Impedance based modeling of battery parameters and behavior
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Modeling battery performance under arbitrary load has gained importance in recent years with the increasing demand on batteries in various fields from automotive industry to consumer electronic devices. Due to numerous application areas of electrochemical energy storage (EES) systems, researchers have tried to predict the battery performance and the voltage using extensive calculations. Unfortunately, in order to achieve high levels of accuracy, the model has to be algebraically and computationally complex. Models with decreased computational and algebraic complexity suffer from loss of accuracy. In this thesis, we offer a new modeling approach to predict the voltage responses of batteries and supercapacitors which is both algebraically straightforward and yielding more accurate results. Our approach is valid using any discharge profile including published by regulatory bodies such as Environmental Protection Agency (EPA). Our method is based on Electrochemical Impedance Spectroscopy (EIS) measurements done on the system to be predicted and slow DC discharge. EIS data is used directly to predict the fast moving portion of the voltage response to the profiles. The EIS data is used as is, namely, in frequency domain without any modeling. The slow DC discharge data provides DC response and is added in through a straightforward lookup table. This widely applicable approach can predict the voltage with less than 1% error, without any adjustable parameters to a large variety of discharge profiles.