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After decades, the battery usage has been widespread for many applications, especially in the field of Electric Vehicle (EV). The battery is a very important component in the EV. Because the battery as the primary power source replacement of the fossil fuel. Therefore, the condition of the batteries should be always in good condition. To prevent failure of the battery for battery management system (BMS) is needed. BMS is a system to regulate the use of the battery and protects the battery from the failure of the battery supply. Many factors can be monitored at BMS, one of which is a State of Charge (SOC). SOC determination is directly related to the estimated OCV (Open Circuit Voltage). The accuracy of the estimation algorithms depend on the accuracy of the model selection to describe the dynamic characteristics of the battery. This study begins with the selection of the right model (fig.1, fig.2, fig.3) for estimating OCV. Selection of appropriate model using RLS algorithm for estimate the battery terminal voltage. Parameter that reference for determining the selection of the model is the max, min, mean, RMSE, mean RMSE of the error. Later models have been used to estimate the OCV. The result based on this research shows that modeling with n = 1 is the best result to be used in model parameter estimation and OCV battery in term of the smaller max, min, mean, rmse error. This research also show us that RLS algorithm can be estimate the parameters of the batery, OCV (fig.4), and terminal voltage of the battery with an error less than 0.1%

Online Battery Parameter And Open Circuit Voltage (OCV) Estimation Using Recursive Least Square (RLS)