基于多项式回归的太阳能PV阵列电池荷电状态建模
Battery State of Charge Modeling for Solar PV Array using Polynomial Regression
摘要：
在这篇手稿中，我们已经调查了充电状态（SoC）的响应和开路电压下的动态电池模型在可变的电压和电流在电池的充电周期。这些变化的输入电压和电流已经得到了使用的太阳能光伏阵列的变化的辐照度和表面温度连接作为动态电池模型的输入，以存储在它里面的能量。为了使仿真结果与实际情况相匹配，这些变化的辐照度和表面温度的太阳能光伏阵列随时间的变化进行了仿真。在形成能量并将能量存储在动态电池模型内之后，使用卡尔曼滤波器方法估计电池的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）和荷电状态（SoC）。针对多项式次数为2、3、4和5绘制了OCV和SoC之间的回归图。结果表明，R $^2$保持增加，我们增加的程度回归。同时，随着多项式回归次数的增加，RMSE值不断减小。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
