Smartphone batteries age over the years, leading to a decrease in cordless operation time and increased heat generation, and thus compromising user experience. Yet, Android users do not have any possibility to quantify the state of health of their batteries. Here we introduce an Android app that monitors capacity loss and internal resistance increase of the smart phone battery using cloud-based data analysis during regular operation.

The app is based on a novel algorithm for battery state diagnosis using voltage-controlled models (VCM) [1-3]. To perform this state diagnosis, the algorithm requires voltage, current and temperature data. This data is recorded from the Android phone. The algorithm uses measured cell voltage as input to an equivalent circuit model, resulting in simulated cell state of charge (SOC) and current as output. After an equivalent full cycle, the app sends the measured data (voltage, current, temperature) to an Influx data base running on a server. The server uses the data to determine the state of health (SOH) in terms of both capacity loss and internal resistance increase [3]. These results are sent back to the app and displayed to the user. The VCM algorithm is truly operando (working with arbitrary load profiles) and computationally efficient (no observers, filters or neural networks required), allowing its use on the server with little computational resources for a large number of devices.

We show operando results from over 2 years of operation of several Android devices, including smart phones and tablets. Exemplary results are shown in the attached figures. Specific challenges observed in data analysis include the handling of Android’s doze mode (providing only limited amount of measurement data), gaps in the data (e.g., when the phone is switched off) as well as missing independent information on the cell (e.g., chemistry and cut-off voltages). After adapting the algorithm accordingly, we were able to demonstrate a reliable long-term operation of the app.

[1] J. A. Braun, R. Behmann, D. Schmider, and W. G. Bessler, „State of charge and state of health diagnosis of batteries with voltage-controlled models“, J. Power Sources 544, 231828 (2022), https://doi.org/10.1016/j.jpowsour.2022.231828.
[2] J. A. Braun, R. Behmann, D. Chabrol, F. Fuchs, W. G. Bessler, „Single-cell operando SOC and SOH diagnosis in a 24 V lithium iron phosphate battery with a voltage-controlled model“, J. Energy Storage 85, 110986 (2024), https://doi.org/10.1016/j.est.2024.110986.
[3] W. G. Bessler, „Capacity and Resistance Diagnosis of Batteries with Voltage-Controlled Models”, J. Electrochem. Soc. 171, 080510 (2024), https://doi.org/10.1149/1945-7111/ad6938.