Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm
Open accessAbstract
Conducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation research. However, the relationships between variables in lithium-ion battery datasets are mostly nonlinear, and a single data-driven algorithm is susceptible to a weak generalization ability affected by the dataset itself. Meanwhile, most of the related studies on battery health estimation are offline estimation, and the inability for online estimation is also a problem to be solved. In this study, an integrated learning method based on a stacking algorithm is proposed. In this study, the end voltage and discharge temperature were selected as the characteristics based on the sample data of NASA batteries, and the B0005 battery was used as the training set. After training on the dataset and parameter optimization using a Bayesian algorithm, the trained model was used to predict the SOH of B0007 and B0018 models. After comparative analysis, it was found that the prediction results obtained based on the proposed model not only have high accuracy and a short running time, but also have a strong generalization ability, which has a great potential to achieve online estimation.
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ML models are most effective in scenarios where the battery operates under condition changes because they can describe relationships between variables in detail.
“ML models are most effective in scenarios where the battery operates under condition changes, because it can describe relationships between variables in detail, [42].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [42]
The Introduction (page unavailable) says data-driven methods can adapt to uncertainty and nonlinear battery behavior and map measured features to state of charge. It does not establish that ML models are most effective when operating conditions change, or that describing variable relationships in detail is the reason for such superiority. (LLM, conf=0.83)
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Cited by
Sorted by citations
Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement
Rehman, A. (2024) · Power System Technology · doi:10.52783/pst.906 · Cited by 12 · see citing works
- Authors
- Dong, Y., Chen, K., Zhang, G., & Li, R.
- Affiliations
- Harbin University of Science and Technology (CN)
- Corresponding author
- Zhang, Guiling
- Li, Ran
- Venue
- World Electric Vehicle Journal
- Publisher
- MDPI AG
- Published
- 2024-02-20
- Volume
- 15
- Issue
- 3
- Pages
- 75
- ISSN
- 2032-6653 (ISSN-L)
- OpenAlex
- W4391972313
- Cited by
- 11
- References
- 0
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 2
- FWCI
- 1.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- World Electric Vehicle Journal (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Heilongjiang Provincial Natural Science Foundation of China (ZD2023B001)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Keywords
- lithium-ion batteries, state of charge estimation, state of health estimation, stacking ensemble, battery degradation, discharge temperature, Bayesian hyperparameter optimization, battery cycle life, NASA battery dataset
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