State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression

Haiyan Jin, Ningmin Cui, Lei Cai, Jinhao Meng, Junxin Li, Jichang Peng and Xinchao Zhao

Energy, 2023, vol. 262, issue PB

Abstract: State-of-Health (SOH) estimation is crucial for the safety and reliability of battery-based applications. Data-driven methods have shown their promising potential in battery SOH estimation, yet creating a high-performance model with a compact structure is still a grand challenge. This paper focuses on constructing the elastic feature to formulate auto-configurable Gaussian Process Regression (GPR) to address this issue. To eliminate the impacts of the kernels on GPR, an evolutionary framework is designed to organize the kernel configuration. Meanwhile, a hierarchical feature construction strategy reduces the complexity of the extracted feature according to the geometry of the charging curve. Experiments on three battery datasets demonstrate the effectiveness of the proposed method, demonstrating the practical value of the proposed method for the battery management system (BMS) to construct feature more feasible, and to provide the optimal kernel configuration automatically.

Keywords: State of health; Gaussian process regression; Evolutionary framework; Lithium-ion batteries; Kernel function (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222023854

DOI: 10.1016/j.energy.2022.125503

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