An Ultrafast Variable Forgetting Factor Recursive Least Square Method for Determining the State-of-Health of Li-Ion Batteries

Abstract

Fast and accurate detections of state-of-health (SoH) are urgently required by various industrial sectors to facilitate reuse and recycling of Li-ion batteries. However, existing SoH identification methods rarely reconcile the monitoring speed and accuracy, which results in redundant carbon footprints and electronic wastes. To address this critical issue, an ultrafast and accurate SoH monitoring is proposed in this paper. The proposed variable forgetting factor Recursive Least Squares (VFFRLS) method is designed based on a least square method with forgetting factors to determine the electrical parameters of a simple electrical model. The established electrical model can be easily implemented using cost-effective micro-controller units (MCUs). The weighting factors of the objective function is automatically determined based on the Genetic Algorithm (GA). Experimental results validate the superior performance of the proposed strategy over conventional methods in detecting the SoH of one new and three aged batteries. For the new battery, the relative error of the estimated SoH is almost 0%. For the aged batteries, the relative error of the estimated SoH is less than 2.78%. State-of-health (SoH), Li-ion battery, electrical model, variable forgetting factor Recursive Least Squares (VFFRLS) method, Genetic Algorithm (GA). INDEX TERMS