An online model-based method for state of energy estimation of lithium-ion batteries using dual filters
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From 1.5-5 h when the cell was discharged at current rate as large as 3.0 C, the estimated SOE from the model in [35] deviated seriously from the actual one, while the SOE estimation of the proposed model was much closer to the actual one.
“Fro m 1.5-5 h when the cell was discharged at current rate of as large as 3.0 C, the estimated SOE fro m the model in [35] deviated seriously fro m the actual one, while the SOE estimat ion of the proposed model was much closer to the actual one.”
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 [35]
Compared to the model in [35], the electrochemical reactions in the battery at various operating conditions and especially the ageing levels were involved into the proposed model by accurately addressing the model parameter values with higher resolution.
“Co mpared to the model in [35], the electrochemical reactions in the battery at various operating conditions and especially the ageing levels were involved into the proposed model by accurately addressing the model parameter values with higher resolution.”
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 [35]
The MAPE and RMSPE of the proposed model were 9.2% and 4.7%, while those of the model in [35] were 26% and 10.9%.
“Specifically, the MAPE and RMSPE of the proposed model were 9.2% and 4.7%, wh ile those of the model in [35] were 26% and 10.9%, respectively.”
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 [35]
From 0-1.5 h when load current was moderate (within ±1.0 C), both SOE estimations could achieve acceptable results, and the proposed model performed slightly better than the model in [35].
“According to Fig. 26, fro m 0-1.5 h when the load current was moderate (within ±1.0 C), both the SOE estimation could achieve acceptable results, and our proposed model performed slightly better than the model in [35].”
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 [35]
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- Authors
- Dong, G., Chen, Z., Wei, J., Zhang, C., & Wang, P.
- Affiliations
- University of Science and Technology of China (CN)
- Corresponding author
- Chen, Zonghai
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 301
- Pages
- 277-286
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W1917365787
- Cited by
- 139
- References
- 27
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 12 · 2023: 13 · 2022: 12 · 2021: 19 · 2020: 14 · 2019: 17 · 2018: 15 · 2017: 11 · 2016: 10
- FWCI
- 12.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- lithium-ion batteries, state of energy estimation, dual filters, extended Kalman filter, particle filter, response surface model, temperature effects, discharge rate, lithium iron phosphate batteries, online parameter estimation, electric vehicles, energy storage systems
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