Joint State‐of‐Charge and State‐of‐Health Estimation for Lithium‐Ion Batteries Based on Improved Lebesgue Sampling and Division of Aging Stage

Abstract

Accurate estimation of state‐of‐charge (SOC) and state‐of‐health (SOH) is an important guarantee for the safe and stable operation of lithium‐ion batteries (LIBs). To reduce the computational cost of the battery management system, a joint SOC and SOH estimation method is proposed that achieves a balanced allocation of computational resources and prediction accuracy. It is based on a combined circuit model and data‐driven method, which uses improved Lebesgue sampling for the capacity update of SOC estimation. It updates capacity reduction calculation when the capacity decays slowly, and can reduce the estimation error without increasing the number of sampling calculations. To improve the SOH estimation accuracy, two types of relevant health factors (HFs) are selected according to the aging mechanism of the battery. The battery aging stage is divided according to the HFs, and the SOH estimation method is proposed according to the battery aging stage. Experimental validation of the method is carried out by means of the aging dataset of LIBs from Oxford University batteries. The results show that the proposed joint estimation of SOH and SOC method has high accuracy and less computational cost in the whole lifecycle of LIBs.