Source-Free Cross-Domain State of Charge Estimation of Lithium-Ion Batteries at Different Ambient Temperatures
Abstract
Machine learning methods for state of charge (SOC) estimation of lithium-ion batteries (LiBs) face the problem of domain shift. Varying conditions, such as different ambient temperatures, can cause performance degradation of the estimators due to data distribution discrepancy. Some transfer learning methods have been utilized to tackle the problem. At real-time transfer, the source model is supposed to keep updating itself online. In the process, source domain data are usually absent because the storage and acquisition of all historical running data can involve violating the privacy of users. However, existing methods require coexistence of source and target samples. In this article, we discuss a more difficult yet more practical source-free setting where there are only the models pretrained in source domain and limited target data can be available. To address the challenges of the absence of source data and distribution discrepancy in cross-domain SOC estimation, we propose a novel source-free temperature transfer network (SFTTN), which can mitigate domain shift adaptively. In this article, cross-domain SOC estimation under source-free transfer setting is discussed for the first time. To this end, we define an effective approach named minimum estimation discrepancy (MED), which attempts to align domain distributions by minimizing the estimation discrepancy of target samples. Extensive transfer experiments and online testing at fixed and changing ambient temperatures are performed to verify the effectiveness of SFTTN. The experiment results indicate that SFTTN can achieve robust and accurate SOC estimation at different ambient temperatures under source-free scenario.
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Shen et al. developed the source-free temperature transfer network (SFTTN) framework for passive SOC estimation, leveraging pre-trained active models and target data through a 2D-CNN-BiLSTM network to overcome challenges of distribution differences and data scarcity in cross-domain scenarios.
“Shen et al. developed the source-free temperature transfer network (SFTTN) framework for passive SOC estimation, leveraging pre-trained active models and target data through a 2D-CNN-BiLSTM network to overcome challenges of distribution differences and data scarcity in cross-domain scenarios[119].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [119]
Extensive experiments confirmed that SFTTN outperforms or matches other methods in both fixed-temperature and cross-domain SOC estimation under varying ambient temperatures.
“Extensive experiments confirmed that SFTTN outperforms or matches other methods in both fixed-temperature and cross-domain SOC estimation under varying ambient temperatures.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [119]
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- Authors
- Shen, L., Li, J., Zuo, L., Zhu, L., & Shen, H. T.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Shandong Normal University (CN)
- Venue
- IEEE Transactions on Power Electronics
- Published
- 2023-06
- Volume
- 38
- Issue
- 6
- Pages
- 6851-6862
- ISSN
- 0885-8993 (ISSN-L)
- 1941-0107
- OpenAlex
- W4323022459
- Cited by
- 39
- References
- 55
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 15 · 2023: 5
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Power Electronics (publishedVersion)
- Funders
- National Natural Science Foundation of China (62176042)
- Natural Science Foundation of Sichuan, China (2023NSFSC0483)
- Fundamental Research Funds for the Central Universities (ZYGX2021YGCX016)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- state of charge estimation, lithium-ion batteries, cross-domain transfer learning, domain shift, ambient temperature variation, online learning, privacy-preserving estimation
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