A high-fidelity hybrid lithium-ion battery model for SOE and runtime prediction
Abstract
The state of energy (SOE) of Li-ion batteries is a key indicator for the energy optimization and management of energy storage devices (ESDs) in electric vehicles (EVs) and smart grids. To improve the SOE estimation accuracy, a hybrid Li-ion battery model is presented in this study against the dynamic loads and the rate energy effects of the battery. Firstly, in order to take the advantages of both models, a 2ndorder RC battery model is merged with an analytical kinetic battery model for accurately predicting the battery voltage characteristics and capturing the nonlinear rate energy effects to realize the high-fidelity SOE and runtime prediction. Secondly, a new method to separate the fast and slow dynamics of the 2ndorder RC model is developed and presented with high-performance accuracy. Thirdly, commercial Li-ion batteries are tested at dynamic loads under various temperature to validate the effectiveness of the proposed model. The experimental results show high accuracy and reliability of the proposed battery model on the estimation of the battery SOE and the battery terminal voltage responses under dynamic loads.
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Li-ion rechargeable batteries are the most promising energy storage solution due to high gravimetric and volumetric energy density, great cycle life, enhanced safety and low self-discharge rate.
“A mong all the energy storage techniques, Li-ion based rechargeable batteries are deemed to be the most promising solution, due to their outperformed perfo rmance such as high gravimetric and volumetric energy density, great cycle life, enhanced safety and low self-discharge rate [1][2][3].”
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 [3]
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- Authors
- Li, K., Soong, B. H., & Tseng, K. J.
- Affiliations
- Nanyang Technological University (SG)
- Singapore Institute of Technology (SG)
- Venue
- 2017 IEEE Applied Power Electronics Conference and Exposition (APEC)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 2374-2381
- ISSN
- 1048-2334 (ISSN-L)
- OpenAlex
- W2616043255
- Cited by
- 12
- References
- 20
- Citations by year
- 2024: 2 · 2023: 2 · 2022: 2 · 2021: 2 · 2019: 2 · 2017: 2
- FWCI
- 1.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Conference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition (publishedVersion)
- Funders
- National Research Foundation Singapore
- 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
- lithium-ion battery modeling, state of energy estimation, execution time prediction, dynamic loading, battery terminal voltage, electric vehicles, energy storage devices, temperature effects
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