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

Conference paper (2024)

Authors

Y. Wang Team Riccardo Ferrari - Mechanical, Maritime and Materials Engineering
Riccardo M.G. Ferrari Team Riccardo Ferrari - Mechanical, Maritime and Materials Engineering
M.H.G. Verhaegen Team Michel Verhaegen - Mechanical, Maritime and Materials Engineering
Research Group
Team Riccardo Ferrari (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2024

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Riccardo Ferrari

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.

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