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<p>Accurate estimation of the State of Charge (SOC) in lithium-ion battery (LiB) packs is challenging due to nonlinear behavior and varying operating conditions. In this work a laboratory prototype hardware setup is developed to gather the real-time data. A 6s1p battery pack along with a charger for charging and a DC motor as load for discharging is used as a hardware setup at two different ambient temperatures of 298 K and 288 K. The Internet Of Things(IoT) based system is used with the help of the ESP32 Microcontroller for efficient monitoring of voltage and SOC values. SOC estimation has been performed using three different methods namely Particle Filter (PF), Wavelet Transform (WT), and a hybrid PF–WT approach with real-time dataset. A microcontroller based monitoring system of current and voltage is used for estimation. Considering both charging and discharging the hybrid method’s performance surpasses that of the other two methods. AT 298 K the hybrid method evaluates to a Root Mean Square Error (RMSE) of 1.248% and a Mean Absolute Percentage Error (MAPE) of 2.468%, which is the best result. The paper aims to ensure the safety of the vehicle and its occupants through intelligent charge estimation and IoT-based monitoring within a unified framework. This results in a scalable Battery Management System(BMS) solution that meets the evolving demands of electric mobility. With its focus on performance, safety, and durability, this initiative represents a meaningful step towards advancing sustainable EV technologies.</p>

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IoT-based real-time state of charge estimation of lithium-ion battery using a hybrid particle filter–wavelet transform

  • 期刊论文

  • 发表日期：

    2026年4月14日

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  • Chiranjib Mukherjee,
  • Shivanshu Kumar,
  • Somyaranjan Majhi,
  • Saral Gupta,
  • Anshita Kumari,
  • Amalendu Bikash Choudhury

摘要

Accurate estimation of the State of Charge (SOC) in lithium-ion battery (LiB) packs is challenging due to nonlinear behavior and varying operating conditions. In this work a laboratory prototype hardware setup is developed to gather the real-time data. A 6s1p battery pack along with a charger for charging and a DC motor as load for discharging is used as a hardware setup at two different ambient temperatures of 298 K and 288 K. The Internet Of Things(IoT) based system is used with the help of the ESP32 Microcontroller for efficient monitoring of voltage and SOC values. SOC estimation has been performed using three different methods namely Particle Filter (PF), Wavelet Transform (WT), and a hybrid PF–WT approach with real-time dataset. A microcontroller based monitoring system of current and voltage is used for estimation. Considering both charging and discharging the hybrid method’s performance surpasses that of the other two methods. AT 298 K the hybrid method evaluates to a Root Mean Square Error (RMSE) of 1.248% and a Mean Absolute Percentage Error (MAPE) of 2.468%, which is the best result. The paper aims to ensure the safety of the vehicle and its occupants through intelligent charge estimation and IoT-based monitoring within a unified framework. This results in a scalable Battery Management System(BMS) solution that meets the evolving demands of electric mobility. With its focus on performance, safety, and durability, this initiative represents a meaningful step towards advancing sustainable EV technologies.