The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Online capacity estimation of lithium-ion batteries with deep long short-term memory networks
Open accessAbstract
There is an increasing demand for modern diagnostic systems for batteries under real-world operation, specifically for the estimation of their state of health, for example, via their remaining capacity. The online estimation of the capacity of a cell is challenging due to the dynamic nature of cell aging and the limited variety of inputs available from a cell under operation. The scope of this work is the development of a data-driven capacity estimation model for cells under real-world working conditions with recurrent neural networks having long short-term memory capability. Voltage-time sensor data from the partial constant current phase charging curve is used as input, reflecting input availability in the real world. The network achieves a best-case mean absolute percentage error of 0.76% and is extremely robust while handling input noise. It also has the ability to handle variations in the length of the input time series and can generate a viable estimation even with an incomplete collection of input due to sensor errors. The model validation with several scenarios is done in a local embedded device, highlighting the use case of such models in future battery management systems.
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Cites
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Ageing mechanisms in lithium-ion batteries
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Degradation diagnostics for lithium ion cells
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“…experimental approach is the first method for SOH estimation, and this can be done using trivial methods such as coulomb counting or more sophisticated approaches such as electrochemical impedance spectroscopy (EIS) [4,5] and incremental capacity and differential voltage (IC/DV) analysis [6 – 9].”
MethodSelf-citationA review on the key issues of the lithium ion battery degradation among the whole life cycle
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“…experimental approach is the first method for SOH estimation, and this can be done using trivial methods such as coulomb counting or more sophisticated approaches such as electrochemical impedance spectroscopy (EIS) [4,5] and incremental capacity and differential voltage (IC/DV) analysis [6 – 9].”
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Production caused variation in capacity aging trend and correlation to initial cell performance
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Cited by
Wang, S., Fan, Y., Jin, S., Takyi-Aninakwa, P., & Fernandez, C. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108920 · Cited by 554
A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM
Ren, X., Liu, S., Yu, X., & Dong, X. (2021) · Energy · doi:10.1016/j.energy.2021.121236 · Cited by 375
Jiang, B., Zhu, J., Wang, X., Wei, X., Shang, W., & Dai, H. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119502 · Cited by 367
Ma, Y., Shan, C., Gao, J., & Chen, H. (2022) · Energy · doi:10.1016/j.energy.2022.123973 · Cited by 351
Wang, S., Jin, S., Bai, D., Fan, Y., Shi, H., & Fernandez, C. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.182 · Cited by 258
Metadata
- Authors
- Li, W., Sengupta, N., Dechent, P., Howey, D., Annaswamy, A., & Sauer, D. U.
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 482
- Pages
- 228863
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W3096459816
- Cited by
- 362
- References
- 15
- FWCI
- 17.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Li, W., Sengupta, N., Dechent, P., Howey, D., Annaswamy, A., & Sauer, D. U. (2021). Online capacity estimation of lithium-ion batteries with deep long short-term memory networks. Journal of Power Sources. https://doi.org/10.1016/j.jpowsour.2020.228863
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