Deep Reinforcement Learning (DRL) shows good performance for optimizing battery energy storage systems (BESS) coordinated operations with photovoltaic plants (PV), yet most studies rely on simulations. Bridging the gap to practical application requir...
Accurate Remaining Useful Life (RUL) prediction for Lithium-ion batteries is critical for system safety, yet its efficacy is frequently limited by data scarcity in industrial contexts. The robustness of hybrid architectures combining Convolutional Ne...
Effective recycling of spent LiNixCoyMn1-x-yO2 (NCM) battery is crucial to ensure sustainability of the lithium-ion battery industry. However, recycling is inherent with multiple operational steps and many effective factors. It is difficult to optimi...
Partial discharge (PD) detection in power transformers is critical for preventing insulation failures in modern power grids, yet remains challenging due to signal complexity and environmental noise. Existing methods struggle with accurate PD classifi...
To improve the intelligent and refined management level of power distribution systems in equipment operation and maintenance as well as emergency support, this work proposes an integrated "prediction-optimization" model that combines genetic algorith...
Organic acid leaching is an effective and sustainable method for simultaneously recovering critical metals from ternary lithium batteries (T-LIBs). However, current methods overlook the structural impact of organic acids and rely on inefficient trial...
Lithium-ion batteries are high-performance energy storage devices that have been widely used in a variety of applications. Accurate early-stage prediction of their remaining useful life is essential for preventing failures and mitigating safety risks...
Data training algorithms based on Artificial Intelligence (AI) often encounter overfitting, underfitting, or bias issues. This article presents the design of a hybrid self-learning algorithm to address the above challenges. The proposed approach is d...
This study proposes a survival-based modeling framework that combines behavioral features with interpretable machine learning to understand and predict user churn in electric vehicle charging services. Using a dataset of 1,074 users and 107,531 charg...
The rapid development of internet of things, big data, and artificial intelligence is propelling sports science into a data-driven era, demanding real-time, multidimensional athletic performance monitoring. Triboelectric nanogenerators (TENGs) have d...
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