Synchronous state of health estimation and remaining useful lifetime prediction of Li-Ion battery through optimized relevance vector machine framework

Zhiqiang Lyu, Geng Wang and Renjing Gao

Energy, 2022, vol. 251, issue C

Abstract: This study proposes a hybrid kernel function relevance vector machine (HKRVM) optimized model for battery prognostics and health management. To monitor battery state of health (SOH), two ageing features (AFs) are extracted from the incremental capacity curve to quantify capacity degradation. To further predict remaining useful life (RUL), the AFs are treated with the BOXCOX transformation to enhance the linearity between AFs and cycles. Then, a metabolic extreme learning machine is developed to successionally predict the degradation trends of AFs quickly and accurately. The HKRVM is proposed to capture the underlying relationship between AFs and capacity. To determine the optimal weights and kernel parameters in HKRVM, the biological evolution in the genetic algorithm (GA) is integrated into the grey wolf optimizer (GWO) to further improve the population diversity and optimization performance of the basic GWO.

Keywords: Battery pack; State of health (SOH); Remaining useful life (RUL); Hybrid kernel function relevance vector machine; BOXCOX transformation; Genetic grey wolf optimizer (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007551

DOI: 10.1016/j.energy.2022.123852

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