Opposites attract: Cyclone engineers team up to improve battery reliability and safety

Energy storage reliability and safety could be improved on everything from electric vehicles to wind turbines because of a research project involving an interdisciplinary team of Iowa State University engineers.

Chao Hu

Chao Hu, assistant professor of mechanical engineering (ME), is the principal investigator (PI) on a project titled “Physics-Based Probabilistic Prognostics for Battery Health Management.” Simon Laflamme, Waldo W. Wegner Professor in Civil Engineering, and Shan Hu, William and Virginia Binger Professor in Mechanical Engineering, are co-PI’s on the nearly $400,000 effort funded by National Science Foundation’s (NSF) Division of Electrical, Communications and Cyber Systems.

The objective of this project is to create a physics-based probabilistic prognostics platform for lithium-ion (Li-ion) batteries, according to Chao Hu. This platform will offer new methods and tools for making a probabilistic prediction of the end-of-life of a battery cell based on rapid degradation inference from noisy voltage and current measurements.

This current effort builds, in part, upon another NSF project Chao Hu and Shan Hu (no relation) collaborated on titled “Data-Driven Dynamic Reliability Assessment of Lithium-Ion Battery Considering Degradation Mechanisms.” The aim of this project, which started in 2016 and will continue through July 2020, is to create an ensemble prognostics framework through exploring multi-physics simulation and data-driven learning.

“In our previous NSF project, the data-driven approach has been proven effective in estimating lithium-ion battery’s state of health,” said Shan Hu. “However, these data-drive methods are data hungry. They need lots of data to make right estimations. In the cases where only limited data is available, knowledge about physics and chemistries underpinning a battery’s charging and discharging processes can make up for the insufficiency of data and enable us to still effectively predict the end-of-life of the battery.”

Shan Hu

For the most recent project, Chao Hu and Shan Hu will integrate physical knowledge of battery degradation with probabilistic machine learning models to enable remaining useful life prediction.

“One potential benefit of incorporating physics is the accuracy improvement in the prediction of the long-term degradation in the early lifetime over no use of physics,” said Chao Hu. “This new capability could help accelerate the processes of battery materials design for materials scientists, and battery supplier selection for end users.”

Laflamme will lend his expertise to this project from a civil engineering perspective by extending methods and concepts from the field of structural health monitoring to the problem of battery health prognostics.

“Research discoveries on condition assessment and reliability forecast of civil structures will be extended to our research problem, with particular attention to physics-informed model enabling the formulation of efficient adaptive representation to learn and forecast battery behavior,” Laflamme said.

The team has also been collaborating with Medtronic, a medical device company based in Minneapolis, Minnesota. This collaboration will allow the project team to validate the developed models and tools using long-term cycling data acquired from commercial implantable battery cells.

Simon Laflamme

“This research, if successful, will produce major advancements in extending battery life while ensuring battery safety,” said Chao Hu. “Advances in energy storage management could reduce the costs and promote the wide-scale adoption of hybrid and electric vehicles and renewable energy sources, which in turn will reduce the dependence of our nation on foreign sources of energy and also reduce harmful carbon emissions.”

Ensuring reliable and safe operation of battery energy storage systems in the power grid is crucial for renewable energy integration, according to Chao Hu. These improved battery energy storage systems can benefit industries in Iowa, such as renewable energy and agriculture.

“The deployment of these battery systems for enhancing renewable penetration has become an increasingly important issue in Iowa,” said Chao Hu. “Additionally, having reliable and safe power sources will promote the widespread deployment of electrically-driven farm equipment, thus having a positive impact on the agriculture industry.”

In addition to the faculty and industry researchers, this project will also utilize student support. The team aims to engage at least one female undergraduate student and at least two undergraduates from underrepresented groups. These undergraduate students will work closely with the graduate students and the PI’s to plan and execute the battery cycling tests. The team also aims to engage in at least one K-12 outreach program to help promote their research.

Work on this project started earlier this month and funding will continue through May 2023.

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