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A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range

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  • Chen, Lei
  • Wang, Shunli
  • Jiang, Hong
  • Fernandez, Carlos

Abstract

Lithium-ion batteries are one of the best choices as energy storage devices for self-powered nodes in wireless sensor networks (WSN) due to their advantages of no memory effect, high energy density, long cycle life, and being pollution-free after being discarded, ensuring that the sensor nodes maintain high power operation for a long time. An improved co-estimation framework for SOE and maximum available energy has been established, considering the problem of maximum available energy decay caused by temperature and battery charge-discharge rate, which can update the maximum available energy in real-time and reduce SOE errors caused by fixed energy values. A multi-timescale SOE and maximum available energy co-estimation framework is proposed to address the asynchronous and coupled characteristics of maximum available energy and SOE estimation, effectively reducing the algorithm's computational complexity. The proposed algorithm is experimentally verified according to the designed dynamic stress test conditions of lithium-ion batteries in WSN nodes. The experimental results show that smaller time scales can provide more accurate and reliable maximum available energy correction and higher SOE estimation accuracy, but the computational time cost is higher than that of larger time scales. To balance SOE estimation accuracy and algorithm computational complexity, the appropriate time scale should be selected based on the SOE estimation accuracy and time cost in practical battery management system working conditions.

Suggested Citation

  • Chen, Lei & Wang, Shunli & Jiang, Hong & Fernandez, Carlos, 2024. "A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range," Applied Energy, Elsevier, vol. 355(C).
    • Handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923015891
    DOI: 10.1016/j.apenergy.2023.122225
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    References listed on IDEAS

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    1. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & He, Tingting & Wei, Yiying, 2016. "Novel methods for estimating lithium-ion battery state of energy and maximum available energy," Applied Energy, Elsevier, vol. 178(C), pages 1-8.
    2. Zhang, Xu & Wang, Yujie & Wu, Ji & Chen, Zonghai, 2018. "A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter," Applied Energy, Elsevier, vol. 216(C), pages 442-451.
    3. Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    4. Ghomian, Taher & Mehraeen, Shahab, 2019. "Survey of energy scavenging for wearable and implantable devices," Energy, Elsevier, vol. 178(C), pages 33-49.
    5. Wang, Yujie & Li, Mince & Chen, Zonghai, 2020. "Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation," Applied Energy, Elsevier, vol. 278(C).
    6. Liu, Chang & Wang, Yujie & Chen, Zonghai, 2019. "Degradation model and cycle life prediction for lithium-ion battery used in hybrid energy storage system," Energy, Elsevier, vol. 166(C), pages 796-806.
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    Citations

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    Cited by:

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    2. Liu, Ruixue & Jiang, Benben, 2025. "A multi-time-resolution attention-based interaction network for co-estimation of multiple battery states," Applied Energy, Elsevier, vol. 381(C).
    3. Lei Pei & Yuhong Wu & Xiaoling Shen & Cheng Yu & Zhuoran Wen & Tiansi Wang, 2025. "Energy State Estimation for Series-Connected Battery Packs Based on Online Curve Construction of Pack Comprehensive OCV," Energies, MDPI, vol. 18(7), pages 1-20, April.
    4. Zeng, Yi & Li, Yan & Zhou, Zhongkai & Zhao, Daduan & Yang, Tong & Ren, Pu & Zhang, Chenghui, 2025. "Joint estimation of state of charge and health utilizing fractional-order square-root cubature Kalman filtering with order scheduling strategy," Energy, Elsevier, vol. 320(C).
    5. Ruan, Guanqiang & Liu, Zixi & Cheng, Jinrun & Hu, Xing & Chen, Song & Liu, Shiwen & Guo, Yong & Yang, Kuo, 2024. "A deep learning model for predicting the state of energy in lithium-ion batteries based on magnetic field effects," Energy, Elsevier, vol. 304(C).
    6. Zou, Yuanru & Shi, Haotian & Cao, Wen & Wang, Shunli & Nie, Shiliang & Chen, Dan, 2025. "A high-speed recurrent state network with noise reduction for multi-temperature state of energy estimation of electric vehicles lithium-ion batteries," Energy, Elsevier, vol. 322(C).

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