Lithium-Ion Battery Parameter Identification for Hybrid and Electric Vehicles Using Drive Cycle Data

Yasser Ghoulam, Tedjani Mesbahi, Peter Wilson, Sylvain Durand, Andrew Lewis, Christophe Lallement and Christopher Vagg
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Yasser Ghoulam: ICube (UMR CNRS 7357), INSA Strasbourg, University of Strasbourg, 67000 Strasbourg, France
Tedjani Mesbahi: ICube (UMR CNRS 7357), INSA Strasbourg, University of Strasbourg, 67000 Strasbourg, France
Peter Wilson: Institute for Advanced Automotive Propulsion Systems (IAAPS), University of Bath, Claverton Down, Bath BA2 7AY, UK
Sylvain Durand: ICube (UMR CNRS 7357), INSA Strasbourg, University of Strasbourg, 67000 Strasbourg, France
Andrew Lewis: Institute for Advanced Automotive Propulsion Systems (IAAPS), University of Bath, Claverton Down, Bath BA2 7AY, UK
Christophe Lallement: ICube (UMR CNRS 7357), INSA Strasbourg, University of Strasbourg, 67000 Strasbourg, France
Christopher Vagg: Institute for Advanced Automotive Propulsion Systems (IAAPS), University of Bath, Claverton Down, Bath BA2 7AY, UK

Energies, 2022, vol. 15, issue 11, 1-15

Abstract: This paper proposes an approach for the accurate and efficient parameter identification of lithium-ion battery packs using only drive cycle data obtained from hybrid or electric vehicles. The approach was experimentally validated using data collected from a BMW i8 hybrid vehicle. The dual polarization model was used, and a new open circuit voltage equation was proposed based on a simplification of the combined model, with the aim of reducing the number of parameters to be identified. The parameter identification was performed using NEDC data collected on a rolling road dynamometer; the results showed that the proposed model improved the accuracy of terminal voltage estimation, reducing the peak voltage error from 2.16% using the Nernst model to 1.28%. Furthermore, the robustness of these models in maintaining accuracy when new drive cycles were used was evaluated by comparing WLTC simulations with experimental measurements. The proposed model showed improved robustness, with a reduction in RMS error of more than 50% compared to the Nernst model. These findings are significant because they will improve the accuracy of model-based battery management systems used in electric vehicles, allowing for improved performance prediction without the requirement of recharacterization for different drive cycles or individual cell characterization.

Keywords: battery parameter identification; lithium-ion battery; electric vehicle; parameter char acterization; optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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