UAS based Li-ion battery model parameters estimation. (September 2017) - Ali, D.; Mukhopadhyay, S.; Rehman, H.; Khurram, A.
Abstract: Estimation of Lithium-ion (Li-ion) battery model parameters is key for accurately determining battery state of charge (SOC). Estimating these parameters requires substantial experimental effort. This work reduces the experimentation required, by using universal adaptive stabilization (UAS) for estimating parameters appearing in battery model state equations. Accuracy of estimated model parameters is verified by comparing the estimated and measured battery terminal voltage. SOC obtained using the estimated model parameters, and open circuit EMF vs SOC curve, captures effects due to discharge currents of small magnitude, which Coulomb counting, well known filtering methods ignore. Rigorous mathematical analysis supports the experimental results presented. Highlights: A clear three step procedure for Li-ion battery model parameters estimation. Application of Universal Adaptive Stabilization (UAS) for parameters estimation. Equivalent circuit model elements depend on battery state of charge (SOC). SOC thus estimated captures effects of small changes in current. Tracking error convergence, estimated parameter convergence proven mathematically, and experimentally.