Estimation of Ro and Vc Parameters Using Recursive Least Square, as Well as OCV Parameters at Rest Conditions in Pulse Test for the Thevenin Battery Model Implemented on Raspberry Pi Zero

Lithium polymer (Li-Po) batteries are one of the most widely used batteries, especially on everyday devices such as mobile phones and laptops. One of the main reasons for using this type of Lithium battery is its high energy density. In its use, this battery needs to be monitored to prevent unwanted things from happening. A model is needed to describe the characteristics of Li-Po batteries well in monitoring changes in the battery system. In general, the model that is often used is the Thevenin battery model. In this study, the parameters in the Thevenin model, such as Ro and Vc, are estimated using the RLS algorithm, while the OCV is estimated according to the terminal voltage value during the rest condition in the pulse test. The entire estimation process is carried out using a low-computing device, the Raspberry Pi Zero, with the help of an INA 260 sensor to read the battery current and voltage. The battery capacity used in this study is 5200mAh with a voltage of 11.1V. The pulse test device in this study uses a constant current discharge and a microcontroller device for the timing process. Before the voltage and current data are used for parameter estimation, the data is filtered using a one-dimensional Kalman filter. The estimation results for OCV, Ro, and Vc show quite good performance, with an MSE value of 5.42× 10−6 V and an RMSE of 0.0023 V.