An Arrhenius-Based Simulation Tool for Predicting Aging of Lithium Manganese Dioxide Primary Batteries in Implantable Medical Devices

This article presents a novel aging-coupled predictive thermo-electrical dynamic modeling tool tailored for primary lithium manganese dioxide ( L i - M n O 2 ) batteries in active implantable medical devices (AIMDs). The aging mechanisms of rechargeable lithium batteries are well documented using computationally intensive physics-based models, unsuitable for real-time onboard monitoring in AIMDs due to their high demands. There is a critical need for efficient, less demanding modeling tools for accurate battery health monitoring and end-of-life prediction as well as battery safety assessment in these devices. The presented model in this article simulates the battery terminal voltage, remaining capacity, temperature, and aging during active discharge, making it suitable for real-time health monitoring and end-of-life prediction. We incorporate a first-order dynamic for internal resistance growth, influenced by time, temperature, discharge depth, and load current. By adopting Arrhenius-type kinetics and polynomial relationships, this model effectively simulates the combined impact of these variables on battery aging under diverse operational conditions. The simulation handles both the continuous micro-ampere-level demands necessary for device housekeeping and periodic high-rate pulses needed for therapeutic functions, at a constant ambient temperature of 37 °C, mimicking human body conditions. Our findings reveal a gradual, nonlinear increase in internal resistance as the battery ages, rising by an order of magnitude over a period of 5 years. Sensitivity analysis shows that as the battery ages and load current increases, the terminal voltage becomes increasingly sensitive to internal resistance. Specifically, at defibrillation events, the ∂ V ∂ R trajectory dramatically increases from 10 − 12 to 10 − 8 , indicating a fourth-order-of-magnitude enhancement in sensitivity. A model verification against experimental data shows an R 2 value of 0.9506, indicating a high level of accuracy in predicting the L i - M n O 2 cell terminal voltage. This modeling tool offers a comprehensive framework for effectively monitoring and optimizing battery life in AIMDs, therefore enhancing patient safety.