Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network
The hysteresis effect represents the difference in open circuit voltage (OCV) between the charge and discharge processes of batteries. An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of LiFePO4 batteries. However, the intricate influence of stat...
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Elsevier
2025-05-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825000102 |
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| author | Lisen Yan Jun Peng Zeyu Zhu Heng Li Zhiwu Huang Dirk Uwe Sauer Weihan Li |
| author_facet | Lisen Yan Jun Peng Zeyu Zhu Heng Li Zhiwu Huang Dirk Uwe Sauer Weihan Li |
| author_sort | Lisen Yan |
| collection | DOAJ |
| description | The hysteresis effect represents the difference in open circuit voltage (OCV) between the charge and discharge processes of batteries. An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of LiFePO4 batteries. However, the intricate influence of state-of-charge (SOC), temperature, and battery aging have posed significant challenges for hysteresis modeling, which have not been comprehensively considered in existing studies. This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions, addressing the intricate dependencies on SOC, temperature, and battery aging. First, a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths, temperatures and aging states. Second, the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability. The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information. Third, the conditional matrix, incorporating temperature, health state, and historical paths, is constructed to provide the scenario-specific information for the adversarial network, thereby enhancing the model’s adaptability. Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions, with accuracy improvements of 31.3–48.7% compared to three state-of-the-art models. |
| format | Article |
| id | doaj-art-d92b8eb90d7844ffaa225678ea0b876e |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-d92b8eb90d7844ffaa225678ea0b876e2025-02-06T05:12:53ZengElsevierEnergy and AI2666-54682025-05-0120100478Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial networkLisen Yan0Jun Peng1Zeyu Zhu2Heng Li3Zhiwu Huang4Dirk Uwe Sauer5Weihan Li6School of Computer Science and Engineering, Central South University, Changsha, 410083, China; Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, GermanySchool of Electronic Information, Central South University, Changsha, 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, 410083, ChinaSchool of Electronic Information, Central South University, Changsha, 410083, China; Corresponding authors.School of Automation, Central South University, Changsha, 410083, ChinaCenter for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, GermanyCenter for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany; Corresponding authors.The hysteresis effect represents the difference in open circuit voltage (OCV) between the charge and discharge processes of batteries. An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of LiFePO4 batteries. However, the intricate influence of state-of-charge (SOC), temperature, and battery aging have posed significant challenges for hysteresis modeling, which have not been comprehensively considered in existing studies. This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions, addressing the intricate dependencies on SOC, temperature, and battery aging. First, a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths, temperatures and aging states. Second, the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability. The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information. Third, the conditional matrix, incorporating temperature, health state, and historical paths, is constructed to provide the scenario-specific information for the adversarial network, thereby enhancing the model’s adaptability. Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions, with accuracy improvements of 31.3–48.7% compared to three state-of-the-art models.http://www.sciencedirect.com/science/article/pii/S2666546825000102Lithium iron phosphate (LFP) batteriesBattery modelingOpen circuit voltageHysteresis modelingData-driven |
| spellingShingle | Lisen Yan Jun Peng Zeyu Zhu Heng Li Zhiwu Huang Dirk Uwe Sauer Weihan Li Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network Energy and AI Lithium iron phosphate (LFP) batteries Battery modeling Open circuit voltage Hysteresis modeling Data-driven |
| title | Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network |
| title_full | Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network |
| title_fullStr | Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network |
| title_full_unstemmed | Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network |
| title_short | Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network |
| title_sort | data driven modeling of open circuit voltage hysteresis for lifepo4 batteries with conditional generative adversarial network |
| topic | Lithium iron phosphate (LFP) batteries Battery modeling Open circuit voltage Hysteresis modeling Data-driven |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825000102 |
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