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Electrode_SOC_and_SOH_estimation_with_electrode_level_ECMs__JES.pdf (2.626Mb)
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Title
Electrode State of Charge and State of Health Estimation with Electrode-Level Equivalent-Circuit Models
Author
Lopetegi, Iker
Fernandez Gonzalez, Sergio
Plett, Gregory L.
Trimboli, M. Scott
IRAOLA, UNAI
Research Group
Almacenamiento de energía
Other institutions
University of Colorado Colorado Springs (UCCS)
Version
Postprint
Rights
© 2025 The Electrochemical Society (“ECS”)
Access
Embargoed access
URI
https://hdl.handle.net/20.500.11984/13903
Publisher’s version
https://doi.org/10.1149/1945-7111/ade297
Published at
Journal of the Electrochemical Society  Vol. 172. N. 6. N. art. 060522, 2025
Publisher
IOP Publishing
Subject (UNESCO Thesaurus)
Electric energy
UNESCO Classification
Energy technology
Abstract
To prolong battery lifetime and maximize performance, battery management systems (BMSs) need to estimate internal variables that are related to battery aging. For that, accurate battery models that ca ... [+]
To prolong battery lifetime and maximize performance, battery management systems (BMSs) need to estimate internal variables that are related to battery aging. For that, accurate battery models that can represent internal variables that are not directly measurable are required. However, most present BMSs use equivalent-circuit models (ECMs or EQMs) for state of charge (SOC) and state of health (SOH) estimation. These models are not able to predict these aging-related variables, and therefore, they cannot be used to limit battery degradation. In this paper, we propose a method for electrode-level SOC (eSOC) and electrode-level SOH (eSOH) estimation using an electrode-level ECM (eECM), which can describe both electrodes’ potentials, giving valuable information for aging prevention. The method produces state of lithiation (SOL) and potential estimates of both electrodes, and updates the eSOH parameters to maintain estimation accuracy through the lifetime of the battery. Furthermore, the eSOH parameter estimates are used to obtain degradation mode information, which could be used to improve state estimation, health diagnosis and prognosis. [-]
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