A component-based design approach for energy flexibility in cyber-physical manufacturing systems

被引:4
作者
Assad, Fadi [1 ]
Rushforth, Emma J. [1 ]
Harrison, Robert [1 ]
机构
[1] Univ Warwick, Warwick Mfg Grp WMG, Univ Rd, Coventry CV4 7AL, W Midlands, England
关键词
Energy flexibility; Cyber-physical Systems (CPS); Component-based design; Sustainability; IoT; Industry; 4.0; RAMI; INDUSTRIAL INTERNET; AUTOMATION SYSTEMS; MANAGEMENT-SYSTEM; EFFICIENCY; THINGS; OPPORTUNITIES; OPTIMIZATION; CONSUMPTION; REDUCTION; FRAMEWORK;
D O I
10.1007/s10845-023-02280-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Energy flexibility of manufacturing systems helps to meet sustainable manufacturing requirements and is getting significant importance with rising energy prices and noticeable climate changes. Considering the need to proactively enable energy flexibility in modern manufacturing systems, this work presents a component-based design approach that aims to embed energy flexibility in the design of cyber-physical production systems. To this end, energy management using Industry 4.0 technologies (e.g., Internet of Things and Cyber-physical Systems) is compared to the literature on energy flexibility in order to evaluate to what extent the energy flexibility practice takes advantage of Industry 4.0 technologies. Another dimension is the coverage of the life cycle of the manufacturing system which guarantees its sustainable design and the ability to achieve energy flexibility by configuring the energy consumption behaviour. A data-based design approach of the energy-flexible components is proposed in the spirit of the Reference Architectural Model Industrie 4.0 (RAMI 4.0), and then it is exemplified using an electric drive (as a component) in order to show the practical applicability of the approach. The energy consumption behaviour of the component is modelled using machine learning techniques. The digital twin of the studied component is developed using Visual Components virtual engineering environment, then its energy consumption behaviour is included in the model allowing the system integrator/decision-maker to configure the component based on the energy availability/price. Finally, external services in terms of an optimisation module and a deep learning module are connected to the digital twin.
引用
收藏
页码:975 / 1001
页数:27
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