A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries
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All above-mentioned research works only focused on SOE estimation and did not involve prediction of voltage responses of batteries; in online SOE estimators, voltage responses were taken as input factors.
“Last but not least, unfortunately, all of the above-mentioned research works only focused on the estimat ion of SOE but did not involve the prediction of the voltage responses of batteries: in the online SOE estimators [15][16][17][18][19][20][21], the voltage responses were taken as input factors.”
From A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects (2018) · cites this paper as [16]
SOE estimation algorithms that extract the relationship between SOC and SOE had accuracy totally dependent on estimated SOC results, and their estimated SOE results were merely verified at load current no more than 1.0 C.
“However, the SOE estimat ion accuracy of these algorithms were totally dependent on the estimated SOC results, and the estimated SOE results were merely verified at load current of no mo re than 1.0 C.”
From A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects (2018) · cites this paper as [16]
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References 21 · Cited by 200
- Authors
- Wang, Y., Zhang, C., & Chen, Z.
- Affiliations
- University of Science and Technology of China (CN)
- Corresponding author
- Chen, Zonghai
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 135
- Pages
- 81-87
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2089118855
- Cited by
- 200
- References
- 21
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 8 · 2023: 14 · 2022: 19 · 2021: 14 · 2020: 21 · 2019: 33 · 2018: 13 · 2017: 23 · 2016: 22 · 2015: 19 · 2014: 1 · 2012: 1
- FWCI
- 26.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Natural Science Fund of China (61375079)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- state of charge estimation, state of energy estimation, lithium iron phosphate batteries, lithium-ion batteries, simultaneous estimation, particle filter, battery management systems, electric vehicles, dynamic temperature, measurement noise robustness
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