Advances in Applied Energy

Lithium-ion batteries have become the best choice for battery energy storage systems and electric vehicles due to their excellent electrical performances and important contributions to achieving the carbon-neutral goal. With the large-scale application, safety accidents are increasingly caused by lithium-ion batteries. As the core component for battery energy storage systems and electric vehicles, lithium-ion batteries account for about 60% of vehicular failures and have the characteristics of the rapid spread of failure, short escape time, and easy initiation of fires, so the safety improvement of lithium-ion batteries is urgent. This study analyses the causes and mechanisms of lithium-ion batteries failures from design, production, and application, investigates its failure features and warning algorithms for thermal runaway, and the concept of long-medium-short graded warning is proposed based on the battery failure mechanism and its evolution to provide a basis for failure warning. As lithium-ion batteries fires are difficult to completely avoid, the characteristics of lithium-ion batteries fires are explored to improve battery structure and develop fire extinguishing agents and methods for fire prevention and suppression. Improving the safety of batteries is a systematic project, and at a time when there has been no breakthrough in the chemical system, improvements, such as build a practical graded warning system, are needed in all aspects of design, production, use and disposal to improve battery safety and minimize the risk of failure.

Yu Yang

Renjie Wang

Zhaojie Shen

Quanqing Yu

Rui Xiong

Weixiang Shen

Towards a safer lithium-ion batteries: A critical review on cause, characteristics, warning and disposal strategy for thermal runaway

迈向更安全的锂离子电池：热失控原因、特征、预警及处置策略的综述

Ken Chen

Kongfu Hu

Hu Li

Siyan Chan

Junjie Chen

Yu Pei

Bin Zhao

Gang Pei

Scalable spectrally selective solar cell for highly efficient photovoltaic thermal conversion

高效光伏热转换的可扩展光谱选择性太阳能电池

Kosaku Nakano

Sophia Vögler

Kenji Tanaka

Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data

电动汽车健康状态估算的进展：基于Transformer的方法利用真实世界数据

The variability of solar photovoltaic (PV) power output, driven by rapidly changing cloud dynamics, hinders the transition to reliable renewable energy systems. Information on future sky conditions, especially cloud coverage, hold the promise for improving PV output forecasting. Leveraging recent advances in generative artificial intelligence (AI), we introduce SkyGPT, a physics-constrained stochastic video prediction model, which predicts plausible future images of the sky using historical sky images. We show that SkyGPT can accurately capture cloud dynamics, producing highly realistic and diverse future sky images. We further demonstrate its efficacy in 15-minute-ahead probabilistic PV output forecasting using real-world power generation data from a 30-kW rooftop PV system. By coupling SkyGPT with a U-Net-based PV power prediction model, we observe superior prediction reliability and sharpness compared with several benchmark methods. The propose approach achieves a continuous ranked probability score (CRPS) of 2.81 kW, outperforming a classic convolutional neural network (CNN) baseline by 13% and the smart persistence model by 23%. The findings of this research could aid efficient and resilient management of solar electricity generation, particularly as we transition to renewable-heavy grids. The study also provides valuable insights into stochastic cloud modeling for a broad research community, encompassing fields such as solar energy meteorology and atmospheric sciences.

Yuhao Nie

Eric Zelikman

Andea Scott

Quentin Paletta

Adam Brandt

SkyGPT: Probabilistic Ultra-short-term Solar Forecasting Using Synthetic Sky Images from Physics-constrained VideoGPT

SkyGPT：利用物理约束视频GPT生成合成天空图像的概率性超短期太阳能预测

Leveraging demand-side flexibility resources (e.g., buildings) is a crucial and cost-effective strategy for addressing the operational and infrastructure-related challenges in power grids pursuing deep decarbonization with high renewable energy penetration. However, the demand flexibility opportunities and financial benefits for residential space heating, which are sizeable demand-side flexibility resources, through emerging building energy management solutions (i.e., smart control and phased change material (PCM) thermal storage) across the US are not fully understood. In this paper, we systematically assess the demand flexibility and cost-saving/revenue potentials in residential space heating through detailed building-level simulations for five consecutive years at a 5-min temporal resolution in 20 metro areas across the high-heating-demand regions of the US. The results show a high degree of synergy between PCM thermal storage and smart control, which enables substantial demand flexibility potential, reaching 98.5% of peak load shifting, and electricity cost-saving/revenue potential, reaching 338.3% of electricity cost reductions, for residential space heating in the US. By achieving such performance, adopting smart control and PCM thermal storage is financially viable in 50% of the tested metro areas. The results reveal that the demand flexibility and cost-saving/revenue potentials of residential space heating in the US are further enhanced by higher volatilities in electricity prices. Active PCM thermal storage has lower energy efficiency but much higher energy flexibility than passive PCM thermal storage. Based on the findings, recommendations for integrating PCM thermal storage and smart control systems within residential space heating are provided.

Shiyu Yang

H. Oliver Gao

Fengqi You

Demand Flexibility and Cost-Saving Potentials via Smart Building Energy Management: Opportunities in Residential Space Heating Across the US

智能建筑能源管理的需求灵活性与成本节约潜力：美国住宅空间供暖的机遇

Iegor Riepin

Tom Brown

Victor M. Zavala

Spatio-temporal load shifting for truly clean computing

时空负载转移实现真正清洁计算

Alessandro Magnino

Paolo Marocco

Massimo Santarelli

Marta Gandiglio

Economic Viability and CO2 Emissions of Hydrogen Production for Ammonia Synthesis: A comparative analysis across Europe

氨合成用氢气生产的经济可行性与二氧化碳排放：欧洲范围内的比较分析

Jieyang Xu

Sebastian Mosbach

Jethro Akroyd

Markus Kraft

Impact of heat pumps and future energy prices on regional inequalities

热泵和未来能源价格对区域不平等的影响

Lidian Niu

Zeyan Zhao

Jiawei Tan

Tao Liang

Fuzheng Zhang

Ning Xiao

Yi He

Shan Xie

Rui Jing

Jian Lin

Feng Wang

Yingru Zhao

A Dynamic Reliability Assessment Framework for Integrated Energy Systems: A New Methodology to Address Cascading Failures

综合能源系统动态可靠性评估框架：应对级联故障的新方法论

Ao Li

Fu Xiao

Ziwei Xiao

Rui Yan

Anbang Li

Yan Lv

Bing Su

Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling

关于采样成本的主动学习以增强AI驱动的建筑能源系统建模

Abbreviation	Adv. Appl. Energy
Journal Impact	13.16
Quartiles(Global)	ENERGY & FUELS(Q1)

ISSN	2666-7924
h-index	33

Publication Information	Publisher: Elsevier Ltd，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2021，Proportion of original research papers: 0.00%，Self Citation Rate:6.20%， Gold OA Rate: 94.37%
Journal Fees	$3600Details
Average review cycle	网友分享经验：8 Weeks

Advances in Applied Energy

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