气候变化领域集成服务门户(CCPortal): Cost, performance prediction and optimization of a vanadium flow battery by machine-learning

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DOI	10.1039/d0ee02543g
	Cost, performance prediction and optimization of a vanadium flow battery by machine-learning
	Li T.; Xing F.; Liu T.; Sun J.; Shi D.; Zhang H.; Li X.
发表日期	2020
ISSN	17545692
起始页码	4353
结束页码	4361
卷号	13 期号:11
英文摘要	Performance optimization and cost reduction of a vanadium flow battery (VFB) system is essential for its commercialization and application in large-scale energy storage. However, developing a VFB stack from lab to industrial scale can take years of experiments due to the influence of complex factors, from key materials to the battery architecture. Herein, we have developed an innovative machine learning (ML) methodology to optimize and predict the efficiencies and costs of VFBs with extreme accuracy, based on our database of over 100 stacks with varying power rates. The results indicated that the cost of a VFB system (S-cost) at energy/power (E/P) = 4 h can reach around 223 $ (kW h)-1, when the operating current density reaches 200 mA cm-2, while the voltage efficiency (VE) and utilization ratio of the electrolyte (UE) are maintained above 90% and 80%, respectively. This work highlights the potential of the ML methodology to guide stack design and optimization of flow batteries to further accelerate their commercialization. © The Royal Society of Chemistry.
英文关键词	Electrolytes; Energy storage; Flow batteries; Machine learning; Predictive analytics; Vanadium; Complex factors; Industrial scale; Operating current densities; Performance optimizations; Performance prediction; Stack designs; Utilization ratios; Voltage efficiencies; Cost reduction; accuracy assessment; database; efficiency measurement; electrolyte; innovation; machine learning; optimization
语种	英语
来源期刊	Energy & Environmental Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/189475
作者单位	Division of Energy Storage, Dalian National Laboratory for Clean Energy (DNL), Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Zhongshan Road 457, Dalian, 116023, China
推荐引用方式 GB/T 7714	Li T.,Xing F.,Liu T.,et al. Cost, performance prediction and optimization of a vanadium flow battery by machine-learning[J],2020,13(11).
APA	Li T..,Xing F..,Liu T..,Sun J..,Shi D..,...&Li X..(2020).Cost, performance prediction and optimization of a vanadium flow battery by machine-learning.Energy & Environmental Science,13(11).
MLA	Li T.,et al."Cost, performance prediction and optimization of a vanadium flow battery by machine-learning".Energy & Environmental Science 13.11(2020).