A Hybrid Data-Driven Method to Predict Battery Capacity of Medical Devices and Analyze Component Effects

Abstract

Lithium-ion batteries are currently the most utilized power source in medical devices due to their long service life, high energy performance, and being portable. The performance of battery-powered medical devices is heavily dependent on battery capacity, which would be directly affected by related battery component parameters. To widen the application of battery-powered medical devices, it is vital to effectively monitor battery capacity and analyze the effects of battery component parameters. This article derives a hybrid data-driven method to achieve accurate early predictions of battery capacity and reliable analysis of battery component effects. To be specific, a Gaussian process regression-based data-driven model is first developed to efficiently capture the underlying fitting among four component parameters and battery capacity. Then two effect analysis tools including the automatic relevance determination kernel-based weights and tree-based local interpretable model-agnostic explanation are equipped to quantify and analyze both global and local effects of these four component parameters, respectively. Illustrative results show that the designed hybrid data-driven method is able to provide accurate battery capacity predictions with 0.97R2, while both global effects and local effects of four component parameters are successfully quantified. Due to the merits of data-driven characteristics, the designed hybrid data-driven method is capable of efficiently helping users to monitor/predict battery capacity and analyze/understand the effects of interested component parameters. This could further benefit battery-powered medical devices for higher-performance and longer-lifetime applications.