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This study integrates battery State of Charge (SOC) estimation into Extended Kalman Filters (EKFs) for attitude estimation in sensor systems. This unified approach accurately estimates both attitude and energy levels without compromising performance.

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Kalman filter couplingefficient consumptionenergy-aware orientationpower-aware motion monitoringstate of charge estimation

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Area of Science:

  • Embedded Systems
  • Sensor Fusion
  • Energy Harvesting

Background:

  • Extended Kalman Filters (EKFs) are crucial for attitude estimation in battery-powered sensing systems using Magnetic and Inertial Measurement Units (MIMUs).
  • Current methods often manage battery state externally, separating it from the core estimation process.

Purpose of the Study:

  • To develop an EKF-based formulation that jointly estimates attitude and battery State of Charge (SOC) within a single filtering framework.
  • To enable integrated energy-aware attitude estimation for resource-constrained embedded systems.

Main Methods:

  • Incorporated battery SOC as an explicit state variable within the EKF.
  • Modeled SOC dynamics using a low-complexity estimator based on terminal voltage and current.
  • Employed a Simplified Extended Kalman Filter (SEKF) for attitude estimation tailored for MIMU applications.

Main Results:

  • Joint SOC and attitude estimation did not degrade the performance of the original SEKF attitude estimation.
  • Achieved SOC estimation errors below 8% under various load profiles (approx. 0.1 W power consumption).
  • Demonstrated suitability for low-power electronic devices like wearables and small autonomous platforms.

Conclusions:

  • The proposed unified EKF formulation effectively integrates attitude and energy state estimation.
  • This approach is suitable for embedded MIMU-based systems with limited computational and energy resources.
  • Directly incorporating energy state into the filtering structure offers a more efficient and unified solution.