A Comprehensive Analysis of the Open-Circuit Voltage (OCV) Method for the Estimation of State of Charge

With increasing popularity of the Electric Vehicles (EVs), the need for a reliable and proficient Battery Management System (BMS) has taken a center stage in order to ensure optimal battery performance and safety. The most important function of a BMS is to quickly and reliably estimate the State of Charge (SoC), which is an indication of the remaining energy. Due to its straightforwardness and cost-efficiency, the Open Circuit Voltage (OCV) method is, by far, one of the most widely used methods to estimate the SoC. This paper focuses on a detailed analysis of the OCV method, explaining as to how it works, and its advantages and limitations. This paper relates the voltage to the SoC of a battery under different conditions. This paper aims to examine the various factors that affect the accuracy of SoC estimation, such as temperature and aging. It provides novel insights into the impact of battery aging and temperature variations upon the OCV-SoC relationship, which is vital for the accurate estimation of the SoC in EVs. By carrying out the analysis of the lithium-ion batteries under varying temperatures and aging conditions, this work identifies the crucial shifts in the OCV-SoC curve and also proposes an adaptive framework for integration of BMS which can potentially alleviate these effects. The findings of this paper underline the potential adjustments to be made in BMS algorithms in order to accurately estimate the SoC, which in turn can address both computational feasibility and practical deployment challenges, thereby contributing to more reliable performance of the EV. The detailed analysis of the OCV method of SoC calculation is carried out using MATLAB. The results obtained, therewith, are discussed in detail. The detailed analysis aims to provide important insights to enhance the overall performance of EV batteries.