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A Data Compensation Model for Predicting SOH and RUL of Lithium–Ion Battery
Feng Hai-Lin,Xu An-Ke 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.1
Accurately estimating the state of health and remaining useful life of lithium–ion batteries is particularly critical to ensure safety and reliability. In this paper, ES-EDM-DCM, a data compensation model which fusing a new empirical model ES-EDM and a feature-driven model is established. The ES-EDM describes the degradation trajectory of li–ion batteries better. The feature driven model is established based on support vector regression which driven by a single health feature extracted and optimized from partial battery data. Finally, two datasets are used to verify that the prediction error of the proposed model is reduced by 0.1–3.68% compared with two single models in this paper. And compared with other literature methods, the prediction accuracy of the proposed model is improved by 0.9–52.98%. The proposed model has lower computational cost and higher robustness, and the modeling based on partial degradation data is more convenient for practical.