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A mathematical model for lead-acid batteries
Abstract
A mathematical model of a lead-acid battery is presented. This model takes into account self-discharge, battery storage capacity, internal resistance, overvoltage, and environmental temperature. Nonlinear components are used to represent the behavior of the different battery parameters thereby simplifying the model design. The model components are found by using manufacturers specifications and experimental tests. A comparison between the model and experimental results obtained from a battery evaluation test system was used for verification. This model can be used to accurately evaluate battery performance in electrical systems.>
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Citations
Cited by
Accurate electrical battery model capable of predicting runtime and I-V performance
Chen, M., & Rincon-Mora, G. A. (2006) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2006.874229 · Cited by 2,427
An Improved Battery Characterization Method Using a Two-Pulse Load Test
Coleman, M., Hurley, W. G., & Lee, C. K. (2008) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2007.914329 · Cited by 191
Metadata
- Authors
- Salameh, Z. M., Casacca, M. A., & Lynch, W. A.
- Venue
- IEEE Transactions on Energy Conversion
- Published
- 1992-03
- Volume
- 7
- Issue
- 1
- Pages
- 93-98
- ISSN
- 0885-8969 (ISSN-L)
- OpenAlex
- W2124042706
- Cited by
- 638
- References
- 0
- FWCI
- 19.90
- Type
- journal article
- Language
- en
- Open access
- closed
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: A mathematical model for lead-acid batteries. https://app.cochat.ai/papers/10.1109/60.124547
- The paper
- Salameh, Z. M., Casacca, M. A., & Lynch, W. A. (1992). A mathematical model for lead-acid batteries. IEEE Transactions on Energy Conversion. https://doi.org/10.1109/60.124547
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
