A Hybrid Data-Driven Method to Predict Battery Capacity of Medical Devices and Analyze Component Effects - Fang, Run; Liao, Chengsheng; Quan, Hong; Zeng, Libo; Peng, Qiao (Frontiers in Energy Research, 2022)
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 analyse the effects of battery component parameters. This paper 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 analyse 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 0.97 R^2, while both global effects and local effects of four component parameters are successfully quantified. Due to the merits of data-driven characteristic, the designed hybrid data-driven method is capable of efficiently helping users to monitor/predict battery capacity and analyse/understand effects of interested component parameters. This could further benefit battery-powered medical device for higher-performance and longer-lifetime applications.