Data-Driven Models Using Impedance Spectroscopy for Predicting Degradation and Optimizing Electrochemical Systems
Authors: Murphy Lucas , Polglase Aidan , Janke Sophie 
Published: December 2024

As the demand for sustainable energy storage and conversion technologies grows, advanced modeling techniques are essential to optimize the performance, cost, and lifespan of electrochemical systems. Batteries, electrolyzers, and fuel cells experience complex degradation mechanisms, which traditional physics-based models struggle to predict due to the difficulty in measuring all necessary material parameters. Supporting industry partners for this work have identified gaps in plant-level design optimization models, where understanding potential failures and degradation of energy storage technologies is highly sought after. Data-driven models demonstrate the potential to provide elevated confidence to pursue projects with these emerging electrochemical storage technologies.

Data-driven models show promise in forecasting issues like battery capacity fade and voltage drop, but they typically require large historical datasets and continuous monitoring, which may not always be feasible. A novel method using Electrochemical Impedance Spectroscopy (EIS) overcomes this limitation, predicting lithium-ion battery degradation without prior cell cycling history or continuous monitoring. EIS measurements capture complex variables contributing to degradation, such as temperature distribution, catalyst agglomeration (in fuel cells and electrolyzers), and solid electrolyte interphase (SEI) growth (in batteries), which are difficult to measure directly. By combining EIS with data-driven approaches, patterns can be identified, providing detailed insights into system health and enabling real-time diagnostics and predictive maintenance, ultimately extending the lifespan of electrochemical devices. With insights into electrochemical degradation mechanisms, EIS interrogated features have proven to be strong indicators of future non-linear battery degradation. We have developed an EIS informed binary classification algorithm for sorting batteries based on their future rate of degradation that yields over 90% accuracy. Moving forward we look to apply these methods to other electrochemical systems as EIS shows promise as a rapid system assessment tool.

The HYER (HYdrogen Electrolyzer Research) project, focused on PEM electrolyzers, is currently developing proof-of-concept models, with full models to follow. Accelerated stress tests (ASTs) will evaluate electrolyzer cells and stacks while real-time EIS data is collected. Three machine learning models are being developed from this dataset using techniques such as autoencoders, neural networks, equivalent circuit modeling and time-series feature engineering: (1) a degradation forecasting model that doesn't rely on previous cell history, (2) a lower-resolution predictive tool for rapid prototyping, and (3) a high-resolution time-domain digital twin for capturing current and future cell behavior. These models combine equivalent circuit data from EIS with predictive analytics, offering valuable tools for researchers, OEM’s and operators.

This research significantly advances the operando understanding of electrochemical degradation across batteries and electrolyzers. It provides practical tools for real-time diagnostics, predictive modeling, and design optimization, enabling more efficient research and operations for industry and academia. These tools will drive the development of next-generation electrochemical devices while supporting better maintenance and design strategies.