Battery State of Charge Modeling for Solar PV Array using Polynomial Regression

TL;DR

This paper models the battery's State of Charge (SoC) using polynomial regression based on data from a dynamic battery model influenced by variable solar PV array conditions, improving accuracy with higher polynomial degrees.

Contribution

It introduces a polynomial regression approach to accurately estimate battery SoC from OCV, considering variable PV array conditions, with analysis of different polynomial degrees.

Findings

Higher polynomial degrees improve R² and reduce RMSE.

Polynomial regression effectively models OCV-SoC relationship.

Results demonstrate enhanced SoC estimation accuracy.

Abstract

In this manuscript, we have investigated the response of the State of Charge (SoC) and the open-circuit voltage across the dynamic battery model under the variable voltage and current during the charging cycle of the battery. These variable input voltage and current have been obtained using the variable irradiance and surface temperature of a Solar PV array which is connected as an input of the dynamic battery model to store the energy within it. In order to match the Simulation result with reality, these variable irradiance and surface temperature of Solar PV Array with respect to time has been simulated. After forming and storing the energy within the dynamic battery model; the SoC of the battery has been estimated using the Kalman filter approach. After the successful estimation of SoC; the Open Circuit Voltage (OCV) and State of Charge (SoC) have been plotted using the polynomial regression technique. The regression plots between the OCV and SoC have been drawn for the polynomial degree of 2, 3,4, and 5. Results reveal that R$^2$ keeps increasing as we increase the degrees of regression. Simultaneously the value of RMSE keeps decreasing as we increase the degree of the polynomial regression.