Recursive least square estimation approach to real-time parameter identification in li-ion batteries
Document Type
Conference Proceeding
Publication Date
10-1-2019
Publication Title
2019 IEEE Electrical Power and Energy Conference, EPEC 2019
Keywords
battery fuel gauge, battery impedance, Battery management system, equivalent circuit model, recursive least-squares algorithm, state of charge
Abstract
In this paper, we consider the problem of equivalent circuit model (ECM) parameter identification in Li-ion batteries. Accurate estimation of the ECM parameters is critical for the safety, efficiency and reliability of the battery system. Existing approaches to solve this problem depend on information and parameters, such as, battery capacity, state-of-charge (SOC) and open circuit voltage (OCV) characterization parameters. Such reliance on other parameters makes the ECM identification less accurate. In this paper, we present a real-time approach to ECM identification. The proposed approach relies only on the measured voltage across the battery terminal and current through the battery. Also, the proposed approach is unaffected by the amount of hysteresis in the battery. Further, robustness in parameter identification is achieved through the inclusion of the measurement noise covariance matrix. The proposed algorithm was tested on simulated as well as real world battery data and found to be accurate within 1% uncertainty.
DOI
10.1109/EPEC47565.2019.9074825
ISBN
9781728134062
Recommended Citation
Raihan, Sheikh Arif and Balasingam, Balakumar. (2019). Recursive least square estimation approach to real-time parameter identification in li-ion batteries. 2019 IEEE Electrical Power and Energy Conference, EPEC 2019.
https://scholar.uwindsor.ca/computersciencepub/114