Online state of charge and capacity dual estimation with a multi-timescale estimator for lithium-ion battery

Abstract

Reliable online estimation of state of charge (SOC) and capacity is critically important for the battery management system. This paper presents a multi-timescale estimator to dually estimate the SOC and capacity for lithium-ion battery. The first-order RC model is used to simulate the dynamics of lithium-ion battery. Based on the battery model, the open circuit voltage (OCV) is timely updated with a simple OCV, the result of which is further corrected with the Kalman filter (KF). Then the SOC is inferred from the SOC-OCV look-up table. Meanwhile, a RLS-based capacity estimator is formulated to work simultaneously with the SOC estimation in the form of dual estimation. Different timescales are adopted for the dual estimator to improve accuracy and stability. Experimental results suggest that the proposed method estimates SOC and capacity in real time with fast convergence and high precision.