@inproceedings{25e07e9111324df4b58c7a833419747b,
title = "Novel online fitting algorithm for impedance-based state estimation of Li-ion batteries",
abstract = "The impedance of a Li-ion battery is an important parameter for the battery's state-of-charge (SOC) and state-of-health (SOH) estimation. Battery impedance is typically modeled by an equivalent-circuit-model (ECM) in which the variations in the specific model parameters can be used for estimating the SOC and the SOH. However, the fact that the battery impedance is highly non-linear complicates the parameterization of the model. The model is traditionally obtained by a complex non-linear least-squares fitting algorithm which comes with high complexity. This paper proposes a novel approach to extract all the ECM parameters of the battery impedance obtained with online-capable pseudo-random-sequence (PRS) measurements. Although the algorithm has low complexity, it still captures the desired variations in the ECM parameters as a function of SOC. The algorithm is validated for the impedance data from a lithium-iron-phosphate cell.",
keywords = "Impedance, Batteries, Computed tomography, Battery charge measurement, Impedance measurement, Electronic countermeasures, Frequency measurement, Li-ion batteries, PRS, Online impedance measurements, SOC, SOH, State-estimation",
author = "Jussi Sihvo and Tomi Roinila and Tuomas Messo and Daniel-Ioan Stroe",
year = "2019",
month = oct,
doi = "10.1109/IECON.2019.8927338",
language = "English",
isbn = "978-1-7281-4879-3",
series = "Annual Conference of the IEEE Industrial Electronics Society",
publisher = "IEEE",
pages = "4531--4536",
booktitle = "IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society",
note = "Annual Conference of the IEEE Industrial Electronics Society ; Conference date: 01-01-1900",
}