UBC Theses and Dissertations
A study of state of health estimation methods for li-ion batteries Shabbir, Hassan
Currently, battery management systems are battery chargers, commonly comprised of power electronic circuits, which lack the ability to accurately estimate the state of health of a battery. Since, batteries have a limited lifetime, repeated charge and discharge cycles quickly deteriorate the electrical properties of the battery. With the reduced capacity and several other changes in the state of health of a battery, the electronic device might malfunction. This research is aimed to provide on device upgrade for all battery management systems and battery chargers to include battery health monitoring ability. For the evaluation of state of health of batteries, two approaches are considered in parallel, Electrochemical Impedance Spectroscopy (EIS) and profiling through charge and discharge curves. For EIS, the initial focus of this research is to design and validate the hardware that can perform EIS scans over a desired range of frequencies. Based on the footprints of scan, a state of health classification algorithm is proposed which categorizes batteries according to the set threshold. The main contribution of this project to existing EIS technology is the eradication of the need of battery modeling and parameter estimation from Nyquist plot to find the state of health of a battery. Tests are performed on hardware prototype to validate the designed algorithm that shows State of Health estimation accuracy of almost 90%. Another method considered for State of Health estimation is profiling through charge and discharge curves of the Li Ion batteries. Raw profiling data is examined to decipher the correlation between shape of charge and discharge curves and state of health. From the charging profile of the battery, constant charge current duration parameter is identified to possess promising potential to provide information about state of health of a battery. The behavior of the parameter is investigated in detail with repeated laboratory tests on almost 200 samples gathered from five different battery vendors. This technique showed above 90% classification accuracy. Finally a comparison is drawn between the EIS technology and charge curve profiling method with respective advantages and disadvantages to emphasize the suitability of each technique for different field applications.
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