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Real-time state-of-charge estimation for rechargeable batteries based on in-situ ultrasound-based battery health monitoring and extended Kalman filtering model

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Listed:
  • Yang, Fan
  • Mao, Qian
  • Zhang, Jiaming
  • Hou, Shilin
  • Bao, Guocui
  • Cheng, Ka-wai Eric
  • Dai, Jiyan
  • Lam, Kwok-Ho

Abstract

Ultrasonic testing has emerged as a crucial non-invasive method for monitoring battery health, particularly for accurate State-of-Charge (SoC) estimation in Battery Management Systems (BMS). Unlike invasive methods relying on real-time collection of battery current and voltage, ultrasonic inspection offers timely feedback without interfering with battery properties. However, challenges remain in accurately estimating SoC during rechargeable battery discharging due to ultrasonic echo interference. This study presents an ultrasound-based in-situ rechargeable battery health monitoring system, incorporating advanced signal processing techniques. The proposed Ultrasonic Signal Empirical Mode Decomposition-Extended Kalman Filtering (USED-EKF) algorithm, based on Biot's theory, achieves real-time SoC estimation with exceptional accuracy (maximum error 0.63 %). Compared to conventional EKF, USED-EKF outperforms with significantly lower errors under constant current conditions. Additionally, our model enables the detection of overcharged batteries using ultrasound echo for the first time. This research demonstrates the potential of ultrasonic testing in cost-effective battery maintenance and explosion prevention, contributing to advancements in battery monitoring and safety measures. This research showcases the potential of ultrasonic testing as a cost-effective tool for battery maintenance and the prevention of battery explosions. The achieved results position our study as a pivotal driver in expediting these critical processes, highlighting the significance of our proposed model in advancing battery monitoring and safety measures.

Suggested Citation

  • Yang, Fan & Mao, Qian & Zhang, Jiaming & Hou, Shilin & Bao, Guocui & Cheng, Ka-wai Eric & Dai, Jiyan & Lam, Kwok-Ho, 2025. "Real-time state-of-charge estimation for rechargeable batteries based on in-situ ultrasound-based battery health monitoring and extended Kalman filtering model," Applied Energy, Elsevier, vol. 381(C).
    • Handle: RePEc:eee:appene:v:381:y:2025:i:c:s0306261924025455
    DOI: 10.1016/j.apenergy.2024.125161
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    References listed on IDEAS

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    1. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    2. Ren, Dongsheng & Feng, Xuning & Lu, Languang & He, Xiangming & Ouyang, Minggao, 2019. "Overcharge behaviors and failure mechanism of lithium-ion batteries under different test conditions," Applied Energy, Elsevier, vol. 250(C), pages 323-332.
    3. Chou, Yi-Sin & Hsu, Ning-Yih & Jeng, King-Tsai & Chen, Kuan-Hsiang & Yen, Shi-Chern, 2016. "A novel ultrasonic velocity sensing approach to monitoring state of charge of vanadium redox flow battery," Applied Energy, Elsevier, vol. 182(C), pages 253-259.
    4. Xiaoyu Li & Chuxin Wu & Chen Fu & Shanpu Zheng & Jindong Tian, 2022. "State Characterization of Lithium-Ion Battery Based on Ultrasonic Guided Wave Scanning," Energies, MDPI, vol. 15(16), pages 1-19, August.
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