2020/08/20 by Siddhi Vinayak Pandey, Pandey, Siddhi Vinayak, Jeet Patel +3
Computer Science · Energy · Engineering · Mathematics · #Advanced Battery Technologies Research #Artificial intelligence #Battery (electricity) #Computer science #Control theory (sociology) #Electrical engineering #Engineering #FOS: Electrical engineering #Irradiance #Linear regression #Mathematics #Meteorology #Open-circuit voltage #Optics #Photovoltaic System Optimization Techniques #Photovoltaic system #Physics #Polynomial #Polynomial regression #Power (physics) #Regression analysis #Solar Radiation and Photovoltaics #Solar irradiance #State of charge #State variable #Statistics #Systems and Control (eess.SY) #Variable (mathematics) #Voltage #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.09038
published in arXiv (Cornell University) (Cornell University) · Results under progress
openalex publication_date 2020/08/20 · arxiv created 2020/12/07 · arxiv updated 2020/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this manuscript, we have investigated the response of the State of Charge\n(SoC) and the open-circuit voltage across the dynamic battery model under the\nvariable voltage and current during the charging cycle of the battery. These\nvariable input voltage and current have been obtained using the variable\nirradiance and surface temperature of a Solar PV array which is connected as an\ninput of the dynamic battery model to store the energy within it. In order to\nmatch the Simulation result with reality, these variable irradiance and surface\ntemperature of Solar PV Array with respect to time has been simulated. After\nforming and storing the energy within the dynamic battery model; the SoC of the\nbattery has been estimated using the Kalman filter approach. After the\nsuccessful estimation of SoC; the Open Circuit Voltage (OCV) and State of\nCharge (SoC) have been plotted using the polynomial regression technique. The\nregression plots between the OCV and SoC have been drawn for the polynomial\ndegree of 2, 3,4, and 5. Results reveal that R2 keeps increasing as we\nincrease the degrees of regression. Simultaneously the value of RMSE keeps\ndecreasing as we increase the degree of the polynomial regression.\n