论文标题
使用多项式回归的太阳能光伏阵列的电池电荷模型
Battery State of Charge Modeling for Solar PV Array using Polynomial Regression
论文作者
论文摘要
在本手稿中，我们研究了电池电压和电池充电周期内电池电量模型的充电状态（SOC）和开路电压的响应。这些变量输入电压和电流是使用太阳能PV阵列的可变辐照度和表面温度获得的，该阵列被连接为动态电池模型的输入，以存储在其内部的能量。为了使模拟结果与现实相匹配，已经模拟了这些可变的辐照度和太阳PV阵列的表面温度。在动态电池模型中形成和存储能量之后；使用Kalman滤波器方法估算了电池的SOC。在成功估计SOC之后；使用多项式回归技术绘制了开路电压（OCV）和电荷状态（SOC）。 OCV和SOC之间的回归图的多项式程度为2、3,4和5。结果表明，随着我们提高回归程度，R $^2 $不断增加。同时，随着我们增加多项式回归程度，RMSE的值不断降低。
In this manuscript, we have investigated the response of the State of Charge (SoC) and the open-circuit voltage across the dynamic battery model under the variable voltage and current during the charging cycle of the battery. These variable input voltage and current have been obtained using the variable irradiance and surface temperature of a Solar PV array which is connected as an input of the dynamic battery model to store the energy within it. In order to match the Simulation result with reality, these variable irradiance and surface temperature of Solar PV Array with respect to time has been simulated. After forming and storing the energy within the dynamic battery model; the SoC of the battery has been estimated using the Kalman filter approach. After the successful estimation of SoC; the Open Circuit Voltage (OCV) and State of Charge (SoC) have been plotted using the polynomial regression technique. The regression plots between the OCV and SoC have been drawn for the polynomial degree of 2, 3,4, and 5. Results reveal that R$^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.
