(1) Using the open-circuit-voltage (OCV) data (measured at C/20) in the file HW2_P1_V_avgC20. mat, obtain an analytic expression for the battery OCV; that is, the OCV is a function of the

battery state-of-charge (SOC). Use the data set V_avgC20 among three data sets. Consider the following nth order polynomial for the OCV: TI Vocv(2) = Σα;z", i=0 where z is the battery SOC. Use the skeleton m-file HW2_P1_Skeleton.m to perform the parameter identification in the sense of least square regression. • Report your identified model parameters using a polynomial order of n = 4. • Provide a figure with two subplots showing the measured and estimated (by your model) battery voltage vs. battery SOC in the top subplot and the error between the measured and estimated voltages in the bottom subplot. • Investigate the performance of the model in terms of accuracy, RMSE, while changing the order of polynomial. Is the RMSE always decreasing as you increase n? Is there a disadvantage in having a high order polynomial fi+?