Researchers at the Pacific Northwest National Laboratory (PNNL) in the US have developed a new model to simulate the performance of flow battery systems.
The EZBattery model is a physics-based simulation tool that allows researchers to predict the performance of redox flow batteries and variants in under one second, said PNNL. This is without the need to have training datasets.
The model can predict how redox flow batteries will behave based on laboratory-scale experiments, it said.
In the past, experimenting on batteries took a lot of time, especially to understand a battery’s lifespan. This required viewing a lot of charge-discharge cycles. It also required trial and error to match the right materials.
But this tool allows researchers to more easily optimise battery designs and electrolyte chemistries without the need for extensive physical prototyping or weeks of experimentation, it said.
The model deals with the three main architectures of flow batteries:
- liquid electrolyte flow systems
- hybrid systems
- redox-targeting/redox-shuttling flow batteries.
The EZBattery model is more powerful because it can simulate individual batteries as well as large-scale systems. It can also deal with constant current and varying power operations, PNNL said.
Jie Bao, lead researcher and project lead, said: “This new model is like a superpower for energy storage researchers aiming to accelerate the development of energy storage technologies. To get the best performing long-duration energy storage system that will power our homes and the grid, we need to be able iterate quickly and efficiently.”
The model is publicly available on GitHub, with detailed tutorials for users, and an easy-to-use software which has input and output formats. Users can also calibrate the model with an automated process and control the start and stop operating time and state of charge of any given battery combinations.