The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Online State-of-Health Estimation of VRLA Batteries Using State of Charge
Abstract
This paper presents an online method for the estimation of the state of health (SOH) of valve-regulated lead acid (VRLA) batteries. The proposed method is based on the state of charge (SOC) of the battery. The SOC is estimated using the extended Kalman filter and a neural-network model of the battery. Then, the SOH is estimated online based on the relationship between the SOC and the battery open-circuit voltage using fuzzy logic and the recursive least squares method. To obtain the open-circuit voltage while the battery is operating, the reflective charging process is employed. Experimental results show good estimation of the SOH of VRLA batteries.
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Cites
Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009) · Applied Energy · doi:10.1016/j.apenergy.2008.11.021 · Cited by 1,636
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831
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
Cited by
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059
Predicting the state of charge and health of batteries using data-driven machine learning
Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J., & Seh, Z. W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0156-7 · Cited by 793
Lipu, M. S. H., Hannan, M. A., Hussain, A., Hoque, M. M., Ker, P. J., Saad, M. H. M., & Ayob, A. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.09.065 · Cited by 755
Nejad, S., Gladwin, D. T., & Stone, D. A. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.03.042 · Cited by 565
Vidal, C., Malysz, P., Kollmeyer, P., & Emadi, A. (2020) · IEEE Access · doi:10.1109/access.2020.2980961 · Cited by 512
Metadata
- Authors
- Shahriari, M., & Farrokhi, M.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2013-01
- Volume
- 60
- Issue
- 1
- Pages
- 191-202
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W1998527754
- Cited by
- 190
- References
- 3
- FWCI
- 25.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shahriari, M., & Farrokhi, M. (2013). Online State-of-Health Estimation of VRLA Batteries Using State of Charge. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2012.2186771
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