Datong Liu a,*, Jingyue Pang a, Jianbao Zhou a, Yu Peng a, Michael Pecht b
a Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
b Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20740, USA
State of health (SOH) estimation plays a significant role in battery prognostics. It is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. At present, many algorithms have been applied to perform prognostics for SOH estimation, especially data-driven prognostics algorithms supporting uncertainty representation and management. To describe the uncertainty in evaluation and prediction, we used the Gaussian Process Regression (GPR), a data-driven approach, to perform SOH prediction with mean and variance values as the uncertainty representation of SOH. Then, in order to realize multiple-step-ahead prognostics, we utilized an improved GPR method—combination Gaussian Process Functional Regression (GPFR)—to capture the actual trend of SOH, including global capacity degradation and local regeneration. Experimental results confirm that the proposed method can be effectively applied to lithium-ion battery monitoring and prognostics by quantitative comparison with the other GPR and GPFR models.
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