DRT-based SoC estimation for commercial Li-ion battery pack

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dc.contributor.authorIurilli, Pietro
dc.contributor.authorKoch, Nelson
dc.contributor.authorCarrillo, Rafael E.
dc.contributor.authorBrivio, Claudio
dc.date.accessioned2024-09-09T13:22:54Z
dc.date.available2024-09-09T13:22:54Z
dc.date.issued2023
dc.description.abstractThe precise assessment of battery SoC is crucial to maximize battery pack performance. This work presents a physics-based State of Charge (SoC) estimation algorithm for Li-ion battery packs based on Electric Circuit Model (ECM) derived from Electrochemical Impedance Spectroscopy (EIS) and Open Circuit Voltage (OCV) measurements. Distribution of Relaxation Time (DRT) has been applied to deconvolve EIS spectra, to configure the ECM and to extract the model parameters. An Extended Kalman filter (EKF) is applied to estimate SoC and correct it. The algorithm is applied to a commercial NMC battery module (20s1p) and compared with state-of-the art SoC estimator based on Coulomb Counting (CC). The tested case-study show that ECM-based SoC estimation can reduce the SoC estimation error of one order of magnitude per cycle, especially in scenarios where the CC cannot be re-initialized based on OCV.
dc.identifier.citation2023 International Conference on Clean Electrical Power, ICCEP 2023, pp. 1 - 7
dc.identifier.doi10.1109/ICCEP57914.2023.10247431
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1503
dc.identifier.urlhttps://doi.org/10.1109/ICCEP57914.2023.10247431
dc.language.isoen
dc.titleDRT-based SoC estimation for commercial Li-ion battery pack
dc.typeConference / Workshop
dc.type.csemdivisionsBU-V
dc.type.csemresearchareasBatteries
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