root mean square error (RMSE) of 0.2299, and the increase of only 0.2243 RMSE is noted for 30-step ahead prediction. The RUL prediction using residual signal shows an increase of 3 to 5% in accuracy. This proposed methodology is a prospective approach for an efficient battery health prognostic.
Competing Interests: The authors declare no conflicts of interest.
(Copyright © 2022 Muhammad Umair Ali et al.)
Databáze: MEDLINE
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