CHAIN: Cyber Hierarchy and Interactional Network Enabling Digital Solution for Battery Full-Lifespan Management

Yang, Shichun; He, Rong; Zhang, Zhengjie; Cao, Yaoguang; Gao, X. L.; Liu, Xinhua

doi:10.1016/j.matt.2020.04.015

Cited by 114 publications

(37 citation statements)

References 62 publications

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“…Besides, smart systems containing interactions between ESD and ESS, and use of artificial intelligence to make controls and decisions, are needed. An example of such a smart system based on a high level of artificial intelligence is CHAIN (cyber hierarchy and interactional network) proposed by (Yang et al, 2020a). This system is capable of both interactional computing and data storage and can store abundant information further for deep learning.…”

Section: Summary and Future Directionsmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

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Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

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Section: Summary and Future Directionsmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

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“…The term and scope of Digital Twin have not been undisputed and similar concepts have been introduced. These concepts often have the same or partially the same features, but with different names such as device shadow [21], [22], virtual twin [23], virtual object [24], hyper-computational model [22], mirrored system [22], [25], synchronized virtual prototype [22], digital counterpart [26], digital surrogate [27], product agent [28], [29], avatar [30] and product avatar [26], [31].…”

Section: A History Of Digital Twinmentioning

confidence: 99%

5-Dimension Cross-Industry Digital Twin Applications Model and Analysis of Digital Twin Classification Terms and Models

Newrzella¹,

Franklin

Haider

2021

IEEE Access

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A Digital Twin is an auspicious cross-industry concept in the era of digitalization, which promises a wide range of benefits such as efficiency improvements, predictions of future opportunities and challenges, and respective recommendations. At present, a variety of definitions and terms exist, causing increasing confusion among practitioners and users. Here we address this need for consolidation with a holistic view of the Digital Twin concept across industries. We analyze classification models and Digital Twin terms in academia and industry in order to propose a 5-dimension cross-industry Digital Twin applications model. This model, based on the core three-part Digital Twin concept introduced by Grieves in 2002, enables ease of understanding and cross-industry classification and development of applications within the concept of the Digital Twin. The proposed model consists of the dimensions scope of the physical entity, feature(s) of the physical entity, form of communication, scope of the virtual entity, and user-specific outcome/value created.

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“…In this paper, based on the simulation analysis of the virtual prototype model of the chain drive system under normal operating conditions, the dynamics of the chain drive system when the chain jamming fault and chain breakage fault occur are studied by setting the fault conditions. When the chain jamming fault occurs, it shows the jamming and tensing of the scraper chain; when the chain breakage fault occurs, it shows the sudden breakage of a scraper chain, and the scraper chains at the fault location are separated from each other [20]. These two protocols can effectively reduce the average energy consumption of network nodes, and the data collection rate of both exceeds 88%.…”

Section: Experimental Optimization Design Of Mechanical Chainmentioning

confidence: 99%

Optimized Design of Mechanical Chain Drive Based on a Wireless Sensor Network Data Algorithm

Zhuang

Ding

et al. 2021

Journal of Sensors

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In this paper, we use a wireless sensor network data algorithm to optimize the design of mechanical chain drive by conducting an in-depth study of the mechanical chain drive optimization. We utilize the crowdsourcing feature of the swarm-wise sensing network for assisted wireless sensor networking to achieve crowdsourcing-assisted localization. We consider a framework for crowdsourcing-assisted GPS localization of wireless sensor networks and propose two recruitment participant optimization objectives, namely, minimum participants and time efficiency, respectively. A model and theoretical basis are provided for the subsequent trusted data-driven participant selection problem in swarm-wise sensing networks. The sprocket-chain engagement frequency has the greatest influence on the horizontal bending-vertical bending composite in different terrain conditions. The dynamic characteristics under working conditions are most influenced, while the scraping of the scraper and the central groove significantly influenced horizontal bending and vertical bending. Under load conditions, the amplitude of the scraper and central groove scraping increases significantly, which harm the dynamics of the scraper conveyor. By monitoring the speed difference between the head and tail sprockets and the overhang of the scraper, the tensioning status of the scraper conveyor chain can be effectively monitored to avoid chain jamming and chain breakage caused by the loose chain, thus improving the reliability and stability of the scraper conveyor.

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CHAIN: Cyber Hierarchy and Interactional Network Enabling Digital Solution for Battery Full-Lifespan Management

Cited by 114 publications

References 62 publications

Machine learning toward advanced energy storage devices and systems

Machine learning toward advanced energy storage devices and systems

5-Dimension Cross-Industry Digital Twin Applications Model and Analysis of Digital Twin Classification Terms and Models

Optimized Design of Mechanical Chain Drive Based on a Wireless Sensor Network Data Algorithm

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