Battery SOC estimation from EIS data based on machine learning and equivalent circuit model Article Swipe
Emanuele Buchicchio
,
Alessio De Angelis
,
Francesco Santoni
,
Paolo Carbone
,
Francesco Bianconi
,
Fabrizio Smeraldi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.energy.2023.128461
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.energy.2023.128461
Estimating the state of charge (SOC) of batteries is fundamental for the proper management and safe operation of numerous systems, including electric vehicles, smart energy grids, and portable electronics. While there is no practical method for direct measurement of SOC, several estimation approaches have been developed, including a growing number of machine-learning-based techniques. Machine learning methods are intrinsically data-driven but can also benefit from a-priori knowledge embedded in a model. In this work, we first demonstrate, through exploratory data analysis, that it is possible to discriminate between different SOC from electrochemical impedance spectroscopy (EIS) measurements. Then we propose a SOC estimation approach based on EIS and an equivalent circuit model to provide a compact way to describe the frequency domain and time-domain behavior of the impedance of a battery. We experimentally validated this approach by applying it to a dataset consisting of EIS measurements performed on four lithium-ion cylindrical cells at different SOC values. The proposed approach allows for very efficient model training and produces a low-dimensional SOC classification model that achieves above 93% accuracy. The resulting low-dimensional classification model is suitable for embedding into battery-powered systems and for online SOC estimation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.energy.2023.128461
- OA Status
- hybrid
- Cited By
- 119
- References
- 46
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4384937756Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.energy.2023.128461Digital Object Identifier
- Title
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Battery SOC estimation from EIS data based on machine learning and equivalent circuit modelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-20Full publication date if available
- Authors
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Emanuele Buchicchio, Alessio De Angelis, Francesco Santoni, Paolo Carbone, Francesco Bianconi, Fabrizio SmeraldiList of authors in order
- Landing page
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https://doi.org/10.1016/j.energy.2023.128461Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.energy.2023.128461Direct OA link when available
- Concepts
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State of charge, Equivalent circuit, Battery (electricity), Computer science, Electronic engineering, Electrical impedance, A priori and a posteriori, Artificial intelligence, Machine learning, Engineering, Electrical engineering, Voltage, Power (physics), Quantum mechanics, Epistemology, Physics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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119Total citation count in OpenAlex
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2025: 65, 2024: 49, 2023: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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