Real-time estimation of model parameters and state-of-charge of lithiumion batteries in electric vehicles using recursive least-square with forgetting factor

Abstract

A precise estimation of the lithium-ion battery’s inner state, such as the state of health (SoH) and the state of charge (SoC) of the battery, is crucial for a reliable and effective performance of a battery management system in an electric vehicle. In this paper, an improved real-time model-based battery parameters estimation method using the recursive least-square algorithm with forgetting factor (RLS-FF) is proposed. Compared to the traditional methods, the proposed model yields the capability to accurately estimate the battery SoC and SoH by including the real-time variation of open circuit voltage and internal resistance of a battery, respectively. Moreover, a forgetting factor is used to capture the online parameter variations by reducing the impact of the older data to keep the model simple and suitable for EV applications. To verify the validity of the proposed model, an experimental test is carried out on a 2012 Nissan Leaf 31.1 Ah Manganese-oxide Li-ion battery cell.