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<p>The shift towards Electric Vehicles (EVs) is transforming the transportation sector, with Lithium-ion batteries playing a crucial role due to their high power-to-weight ratio, energy efficiency, thermal stability, and minimal self-discharge. This research addresses the critical challenge of accurately estimating the state of charge (SOC) and state of health (SOH) of Li-Ion batteries, a key factor in optimizing battery management systems and enhancing overall performance. We assess various SOC estimation techniques and finds that the Adaptive Double Kalman Filter Method excels in delivering accurate combined SOC and SOH measurements. Our findings reveal a notable enhancement in estimation accuracy, achieving a 25% reduction in error compared to conventional methods. The results are supported by thorough hardware testing, confirming their practical applicability. Additionally, our method employs an IoT platform for real-time monitoring of SOC and SOH, with data securely stored in the cloud for future use. This research not only offers a reliable solution for battery management but also advances the field by integrating real-time data with comprehensive validation, establishing a new benchmark in the field.</p>

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Internet of Things Infused Vigilance of Li-Ion Battery Pack for Electric Vehicle Using Adaptive Kalman Approach

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Mitul M. Modi,
  • Rakesh A. Patel

摘要

The shift towards Electric Vehicles (EVs) is transforming the transportation sector, with Lithium-ion batteries playing a crucial role due to their high power-to-weight ratio, energy efficiency, thermal stability, and minimal self-discharge. This research addresses the critical challenge of accurately estimating the state of charge (SOC) and state of health (SOH) of Li-Ion batteries, a key factor in optimizing battery management systems and enhancing overall performance. We assess various SOC estimation techniques and finds that the Adaptive Double Kalman Filter Method excels in delivering accurate combined SOC and SOH measurements. Our findings reveal a notable enhancement in estimation accuracy, achieving a 25% reduction in error compared to conventional methods. The results are supported by thorough hardware testing, confirming their practical applicability. Additionally, our method employs an IoT platform for real-time monitoring of SOC and SOH, with data securely stored in the cloud for future use. This research not only offers a reliable solution for battery management but also advances the field by integrating real-time data with comprehensive validation, establishing a new benchmark in the field.