Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares (2024)

Wang, Y., Ferrari, R. M. G. (2024). Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares. In Proceedings of the European Control Conference, ECC 2024 (pp. 3551-3556). IEEE. https://doi.org/10.23919/ECC64448.2024.10591276

Wang, Yang ; Ferrari, Riccardo M.G. ; Verhaegen, Michel. / Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares. Proceedings of the European Control Conference, ECC 2024. IEEE, 2024. pp. 3551-3556

@inproceedings{20dcd00aa9e1461e8dc4543347b13aa6,

title = "Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares",

abstract = "Identification of lithium-ion (Li-ion) battery models is essential for enhancing the operation of electrical vehicles. This paper develops a novel approach for estimating the equivalent circuit model (ECM) of Li-ion batteries and reconstructing the open-circuit voltage (OCV) and state of charge (SOC) relationship. We formulate the OCV-SOC relation as a piecewise affine (PWA) function and estimate its coefficients and the Markov parameters (impulse response) of the ECM via l1-regularized least squares. The state space model of the ECM is derived through the Ho-Kalman algorithm. Experiments with simulated and real-life battery data demonstrate the method's effectiveness and advantages with respect to the state of the art.",

author = "Yang Wang and Ferrari, {Riccardo M.G.} and Michel Verhaegen",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2024 European Control Conference, ECC 2024 ; Conference date: 25-06-2024 Through 28-06-2024",

year = "2024",

doi = "10.23919/ECC64448.2024.10591276",

language = "English",

pages = "3551--3556",

booktitle = "Proceedings of the European Control Conference, ECC 2024",

publisher = "IEEE",

address = "United States",

}

Wang, Y, Ferrari, RMG 2024, Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares. in Proceedings of the European Control Conference, ECC 2024. IEEE, pp. 3551-3556, 2024 European Control Conference, ECC 2024, Stockholm, Sweden, 25/06/24. https://doi.org/10.23919/ECC64448.2024.10591276

Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares. / Wang, Yang; Ferrari, Riccardo M.G.; Verhaegen, Michel.
Proceedings of the European Control Conference, ECC 2024. IEEE, 2024. p. 3551-3556.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares

AU - Wang, Yang

AU - Ferrari, Riccardo M.G.

AU - Verhaegen, Michel

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2024

Y1 - 2024

N2 - Identification of lithium-ion (Li-ion) battery models is essential for enhancing the operation of electrical vehicles. This paper develops a novel approach for estimating the equivalent circuit model (ECM) of Li-ion batteries and reconstructing the open-circuit voltage (OCV) and state of charge (SOC) relationship. We formulate the OCV-SOC relation as a piecewise affine (PWA) function and estimate its coefficients and the Markov parameters (impulse response) of the ECM via l1-regularized least squares. The state space model of the ECM is derived through the Ho-Kalman algorithm. Experiments with simulated and real-life battery data demonstrate the method's effectiveness and advantages with respect to the state of the art.

AB - Identification of lithium-ion (Li-ion) battery models is essential for enhancing the operation of electrical vehicles. This paper develops a novel approach for estimating the equivalent circuit model (ECM) of Li-ion batteries and reconstructing the open-circuit voltage (OCV) and state of charge (SOC) relationship. We formulate the OCV-SOC relation as a piecewise affine (PWA) function and estimate its coefficients and the Markov parameters (impulse response) of the ECM via l1-regularized least squares. The state space model of the ECM is derived through the Ho-Kalman algorithm. Experiments with simulated and real-life battery data demonstrate the method's effectiveness and advantages with respect to the state of the art.

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U2 - 10.23919/ECC64448.2024.10591276

DO - 10.23919/ECC64448.2024.10591276

M3 - Conference contribution

AN - SCOPUS:85200563793

SP - 3551

EP - 3556

BT - Proceedings of the European Control Conference, ECC 2024

PB - IEEE

T2 - 2024 European Control Conference, ECC 2024

Y2 - 25 June 2024 through 28 June 2024

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Wang Y, Ferrari RMG, Verhaegen M. Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares. In Proceedings of the European Control Conference, ECC 2024. IEEE. 2024. p. 3551-3556 doi: 10.23919/ECC64448.2024.10591276