(3) You are asked to identify the model parameters based on the model structure using the pulse-relaxation test data. The test are in the file: HW2_P3_ChargePulse_C5_at50SOC_5per.mat. For this problem, a nonlinear

least square method is used; use the skeleton m-file HW2_P3_Skeleton.m to perform the nonlinear least squares for parameter identification. The function edge_detects.m is used to determine the indices at the beginning and end of the pulse operation. (i) Consider "OCV-R-RC" type model. • Report your identified model parameters {R₁, R₁, C₁}. • Provide a figure showing the measured and estimated (by your model) battery voltage vs. time during the relaxation period. • Discuss the impact of the data length on the performance of the model in terms of accuracy, RMSE. (ii) Repeat part (i); this time, "OCV-R-RC-RC" type model is considered. • Report your identified model parameters {R., R₁, C₁, R2, C₂}. • Provide a figure showing the measured and estimated (by your model) battery voltage vs. time during the relaxation period. • Discuss the impact of the data length on the performance of the model in terms of accuracy, RMSE. Read the paper by Lin et. al (2014) "A lumped-parameter electro-thermal model for cylindrical batteries," Journal of Power Sources for your reference.