The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
Abstract
This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate (LiFePO4) and lithium titanate (LTO) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs' battery management system (BMS).
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Cites
Khaligh, A., & Li, Z. (2010) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2010.2047877 · Cited by 1,817
He, H., Xiong, R., Zhang, X., Sun, F., & Fan, J. (2011) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2011.2132812 · Cited by 825
State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF
Charkhgard, M., & Farrokhi, M. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2043035 · Cited by 779
Battery-Management System (BMS) and SOC Development for Electrical Vehicles
Cheng, K. W. E., Divakar, B. P., Wu, H., Ding, K., & Ho, H. F. (2011) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2010.2089647 · Cited by 692
Bhangu, B. S., Bentley, P., Stone, D. A., & Bingham, C. M. (2005) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2004.842461 · Cited by 507
Chen, Z., Fu, Y., & Mi, C. C. (2013) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2012.2235474 · Cited by 442
Estimation of Battery State of Health Using Probabilistic Neural Network
Lin, H.-T., Liang, T.-J., & Chen, S.-M. (2013) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2012.2222650 · Cited by 327
Kim, J., Shin, J., Chun, C., & Cho, B. H. (2012) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2011.2158553 · Cited by 278
Advancements in OCV Measurement and Analysis for Lithium-Ion Batteries
Petzl, M., & Danzer, M. A. (2013) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2013.2259490 · Cited by 217
Gould, C. R., Bingham, C. M., Stone, D. A., & Bentley, P. (2009) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2009.2028348 · Cited by 215
Cited by
State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
Yang, F., Li, W., Li, C., & Miao, Q. (2019) · Energy · doi:10.1016/j.energy.2019.03.059 · Cited by 543
Vidal, C., Malysz, P., Kollmeyer, P., & Emadi, A. (2020) · IEEE Access · doi:10.1109/access.2020.2980961 · Cited by 512
Feng, X., Weng, C., He, X., Han, X., Lu, L., Ren, D., & Ouyang, M. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2927120 · Cited by 505
Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Ayob, A., Saad, M. H. M., Karim, T. F., & How, D. N. T. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124110 · Cited by 378
Yang, N., Song, Z., Hofmann, H., & Sun, J. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2021.103857 · Cited by 276
Metadata
- Authors
- Chaoui, H., & Ibe-Ekeocha, C. C.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2017-10
- Volume
- 66
- Issue
- 10
- Pages
- 8773-8783
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2624661965
- Cited by
- 555
- References
- 28
- FWCI
- 21.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chaoui, H., & Ibe-Ekeocha, C. C. (2017). State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2017.2715333
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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