An Accurate Smartphone Battery Parameter Calibration Using Unscented Kalman Filter

TL;DR

This paper demonstrates the use of Unscented Kalman Filter to effectively reduce noise in smartphone battery measurements, enhancing the accuracy of critical parameters like Voltage and State-of-Charge for IoT devices.

Contribution

It introduces a simple UKF-based method for noise mitigation in smartphone battery data, improving parameter estimation accuracy.

Findings

UKF reduced measurement noise effectively.

Achieved low MSE in voltage and SOC measurements.

Enhanced data quality for battery parameter forecasting.

Abstract

Internet of Things (IoT) applications have opened up numerous possibilities to improve our lives. Most of the remote devices, part of the IoT network, such as smartphones, data loggers and wireless sensors are battery powered. It is vital to collect battery measurement data (Voltage or State-of-Charge (SOC)) from these remote devices. Presence of noise in these measurements restricts effective utilization of this dataset. This paper presents the application of Unscented Kalman Filter (UKF) to mitigate measurement noise in smartphone dataset. The simplicity of this technique makes it a constructive approach for noise removal. The datasets obtained after noise removal could be used to improve data-driven time series forecasting models which aid to accurately estimate critical battery parameters such as SOC. UKF was tested on noisy charge and discharge dataset of a smartphone. An overall Mean Squared Error (MSE) of 0.0017 and 0.0010 was obtained for Voltage charge and discharge measurements. MSE for SOC charge and discharge data measurements were 0.0018 and 0.0010 respectively.