Abstract
Battery plays a crucial role in nowadays technology and is extensively used in electrical and electronic systems. In order to utilize battery efficiently and optimally, it is important to be able to provide good control and management functions of the battery while it is in use or charging. Most of those functions are based on the battery operational characteristics as well as accurate measurements or estimations of battery status and/or parameters. For example, parameters such as battery internal resistance could reflect battery status which is very significant information for battery management system. Therefore, it is important that battery parameters can be measured or estimated by different approaches either on-line or off-line. The main focus of this thesis is to investigate and compare two different on-line methods for estimating battery parameters. One is the adaptive observer for dynamical estimation, and the other is the extended Kalman filter for state and parameters estimation. We first carefully examine the derivations and build Matlab simulation programs of the two methods, respectively, and then design and perform a series of systematic simulations for these two methods to study their advantages and disadvantages in battery parameter estimation. Moreover, we also build three Li-ion battery models in Simulink for the aforementioned series of systematic simulations. The battery models can simulate dynamic battery characteristics in Simulink environment under different battery simplification assumptions. These models also can be used for various simulations in different system design applications, e.g., electric vehicle or power system design. By using these Li-ion models, system designers could have a much more accurate simulation result for their systems.