Battery Materials Discovery and Smart Grid Management using Machine Learning

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Battery Materials Discovery and Smart Grid Management using Machine Learning
Title:
Battery Materials Discovery and Smart Grid Management using Machine Learning
Journal Title:
Batteries & Supercaps
Publication Date:
29 August 2022
Citation:
Wong, A. J. Y., Zhou, X., Lum, Y., Yao, Z., Chua, Y. C., Wen, Y., & Seh, Z. W. (2022). Battery Materials Discovery and Smart Grid Management using Machine Learning. Batteries & Supercaps. Portico. https://doi.org/10.1002/batt.202200309
Abstract:
The transition from fossil fuels to renewable energy represents a grand challenge for humankind. For this vision to come to pass, significant advances in energy storage technologies such as batteries, which solve the intermittency of renewable energy, need to be achieved. Developing new battery materials with higher capacities and longer lifetimes is thus of paramount importance. Moreover, the intermittency of renewable energy presents a significant challenge to smart grid management. To this end, researchers have begun turning to machine learning (ML) techniques: algorithms that learn from datasets and automatically improve through experience. These can be used to make predictions and informed decisions, which can accelerate the process of materials discovery and systems management. Here we discuss key ML concepts that have guided important developments in battery materials discovery and smart grid management. In the process, we also examine critical challenges, future opportunities, and how ML can make a significant impact.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - NRF Fellowship
Grant Reference no. : NRF-NRFF2017-04

This research / project is supported by the National Research Foundation - Energy Research Test-Bed and Industry Partnership Funding Initiative, part of the Energy Grid (EG) 2.0 programme
Grant Reference no. : NA

This research is supported by core funding from: SERC
Grant Reference no. : NA
Description:
This is the peer reviewed version of the following article: Wong, A. J. Y., Zhou, X., Lum, Y., Yao, Z., Chua, Y. C., Wen, Y., & Seh, Z. W. (2022). Battery Materials Discovery and Smart Grid Management using Machine Learning. Batteries & Supercaps. Portico. https://doi.org/10.1002/batt.202200309, which has been published in final form at doi.org/10.1002/batt.202200309,. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited
ISSN:
2566-6223
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