State and Parametric Estimation of Li-Ion Batteries in Electrified Vehicles

Abstract: The increasing demand for electric vehicles (EVs) has led to technological advancementsin the field of battery technology. State of charge (SOC) estimation is a vital function ofthe battery management system - the heart of EVs, and Kalman filtering is a commonmethod for SOC estimation. Due to the non uniformities in tuning and testing scenarios,quantifying performance of SOC estimation algorithms is difficult. Gathering data fordifferent operational scenarios is also cumbersome. In this thesis, SOC estimation algorithmsare developed and tested for a variety of scenarios like varying sensor noise andbias properties, varying state and parameter initializations as well as different initial celltemperatures. A validated and open-source simulation plant model is used to enable easygathering of data for different operational scenarios.The simulation results show that unscented Kalman filter performs better than extendedKalman filter in presence of hard nonlinearities and high initial uncertainties. However,both filters gave similar performance under nominal conditions implying that the choiceof estimation algorithms must depend on operational scenarios. Observability analysisalso gave valuable information to aid in selection of algorithms. The simulation plantmodel facilitated easy data collection for initial development of algorithms, which werethen tested successfully using a real dataset. Further testing using real datasets is requiredto enhance validation.