The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network
Open accessAbstract
Implementing carbon neutrality and emission peak policies requires a high-level electric vehicle field. Lithium-ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium-ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium-ion batteries using neural network methods, the methods are classified into three categories: feed-forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next-generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles.
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Self-citationState-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
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Li, Q., Li, D., Zhao, K., Wang, L., & Wang, K. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104215 · Cited by 331
Self-citationReview of battery state estimation methods for electric vehicles - Part I: SOC estimation
Demirci, O., Taskin, S., Schaltz, E., & Acar Demirci, B. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111435 · Cited by 312
Liu, Y., Wang, L., Li, D., & Wang, K. (2023) · Protection and Control of Modern Power Systems · doi:10.1186/s41601-023-00314-w · Cited by 279
Self-citationHou, J., Xu, J., Lin, C., Jiang, D., & Mei, X. (2024) · Energy · doi:10.1016/j.energy.2023.130056 · Cited by 162
Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview
Eleftheriadis, P., Giazitzis, S., Leva, S., & Ogliari, E. (2023) · Forecasting · doi:10.3390/forecast5030032 · Cited by 59
Metadata
- Authors
- Cui, Z., Wang, L., Li, Q., & Wang, K.
- Venue
- International Journal of Energy Research
- Publisher
- Wiley
- Published
- 2021-12-16
- Volume
- 46
- Issue
- 5
- Pages
- 5423-5440
- ISSN
- 0363-907X (ISSN-L)
- 1099-114X
- OpenAlex
- W4200107191
- Cited by
- 384
- References
- 95
- FWCI
- 19.88
- Type
- journal article
- Language
- en
- Open access
- bronze
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