A novel Gaussian model based battery state estimation approach: State-of-Energy
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Online SOE estimators using filters achieved acceptable results, but their intricate filtering algorithms produced heavy computational burdens for BMS microprocessors with limited computational capability.
“These online estimators achieved acceptable results, but the intricate filtering algorith m produced heavy computational burdens for the micro-processors in the BMSs, wh ich were always with limited co mputational capability.”
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 [19]
Due to omission of initial SOE, estimated SOE of online filters based on AUKF and dual filters remained diverged until 10 minutes after estimation started.
“However, due to the omission of the initial SOE, the estimated SOE of the online filters based on, for example AUKF [19,20] and dual filters [21], remained diverged until 10 minutes after the estimat ion started.”
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 [19]
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- Authors
- He, H., Zhang, Y., Xiong, R., & Wang, C.
- Affiliations
- Beijing Institute of Technology (CN)
- Corresponding author
- He, HongWen
- Xiong, Rui
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2015-08
- Volume
- 151
- Pages
- 41-48
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2012660988
- Cited by
- 127
- References
- 27
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 11 · 2023: 18 · 2022: 16 · 2021: 12 · 2020: 9 · 2019: 12 · 2018: 5 · 2017: 12 · 2016: 13 · 2015: 3
- FWCI
- 11.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science & Technology Pillar Program (2013BAG05B00)
- Beijing Institute of Technology Research Fund Program for Young Scholars
- Excellent young scholars Research Fund of Beijing Institute of Technology
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- battery management systems, electric vehicles, open-circuit voltage, genetic algorithm parameter selection, Akaike information criterion, central difference Kalman filter, lithium-ion batteries, LiFePO4, LiMn2O4
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