EXPERIMENTAL VALIDATION OF AN UNKNOWN INPUT ESTIMATION ALGORITHM FOR LITHIUM ION BATTERY APPLICATIONS
Area of Honors:
Bachelor of Science
Dr. Hosam Kadry Fathy, Thesis Supervisor Dr. Jacqueline Antonia O'Connor, Honors Advisor
Battery estimation lco lfp input current current soc kalman filter state space lithium-ion voltage experimental simulation matlab arbin cycler
The thesis develops an experimental setup to validate a model-based algorithm for combined state and current estimation in a lithium-ion battery. The algorithm estimates external (input) current based on the measured terminal voltage. This is useful where current measurement is of interest, but it is either not possible to measure directly or it is too costly for smaller budgeted applications. A paper by Mishra et al. has already theoretically analyzed and validated the algorithm. Although important, the theoretical analysis does not offer a comprehensive picture of the algorithm’s success because the reduced-order model for estimation cannot fully capture the battery’s dynamic. Thus, an experimental investigation becomes necessary. The results of the experiments indicate that the proposed algorithm cannot by itself successfully predict and track the external current and internal state of a battery in the case of Lithium Cobalt Oxide (LCO) and Lithium Iron Phosphate (LFP) lithium-ion batteries. An addition of a 0.5 Ω resistor improves the performance of the algorithm immensely in estimating the input current of the battery. This improvement shows that algorithm can be useful in estimating the input current of a lithium-ion battery without a current sensor if an additional external resistor is used.