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<p>Reliable state-of-charge (SOC) estimation is vital for the safe and efficient operation of lithium-ion battery energy storage systems. However, accurately estimating the SOC poses significant challenges to various methods. In this work, a novel multi-measurement exponential gain unscented Kalman filter (MMEG-UKF) with temperature adaptability is proposed to achieve high-precision SOC estimation in lithium-ion batteries. In contrast to traditional methods, this advanced filter integrates multiple data sources, including current, voltage, and temperature, to provide a comprehensive view of battery charge. An innovative approach based on an MMEG factor dynamically adjusts the filter gain, enhancing estimation accuracy and stability even under rapidly changing conditions. Additionally, the temperature adaptability feature enables the filter to account for the complex impact of temperature variations on battery performance. Through comprehensive experimentation, the proposed method achieves error metrics consistently below 1.5%, underscoring its robustness and reliability across diverse operating conditions. These contributions lay a solid foundation for the efficient management of lithium-ion batteries in energy storage systems, setting a new framework in SOC estimation.</p>

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A multi-measurement exponential gain unscented Kalman filter-based state of charge estimation for lithium-ion batteries with temperature adaptability

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Mamadou Fall,
  • Chunmei Yu,
  • Paul Takyi-Aninakwa,
  • Shunli Wang,
  • Tofik Seid Ali,
  • Liya Zhang

摘要

Reliable state-of-charge (SOC) estimation is vital for the safe and efficient operation of lithium-ion battery energy storage systems. However, accurately estimating the SOC poses significant challenges to various methods. In this work, a novel multi-measurement exponential gain unscented Kalman filter (MMEG-UKF) with temperature adaptability is proposed to achieve high-precision SOC estimation in lithium-ion batteries. In contrast to traditional methods, this advanced filter integrates multiple data sources, including current, voltage, and temperature, to provide a comprehensive view of battery charge. An innovative approach based on an MMEG factor dynamically adjusts the filter gain, enhancing estimation accuracy and stability even under rapidly changing conditions. Additionally, the temperature adaptability feature enables the filter to account for the complex impact of temperature variations on battery performance. Through comprehensive experimentation, the proposed method achieves error metrics consistently below 1.5%, underscoring its robustness and reliability across diverse operating conditions. These contributions lay a solid foundation for the efficient management of lithium-ion batteries in energy storage systems, setting a new framework in SOC estimation.