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Accelerated Aging Test Method Incorporating Load Profiles for Primary Lithium Batteries in Active Implantable Medical Devices

Catalog of Regulatory Science Tools to Help Assess New Medical Devices 

Technical Description

Primary lithium batteries used in AIMDs experience unique operational demands during high-energy therapeutic pulses like defibrillation shocks, for example those used by Implantable Cardioverter Defibrillators (ICDs). The test protocol uses controlled, repeated high-energy pulsation to accelerate battery degradation in a manner representing typical ICD load profiles, which may help device developers evaluate how electrical load stress impacts battery internal resistance and remaining capacity. The provided computational model employs an Arrhenius-based dynamic aging approach with a coupled electro-thermal framework that estimates battery response under applied pulse profiles [3]. The model calculates increases in internal resistance as functions of time, battery temperature, and discharge depth, enabling users to simulate battery discharge profiles to characterize battery aging.

Intended Purpose

This tool can be used to estimate capacity loss in primary lithium batteries over time through accelerated bench testing. While traditional methods only apply elevated temperature to produce accelerated battery aging effects, this test method captures additional aging contributions caused by rapid, high-energy pulsation that would be experienced during typical use for some AIMDs including, but not limited to, ICDs. By incorporating actual AIMD load profiles and conditions in the test protocol, this tool may aid estimation of battery longevity by evaluating capacity loss and increased internal resistance over time under bench test conditions representing real-world conditions. This tool may help inform characterization and selection of batteries to meet device energy delivery needs and assist estimation of expected battery longevity over device service life.

Testing

  • The pulse-induced accelerated aging test protocol was applied to fifteen commercial-grade LiMnO2 cells with similar capacity and chemistry to medical-grade lithium batteries used in AIMDs [2] . Fifteen fresh cells were divided into three groups of five, installed in a commercial temperature-controlled battery test chamber.

Each group undergoes an aging protocol across five different pulse amplitude and duration combinations.

 Aging protocol subjects the cells  to repeated 72 high-energy pulses (30–32 J) with varying current (50–400 mA) and pulse widths (25–200 s), simulating 6 years of ICD defibrillation pulses. Battery capacity loss was measured by comparing capacity after constant-current, constant-voltage (CCCV) discharge to pre-test capacity measurements. Despite maintaining constant pulse energy, increasing discharge pulse amplitude led to nonlinear rises in capacity fade and internal resistance, with up to 44% capacity loss and 95% resistance increase at the highest tested C-rate.

The developed thermally coupled dynamic aging simulation model was used to determine suitable test conditions for the specific batteries under test, and simulation results corroborated experimental findings, showing sharp resistance increases with higher pulse amplitudes. [1] 

Limitations

  • The tool does not establish test acceptance criteria or minimum battery longevity requirements.
  • The tool was only assessed using commercial-grade LiMnO2 cells, not medical-grade implantable batteries typically used in AIMDs. Users should evaluate suitability of this tool for batteries with different chemistries or discharge profiles.
  • The provided test discharge pulse conditions may not represent all devices, and suitability of the method with different pulse conditions has not been assessed.
  • The underlying computational model assumes certain aging mechanisms and behavior consistent with experimental observations in LiMnO2 batteries as described in [1]. Suitability for batteries that may exhibit different behaviors or dominant aging mechanisms was not assessed.

Supporting Documentation

Supporting code for data post-processing and analysis is available here: https://github.com/dbp-osel/AIMD-Batteries-Accelerated-Aging-Test-Method

A comprehensive explanation of the simulation aging model design, modeling assumptions, and result validation is available here:

[1] Doosthosseini M, Khajeh Talkhoncheh M,Silberberg JL, Ghods H, “An Arrhenius-Based Simulation Tool for Predicting Aging of Lithium Manganese Dioxide Primary Batteries in Implantable Medical Devices”, Energies 17(21), 2024. https://doi.org/10.3390/en17215392 

Development of the test method and protocol is described here: 

[2] Doosthosseini M, Ghods H, “Pulse-induced Capacity Degradation Identification with an Accelerated Aging Test Protocol for Non-rechargeable Lithium Batteries in Active Implantable Medical Devices”, Proceedings of the 2026 Design of Medical Devices Conference,  V001T01A008, 2026. https://doi.org/10.1115/DMD2026-1047 

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