Real-Time State of Charge-Open Circuit Voltage Curve Construction for Battery State of Charge Estimation

All state of charge (SoC) estimation algorithms based on equivalent circuit models (ECMs) estimate the open circuit voltage (OCV) and convert it to the SoC using the SoC-OCV nonlinear relation. These algorithms require the identification of ECM parameters and the nonlinear SoC-OCV relation. In literature, various techniques are proposed to simultaneously identify the ECM parameters. However, the simultaneous identification of the SoC-OCV relation remains challenging. This paper presents a novel technique to construct the SoC-OCV relation, which is eventually converted to a single parameter estimation problem. The Kalman filter is implemented to estimate the SoC and the related states in batteries using the proposed parameter estimation and the SoC-OCV construction technique. In the numerical simulations, the algorithm demonstrates that it accurately estimates the battery model parameters, and the SoC estimation error remains below 2%. We also validate the proposed algorithm with a battery experiment. The experimental results show that the error in SoC estimation remains within 2.5%.

Automated summary

This paper presents a novel technique for constructing the SoC-OCV relation in batteries and converting it into a single parameter estimation problem. The Kalman filter is implemented to estimate the SoC and related states in batteries using the proposed parameter estimation and SoC-OCV construction technique. Numerical simulations demonstrate accurate parameter estimation and SoC estimation error below 2%. Experimental results validate the algorithm with an SoC estimation error remaining within 2.5%.