Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization

Xu, Liqianyun; Wu, Feng; Chen, Renjie; Li, Li

doi:10.1016/j.ensm.2023.102785

Cited by 14 publications

(1 citation statement)

References 242 publications

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“…Moreover, the digital tools available for this purpose will encompass data-mining capabilities, which will enable the digitization of text and collation of data. Furthermore, the use of machine learning algorithms will facilitate the making of data-driven decisions [ 339 ].…”

Section: Setting the Stage For Digitizationmentioning

confidence: 99%

Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Chafiq,

Chaouiki,

2023

Nano-Micro Lett.

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Porous organic frameworks (POFs) have become a highly sought-after research domain that offers a promising avenue for developing cutting-edge nanostructured materials, both in their pristine state and when subjected to various chemical and structural modifications. Metal–organic frameworks, covalent organic frameworks, and hydrogen-bonded organic frameworks are examples of these emerging materials that have gained significant attention due to their unique properties, such as high crystallinity, intrinsic porosity, unique structural regularity, diverse functionality, design flexibility, and outstanding stability. This review provides an overview of the state-of-the-art research on base-stable POFs, emphasizing the distinct pros and cons of reticular framework nanoparticles compared to other types of nanocluster materials. Thereafter, the review highlights the unique opportunity to produce multifunctional tailoring nanoparticles to meet specific application requirements. It is recommended that this potential for creating customized nanoparticles should be the driving force behind future synthesis efforts to tap the full potential of this multifaceted material category.

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Section: Setting the Stage For Digitizationmentioning

confidence: 99%

Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Chafiq,

Chaouiki,

2023

Nano-Micro Lett.

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Machine Learning Applications in Battery Management System

Chandana,

Chavan

2024

How Machine Learning Is Innovating Today's World

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GenAI for Scientific Discovery in Electrochemical Energy Storage: State‐of‐the‐Art and Perspectives from Nano‐ and Micro‐Scale

Li,

You

2024

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The transition to electric vehicles (EVs) and the increased reliance on renewable energy sources necessitate significant advancements in electrochemical energy storage systems. Fuel cells, lithium‐ion batteries, and flow batteries play a key role in enhancing the efficiency and sustainability of energy usage in transportation and storage. Despite their potential, these technologies face limitations such as high costs, material scarcity, and efficiency challenges. This research introduces a novel integration of Generative AI (GenAI) within electrochemical energy storage systems to address these issues. By leveraging advanced GenAI techniques like Generative Adversarial Networks, autoencoders, diffusion and flow‐based models, and multimodal large language models, this paper demonstrates significant improvements in material discovery, battery design, performance prediction, and lifecycle management across different types of electrochemical storage systems. The research further emphasizes the importance of nano‐ and micro‐scale interactions, providing detailed insights into optimizing these interactions for improved efficiency and longevity. Additionally, the paper discusses the challenges and future directions for integrating GenAI in energy storage research, highlighting the importance of data quality, model transparency, workflow integration, scalability, and ethical considerations. By addressing these aspects, this research sets a new benchmark for the use of GenAI in battery development, promoting sustainable, efficient, and safer energy solutions.

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Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization

Cited by 14 publications

References 242 publications

Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Machine Learning Applications in Battery Management System

GenAI for Scientific Discovery in Electrochemical Energy Storage: State‐of‐the‐Art and Perspectives from Nano‐ and Micro‐Scale

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