The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems
Open accessAbstract
Abstract With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.
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Cited by
Nyamathulla, S., & Dhanamjayulu, C. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111179 · Cited by 518
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Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications
Yang, S., Zhang, C., Jiang, J., Zhang, W., Zhang, L., & Wang, Y. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.128015 · Cited by 435
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SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output
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Metadata
- Authors
- Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 131
- Pages
- 110015
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W3042713179
- Cited by
- 1,377
- References
- 40
- FWCI
- 57.91
- Type
- journal article
- Language
- en
- Open access
- green
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