State of health estimation of lithium-ion batteries based on a novel indirect health indicator

• DOI: 10.1016/j.egyr.2022.02.220
• Publication Date: 2022-03-08T21:53:40Z
• Authors: Da Lin; Xuesong Zhang; Lulu Wang; Bo Zhao
• Keywords: State of health

Accurately estimating the state of health (SOH) lithium-ion batteries is necessary to ensure battery system’s safe, stable, and efficient operation. It can be directly predicted by capacity, but latter difficult measure online. Therefore, this paper first extracts new indirect indicators from voltage current curves during charging optimizes them using Kalman filter. The Pearson correlation analysis method shows that extracted HIs have a good with capacity. On basis, Gaussian process regression (GPR) modified into multi-kernel (MKGPR) squared exponential covariance function periodic refine accurateness SOH estimation. hyper-parameters MKGPR model are solved employing particle swarm optimization (PSO) reduce errors caused artificial adjustment. Finally, data set provided NASA used evaluate given method, experimental findings reveal proposed approach has high accuracy stability.