Lithium-Ion Battery Parameter Identification via Extremum Seeking Considering Aging and Degradation

Iván Sanz-Gorrachategui, Pablo Pastor-Flores, Antonio Bono-Nuez, Cora Ferrer-Sánchez, Alejandro Guillén-Asensio and Carlos Bernal-Ruiz
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Iván Sanz-Gorrachategui: Electric and Communications Department, University of Zaragoza, 50018 Zaragoza, Spain
Pablo Pastor-Flores: Electric and Communications Department, University of Zaragoza, 50018 Zaragoza, Spain
Antonio Bono-Nuez: Electric and Communications Department, University of Zaragoza, 50018 Zaragoza, Spain
Cora Ferrer-Sánchez: Electric and Communications Department, University of Zaragoza, 50018 Zaragoza, Spain
Alejandro Guillén-Asensio: Electric and Communications Department, University of Zaragoza, 50018 Zaragoza, Spain
Carlos Bernal-Ruiz: Electric and Communications Department, University of Zaragoza, 50018 Zaragoza, Spain

Energies, 2021, vol. 14, issue 22, 1-12

Abstract: Battery parameters such as State of Charge (SoC) and State of Health (SoH) are key to modern applications; thus, there is interest in developing robust algorithms for estimating them. Most of the techniques explored to this end rely on a battery model. As batteries age, their behavior starts differing from the models, so it is vital to update such models in order to be able to track battery behavior after some time in application. This paper presents a method for performing online battery parameter tracking by using the Extremum Seeking (ES) algorithm. This algorithm fits voltage waveforms by tuning the internal parameters of an estimation model and comparing the voltage output with the real battery. The goal is to estimate the electrical parameters of the battery model and to be able to obtain them even as batteries age, when the model behaves different than the cell. To this end, a simple battery model capable of capturing degradation and different tests have been proposed to replicate real application scenarios, and the performance of the ES algorithm in such scenarios has been measured. The results are positive, obtaining converging estimations both with new and aged batteries, with accurate outputs for the intended purpose.

Keywords: Li-ion battery; extremum seeking; parameter tracking; SoC; SoH; battery aging; ECM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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