# A Battery-Aware Sensor Fusion Strategy: Unifying Magnetic-Inertial Attitude and Power for Energy-Constrained Motion Systems

## TL;DR

This paper introduces a new sensor fusion strategy that combines attitude estimation and battery state tracking in energy-limited devices.

## Contribution

The novel contribution is integrating battery state of charge as a state variable within an extended Kalman filter for attitude estimation.

## Key findings

- SOC estimation errors remain below 8% under tested load conditions.
- Incorporating SOC estimation does not degrade attitude estimation performance.
- The approach is suitable for wearable and small autonomous devices with low power consumption (~0.1 W).

## Abstract

Extended Kalman Filters (EKFs) are widely employed for attitude estimation using Magnetic and Inertial Measurement Units (MIMUs) in battery-powered sensing systems. In such applications, energy availability influences system operation, yet battery state information is commonly treated by external supervisory mechanisms rather than being integrated into the estimation process. This work presents an EKF-based formulation in which the battery State of Charge (SOC) is explicitly included as a state variable, allowing joint estimation of attitude and energy state within a single filtering framework. SOC dynamics are modeled using a low-complexity estimator based on terminal voltage and current measurements, while attitude estimation is performed using a Simplified Extended Kalman Filter (SEKF) tailored for embedded MIMU-based applications. The proposed approach was evaluated through numerical simulations under constant and time-varying load profiles representative of low-power electronic devices. The results indicate that the inclusion of SOC estimation does not affect the attitude estimation performance of the original SEKF, while SOC estimation errors remain below 8% for the evaluated load conditions with power consumption of approximately 0.1 W, consistent with wearable and small autonomous electronic platforms. By incorporating energy state estimation directly into the filtering structure, rather than treating it as an external supervisory task, the proposed formulation offers a unified estimation approach suitable for embedded MIMU-based systems with limited computational and energy resources.