Tabular Open Circuit Voltage Modelling of Li-Ion Batteries for Robust SOC Estimation

Abstract

Battery management systems depend on open circuit voltage (OCV) characterization for state of charge (SOC) estimation in real time. The traditional approach to OCV-SOC characterization involves collecting OCV-SOC data from sample battery cells and then fitting a polynomial model to this data. The parameters of these polynomial models are known as the OCV-parameters, or OCV-SOC parameters, in battery management systems and are used for real-time SOC estimation. Even though traditional OCV-SOC characterization approaches are able to abstract the OCV-SOC behavior of a battery in a few parameters, these parameters are only applicable in high precision computing systems. However, many practical battery applications do not have access to high-precision computing resources. The typical approach in a low-precision system is to round the OCV-parameters. This paper highlights the perils of rounding the OCV parameters and proposes an alternative OCV-SOC table. First, several existing OCV-SOC parameters are compared in terms of their expected system requirements and accuracy losses due to rounding. Then, a systematic optimization-based approach is introduced to create an OCV-SOC table that is robust to rounding. A formal performance evaluation metric is introduced to measure the robustness of the resulting OCV-SOC table. It is shown that the OCV-SOC table obtained through the proposed optimization approach outperforms the traditional parametric OCV-SOC models with respect to rounding.

Keywords

• battery management system
• OCV-SOC characterization
• state of charge estimation
• polynomial fitting
• hardware implementation of algorithms
• memory constraints