Joint online update of model structure and parameters for battery equivalent circuits using impedance spectroscopy and machine learning

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A hybrid machine learning framework utilizing electrochemical impedance spectroscopy enables the feasible online updating of battery equivalent circuit model structures and parameters on standard battery management system hardware. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Yilei Lin; Dai Liu; Philipp Welz; Hangying Li; Franco Palau; Philip Bilfinger; Raphael Urban; Cristina Grosu; Martin Schulz; Markus Lienkamp

doi:10.1016/j.etran.2026.100604

May 24, 2026eTransportationOpen Access

Joint online update of model structure and parameters for battery equivalent circuits using impedance spectroscopy and machine learning

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Abstract

• Hybrid ML framework for ECM structure selection and parameterization. • Onboard use of EIS for online battery model updates in electric vehicles. • Open-source labeled EIS dataset across diverse battery conditions. • Feasibility analysis for deployment on standard BMS hardware.

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Lin et al. (2026) studied this question.

synapsesocial.com/papers/6a2072ccc7fd8e96e4f5f9f9 https://doi.org/10.1016/j.etran.2026.100604

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Joint online update of model structure and parameters for battery equivalent circuits using impedance spectroscopy and machine learning

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Joint online update of model structure and parameters for battery equivalent circuits using impedance spectroscopy and machine learning

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