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We present a Hybrid Multiphase Porous Electrode Theory (Hybrid-MPET) model that accurately predicts the performance of Medtronic's implantable medical device battery lithium/carbon monofluoride (C F x) - silver vanadium oxide (SVO) under both low-rate background monitoring and high-rate pulsing currents. The distinct properties of multiple active materials are reflected by parameterizing their thermodynamics, kinetics, and mass transport properties separately. Diffusion limitations of Li + in SVO are used to explain cell voltage transient behavior during pulse and post-pulse relaxation. We also introduce change in cathode electronic conductivity, Li metal anode surface morphology, and film resistance buildup to capture evolution of cell internal resistance throughout multi-year electrical tests. We share our insights on how the Li + redistribution process between active materials can restore pulse capability of the hybrid electrode, allow C F x to indirectly contribute to capacity release during pulsing, and affect the operation protocols and design principles of batteries with other hybrid electrodes. We also discuss additional complexities in porous electrode model parameterization and electrochemical characterization techniques due to parallel reactions and solid diffusion pathways across active materials. We hope our models can complement future experimental research and accelerate development of multi-active material electrodes with targeted performance. • Hybrid-MPET model of Medtronic's high-rate Li/CF x -SVO battery is presented. • Separate material properties, diffusion limitation, and aging are accounted for. • Model prediction accuracy is validated on large battery dataset. • Cell pulse capability restoration is explained by Li＋ redistribution across materials. • Li＋ redistribution impact on general cell operation and design principles are discussed. [ABSTRACT FROM AUTHOR]

Medtronic Implantable Cardioverter Defibrillators (ICDs) and Cardiac Resynchronization Therapy Defibrillators (CRT-Ds) rely on high-energy density, lithium batteries, which are manufactured with a special lithium/carbon monofluoride (C F x)–silver vanadium oxide (SVO) hybrid cathode design. Consistently high battery performance is crucial for this application, since poor performance may result in ineffective patient treatment, whereas early replacement may involve surgery and increase in maintenance costs. To evaluate performance, batteries are tested, both at the time of production and post-production, through periodic sampling carried out over multiple years. This considerable amount of experimental data is exploited for the first time in this work to develop a data-driven, machine learning approach, relying on Generalized Additive Models (GAMs) to predict battery performance, based on production data. GAMs combine prediction accuracy, which enables evaluation of battery performance immediately after production, with model interpretability, which provides clues on how to further improve battery design and production. Model interpretation allows to identify key features from the battery production data that offer physical insights to support future battery development, and foster the development of physics-based model for hybrid cathode batteries. The proposed approach is validated on 21 different datasets, targeting several performance-related features, and delivers consistently high prediction accuracy on test data. • ICD battery reliability is ensured with life-test experiments spanning multiple years. • ML provides accurate prediction of life-test experiments based on production data. • Interpretable ML fosters the development of battery design and physics-based models. • Approach is validated on 21 datasets, analysed for the first time in the literature. [ABSTRACT FROM AUTHOR]