2026-04-11T17:25:38Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/62882024-03-27T08:22:02Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Lopetegi, Iker author Oca, Laura author IRAOLA, UNAI author 2024 Battery management systems (BMSs) are required to estimate many non-measurable values that describe the actual operating condition of batteries; such as state of charge (SOC) or state of health (SOH). In order to improve accuracy, many physical states and parameters can be estimated using physics-based models (PBMs). These estimates could be used to improve the control and
prognosis of batteries. In a series of papers, we propose a new method to estimate internal physical states, SOC, SOH and other electrode-specific state of health (eSOH) parameters of a lithium-ion battery, using interconnected sigma-point Kalman filters (SPKFs) and a single-particle model with electrolyte dynamics (SPMe). This second paper focuses on eSOH parameter estimation. Simulation
results show that the method is capable of estimating the eSOH parameters and key degradation modes that can occur inside a lithium-ion battery cell using only cell voltage and current measurements. 0013-4651 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=175879 https://hdl.handle.net/20.500.11984/6288 Lithium Ion Battery Sigma-Point Kalman Filter (SPKF) State-of-Charge (SOC) Estimation Physics-based model (PBM) State-of-Health (SOH) electrode- State-of-Health (eSOH) Degradation Modes A New Battery SOC/SOH/eSOH Estimation Method Using a PBM and Interconnected SPKFs: Part II. SOH and eSOH Estimation