The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A GRU-RNN based momentum optimized algorithm for SOC estimation
Abstract
Abstract For a lithium battery, a gated recurrent unit recurrent neural network (GRU-RNN) based momentum gradient method is investigated to estimate its state of charge (SOC). In the momentum gradient method, the current weight change direction takes a compromise of the gradient direction at current instant and at historical time to prevent the oscillation of the weight change and to improve the SOC estimation speed. The details include: (1) construct a GRU-RNN model for estimating SOC by taking the measured voltage and current as the inputs, and the estimated SOC as the output of the GRU-RNN; (2) to promote the SOC convergence speed, explore the momentum gradient algorithm to optimize the weights of the network by introducing a momentum term; (3) to prevent overfitting and to improve generalization ability of the GRU-RNN model, add noises to the sample data, so as to improve the SOC estimation accuracy; (4) set up a lithium battery test platform to sample data in battery discharge process and to implement MATLAB simulation. The simulation results verify that the momentum optimized GRU-RNN model can accurately and effectively estimate the SOC of the lithium battery.
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Cites
Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.001 · Cited by 2,005
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2787586 · Cited by 906
Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.06.104 · Cited by 818
State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
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The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer
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State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network
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Cited by
Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.113 · Cited by 441
Cui, Z., Wang, L., Li, Q., & Wang, K. (2022) · International Journal of Energy Research · doi:10.1002/er.7545 · Cited by 384
SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output
Chen, J., Zhang, Y., Wu, J., Cheng, W., & Zhu, Q. (2023) · Energy · doi:10.1016/j.energy.2022.125375 · Cited by 362
Hannan, M. A., How, D. N. T., Lipu, M. S. H., Mansor, M., Ker, P. J., Dong, Z. Y., Sahari, K. S. M., Tiong, S. K., Muttaqi, K. M., Mahlia, T. M. I., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98915-8 · Cited by 196
Wang, Y.-X., Chen, Z., & Zhang, W. (2022) · Energy · doi:10.1016/j.energy.2022.123178 · Cited by 166
Metadata
- Authors
- Jiao, M., Wang, D., & Qiu, J.
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 459
- Pages
- 228051
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W3012958665
- Cited by
- 320
- References
- 8
- FWCI
- 14.87
- Type
- journal article
- Language
- en
- Open access
- closed
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