The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Review on the Battery Model and SOC Estimation Method
Open accessAbstract
The accuracy of the power battery model and SOC estimation directly affects the vehicle energy management control strategy and the performance of the electric vehicle, which is of great significance to the efficient management of the battery and the improvement of the reliability of the vehicle. Based on the research of domestic and foreign battery models and the previous results of SOC estimation, this paper classifies power battery models into electrochemical mechanism models, equivalent circuit models and data-driven models. This paper analyzes the advantages and disadvantages of various battery models and current research progress. According to the choice of battery model, the previous research results of the power battery SOC estimation method are divided into three categories: the direct measurement method not based on battery model, the estimation method using black box battery model, and the battery model SOC estimation method based on state space. This paper will summarize and analyze the principles, applicable scenarios and research progress of the three categories of estimation algorithms aiming to provide references for future in-depth research. Finally, in view of the shortcomings of the battery model and estimation algorithm of the existing method, the future improvement direction is proposed.
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Cites
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“In order to meet the requirements of different discharge rates and real-time estimation of SOC, the P2D model is reasonably simplified to obtain other SP2D models [19–21].”
MethodInfluentialCritical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
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State-of-health estimation for the lithium-ion battery based on support vector regression
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State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network
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Cited by
Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review
Zhang, D., Zhong, C., Xu, P., & Tian, Y. (2022) · Machines · doi:10.3390/machines10100912 · Cited by 131
Rimsha, Murawwat, S., Gulzar, M. M., Alzahrani, A., Hafeez, G., Khan, F. A., & Abed, A. M. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108039 · Cited by 84
Yun, J., Choi, Y., Lee, J., Choi, S., & Shin, C. (2023) · IEEE Access · doi:10.1109/access.2023.3305950 · Cited by 79
State of health estimation of lithium-ion battery based on CNN–WNN–WLSTM
Yao, Q., Song, X., & Xie, W. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01300-3 · Cited by 53
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Metadata
- Authors
- Zhou, W., Zheng, Y., Pan, Z., & Lu, Q.
- Venue
- Processes
- Publisher
- MDPI AG
- Published
- 2021-09-20
- Volume
- 9
- Issue
- 9
- Pages
- 1685
- ISSN
- 2227-9717 (ISSN-L)
- OpenAlex
- W3201438206
- Cited by
- 394
- References
- 39
- FWCI
- 18.92
- Type
- journal article
- Language
- en
- Open access
- gold
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