The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation
Open accessAbstract
Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.
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Cites
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Cited by
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,377
Shrivastava, P., Soon, T. K., Idris, M. Y. I. B., & Mekhilef, S. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.06.040 · Cited by 704
Towards the future of smart electric vehicles: Digital twin technology
Bhatti, G., Mohan, H., & Raja Singh, R. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110801 · Cited by 533
Review on the Battery Model and SOC Estimation Method
Zhou, W., Zheng, Y., Pan, Z., & Lu, Q. (2021) · Processes · doi:10.3390/pr9091685 · Cited by 394
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
Metadata
- Authors
- Ali, M. U., Zafar, A., Nengroo, S. H., Hussain, S., Junaid Alvi, M., & Kim, H.-J.
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2019-01-30
- Volume
- 12
- Issue
- 3
- Pages
- 446
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2912511381
- Cited by
- 442
- References
- 28
- FWCI
- 21.86
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ali, M. U., Zafar, A., Nengroo, S. H., Hussain, S., Junaid Alvi, M., & Kim, H.-J. (2019). Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation. Energies. https://doi.org/10.3390/en12030446
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