Hybrid-MPET: an open-source simulation software for hybrid electrode batteries

TL;DR

Hybrid-MPET is an open-source simulation software that models hybrid porous electrodes with multiple active materials, enabling efficient exploration of complex battery designs and accelerating development.

Contribution

The paper introduces Hybrid-MPET, a novel modular simulation framework capable of modeling multi-material hybrid electrodes at multiple scales, validated with real-world battery examples.

Findings

Validated models for Si-Gr anodes in EV batteries

Validated models for CFx-SVO cathodes in medical devices

Demonstrated rapid performance prediction across design parameters

Abstract

As the design of single-component battery electrodes has matured, the battery industry has turned to hybrid electrodes with blends of two or more active materials to enhance battery performance. Leveraging the best properties of each material while mitigating their drawbacks, multi-component hybrid electrodes open a vast new design space that could be most efficiently explored through simulations. In this article, we introduce a mathematical modeling framework and open-source battery simulation software package for Hybrid Multiphase Porous Electrode Theory (Hybrid-MPET), capable of accounting for the parallel reactions, phase transformations and multiscale heterogeneities in hybrid porous electrodes. Hybrid-MPET models can simulate both solid solution and multiphase active materials in hybrid electrodes at intra-particle and inter-particle scales. Its modular design also allows the combination of different active materials at any capacity fraction. To illustrate the novel features of Hybrid-MPET, we present experimentally validated models of silicon-graphite (Si-Gr) anodes used in electric vehicle batteries and carbon monofluoride (CFx) - silver vanadium oxide (SVO) cathodes used in implantable medical device batteries. The results demonstrate the potential of Hybrid-MPET models to accelerate the development of hybrid electrode batteries by providing fast predictions of their performance over a wide range of design parameters and operating protocols.