Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning

Yang, Hao; He, Zhengqiu; Zhang, Mengdi; Tan, Xiaojie; Sun, Kang; Liu, Haiyan; Wang, Ning; Guan, Lu; Wang, Chongze; Wan, Yi; Wang, Wanli; Han, Hwataik; Wu, Mingbo

doi:10.1002/eom2.12330

Cited by 15 publications

(4 citation statements)

References 197 publications

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“…AI technologies help optimize energy use, increase grid efficiency, and enable predictive maintenance in renewable energy infrastructure [38]. Machine learning algorithms make accurate energy demand possible by examining enormous databases [39]. AI-driven automation optimizes operational procedures, producing more effective energy systems [40].…”

Section: Applications Of Ai In Sustainable Energymentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

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The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency of energy systems. This article conducts an exhaustive analysis of the critical aspects concerning the AI-sustainable energy nexus, encompassing the challenges in technological integration and the facilitation of intelligent decision-making processes pivotal for sustainable energy frameworks. It is demonstrated that AI applications, ranging from optimization algorithms to predictive analytics, possess a revolutionary capacity to bolster intelligent decision-making in sustainable energy. However, this integration is not without its challenges, which span technological complexities and socio-economic impacts. The article underscores the imperative for deploying AI in a manner that is transparent, equitable, and inclusive. Best practices and solutions are proposed to navigate these challenges effectively. Additionally, the discourse extends to recent advancements in AI, including edge computing, quantum computing, and explainable AI, offering insights into the evolving landscape of sustainable energy. Future research directions are delineated, emphasizing the importance of enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining socio-economic ramifications, exploring models of human-AI collaboration, fortifying security measures, and evaluating the impact of emerging technologies. This comprehensive analysis aims to inform academics, practitioners, and policymakers, guiding the creation of a resilient and sustainable energy future.

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Section: Applications Of Ai In Sustainable Energymentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

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“…The design of the classifier wheel is intentionally tailored to enable the precise separation of smaller particles while excluding larger ones. The larger particles gather in the lower section of the chamber, forming the coarse starch fraction [32]. The coarse fraction is distinguished by its elevated starch and fiber content, whereas the fine fraction is abundant in protein.…”

Section: Air Classificationmentioning

confidence: 99%

Extraction, Modification, Biofunctionality, and Food Applications of Chickpea (Cicer arietinum) Protein: An Up-to-Date Review

Patil,

Bains,

Sridhar

et al. 2024

Foods

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Plant-based proteins have gained popularity in the food industry as a good protein source. Among these, chickpea protein has gained significant attention in recent times due to its high yields, high nutritional content, and health benefits. With an abundance of essential amino acids, particularly lysine, and a highly digestible indispensable amino acid score of 76 (DIAAS), chickpea protein is considered a substitute for animal proteins. However, the application of chickpea protein in food products is limited due to its poor functional properties, such as solubility, water-holding capacity, and emulsifying and gelling properties. To overcome these limitations, various modification methods, including physical, biological, chemical, and a combination of these, have been applied to enhance the functional properties of chickpea protein and expand its applications in healthy food products. Therefore, this review aims to comprehensively examine recent advances in Cicer arietinum (chickpea) protein extraction techniques, characterizing its properties, exploring post-modification strategies, and assessing its diverse applications in the food industry. Moreover, we reviewed the nutritional benefits and sustainability implications, along with addressing regulatory considerations. This review intends to provide insights into maximizing the potential of Cicer arietinum protein in diverse applications while ensuring sustainability and compliance with regulations.

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“…Such a method realizes more detailed analysis for batteries and accurately predicts battery degradation trajectory. [42][43][44] Although tremendous achievements have been made, two critical issues remain for developing a highly efficient and universal model to predict battery SOH. First, existing methods achieve better nonlinear description capability at the cost of greater computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al 41 proposed a generalizable, data‐driven online approach to forecast the capacity degradation trajectory of lithium batteries, in which modeling for individual batteries enables online learning. Such a method realizes more detailed analysis for batteries and accurately predicts battery degradation trajectory 42–44 …”

Section: Introductionmentioning

confidence: 99%

A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries

Han,

Gao,

Chen

et al. 2024

InfoMat

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Accurately forecasting the nonlinear degradation of lithium‐ion batteries (LIBs) using early‐cycle data can obviously shorten the battery test time, which accelerates battery optimization and production. In this work, a self‐adaptive long short‐term memory (SA‐LSTM) method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data. Specifically, two features were extracted from discharge voltage curves by a time‐series‐based approach and forecasted to further cycles using SA‐LSTM model. The as‐obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model. The proposed method achieved an average online prediction error of 6.00% and 6.74% for discharge capacity and end of life, respectively, when using the early‐cycle discharge information until 90% capacity retention. Furthermore, the importance of temperature control was highlighted by correlating the features with the average temperature in each cycle. This work develops a self‐adaptive data‐driven method to accurately predict the cycling life of LIBs, and unveils the underlying degradation mechanism and the importance of controlling environmental temperature.image

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Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning

Cited by 15 publications

References 197 publications

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Extraction, Modification, Biofunctionality, and Food Applications of Chickpea (Cicer arietinum) Protein: An Up-to-Date Review

A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries

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