Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium-Ion Batteries, Part-2: Data Collection Procedure
Prarthana Pillai, James Nguyen, Balakumar Balasingam
Abstract
Battery management systems (BMSs) rely on the prior characterization of the open circuit voltage (OCV) against the state of charge (SOC) for accurate SOC estimation in real-time. The OCV-SOC characterization is done offline in a laboratory setting using sample batteries. Various quantities defined for the OCV-SOC characterization process may determine the performance indicators of the battery, such as battery capacity, SOC and available power. Clearly defined OCV-SOC characterization will result in better performance and predictability of the BMS in electric vehicle applications. This paper is the second part of a series of papers about empirical approaches to open-circuit voltage (OCV) modeling and its performance comparison in lithium-ion batteries. The first part of the series introduced various sources of uncertainties in the OCV models and established a theoretical relationship between uncertainties and the performance of a battery management system. In this paper, clearly defined approaches for low-rate OCV data collection are described in detail. The data collection is designed with consideration to several parameters that affect the experimental time and the performance of the BMS. Based on the proposed approach, data is collected from 16 battery cells through 28 different experiments and analyzed. The results demonstrate the importance of a clearly defined data collection plan for the accuracy of SOC estimation in battery management systems.