Accelerated Validation of New Materials and Technologies
One of the greatest challenges in energy storage is the long horizon required for the optimization and implementation of new materials and concepts at the commercial level. This is due to the massive multidimensional optimization parameter spaces as well as the long time required to test the performance lifetime of a given set of parameters. StorageX tackles this problem by bringing together faculty from materials science and data science to address the two major bottlenecks in battery development: navigating massive multidimensional optimization parameter spaces, and testing the performance lifetime of a given set of parameters.
Performance Prediction
Using advanced machine learning methods on a wide range of data streams, we look for diagnostic features that predict battery lifetime under a wide range of use cases and for a wide range of different battery chemistries. Aside from accelerating cell testing during development and optimization, these capabilities allow for:
- Adaptive charging algorithms to minimize certain types of degradation based on how and where a battery is used
- Grading batteries for second life applications
- Extending the warranty of commercial cells and packs
- Pricing and warrantying used electric vehicle battery packs for second-life applications
StorageX supports several projects aimed at developing this capability at the cell, module, and pack level involving faculty in Materials Science, Systems Engineering, and Business.
Accelerated Optimization
We develop advanced experimental design and machine learning algorithms as well as physical infrastructure to automate and accelerate the development and testing processes. This includes efficiently utilizing hundreds of parallel battery testing channels and leveraging performance prediction models to rapidly navigate massive complex parameter spaces.
Researchers
- Associate Professor of Energy Science Engineering, at the Stanford Doerr School of Sustainability and Senior Fellow at the Precourt Institute for Energy
- K. K. Lee Professor and Professor, by courtesy, of Materials Science and Engineering and of Chemistry
- Precourt Family Professor, Professor of Energy Science Engineering and Senior Fellow at the Precourt Institute for Energy and at the Woods Institute for the Environment
- Professor of Aeronautics and Astronautics
- Managing Director, SECA - Stanford Energy Corporate Affiliates
- Associate Professor of Materials Science and Engineering, of Energy Science and Engineering and Senior Fellow at the Precourt Institute for Energy
- Director, Precourt Institute for Energy, Fortinet Founders Professor, Professor of Materials Science and Engineering, of Energy Science and Engineering, of Photon Science, Senior Fellow at Woods and Professor, by courtesy, of Chemistry
- Postdoctoral Scholar
- Co-President, Stanford Battery Club
- International Symposium Leader
- StorageX Industrial Liaison Student Coordinator
- Associate Professor of Civil and Environmental Engineering
- Dean, Stanford Doerr School of Sustainability, Jay Precourt Professor, Professor of Mechanical Engineering, of Energy Science & Engineering, of Photon Science, by courtesy, of Materials Sci & Eng and Senior Fellow, by courtesy, at Hoover
- Program Manager
- Assistant Professor of Energy Science Engineering and, by courtesy, of Electrical Engineering
- Tech Talk Leader
- Assistant Professor of Chemical Engineering
- William R. Timken Professor in the Graduate School of Business, Emeritus
- Professor of Materials Science and Engineering
- Co-President, Stanford Battery Club
- Assistant Professor of Chemical Engineering, Center Fellow at the Precourt Institute for Energy, by courtesy, at the Woods Institute for the Environment and Assistant Professor, by courtesy, of Civil and Environmental Engineering
- Program Manager, SECA - Stanford Energy Corporate Affiliates
Recent Publications
- Onori, S. (2020). Aging-Aware Optimal Energy Management Control for a Parallel Hybrid Vehicle Based on Electrochemical-Degradation Dynamics. https://doi.org/10.1109/TVT.2020.3019241
- Chueh, W. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4, 383–391. https://doi.org/10.1038/s41560-019-0356-8
- Gent, W., & Chueh, W. (2019). Metal–oxygen decoordination stabilizes anion redox in Li-rich oxides. Nature Materials, 18, 256–265. https://doi.org/10.1038/s41563-018-0276-1