The management and control of material flow forms the core of manufacturing execution systems （MES） in the petrochemical industry. The bottleneck in the application of MES is the ability to match the material-flow m...The management and control of material flow forms the core of manufacturing execution systems （MES） in the petrochemical industry. The bottleneck in the application of MES is the ability to match the material-flow model with the production processes. A dynamic material-flow model is proposed in this paper after an analysis of the material-flow characteristics of the production process in a petrochemical industry. The main material-flow events are described, including the movement, storage, shifting, recycling, and elimination of the materials. The spatial and temporal characters of the material-flow events are described, and the material-flow model is constructed. The dynamic material-flow model introduced herein is the basis for other subsystems in the MES. In addition, it is the subsystem with the least scale in MES. The dynamic-modeling method of material flow has been applied in the development of the SinoMES model. It helps the petrochemical plant to manage the entire flow information related to tanks and equipments from the aspects of measurement, storage, movement, and the remaining balance of the material. As a result, it matches the production process by error elimination and data reconciliation. In addition, it facilitates the integration of application modules into the MES and guarantees the potential development of SinoMES in future applications.展开更多
For safe and reliable operation of lithium-ion batteries in electric vehicles,the real-time monitoring of their internal states is important.The purpose of our study is to find an easily implementable,online identific...For safe and reliable operation of lithium-ion batteries in electric vehicles,the real-time monitoring of their internal states is important.The purpose of our study is to find an easily implementable,online identification method for lithium-ion batteries in electric vehicles.In this article,we propose an equivalent circuit model structure.Based on the model structure we derive the recursive mathematical description.The recursive extended least square algorithm is introduced to estimate the model parameters online.The accuracy and robustness are validated through experiments and simulations.Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%.In addition,it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.展开更多
基金the National High Technology Research and Development Program of China (No.2007AA04Z191).
文摘The management and control of material flow forms the core of manufacturing execution systems （MES） in the petrochemical industry. The bottleneck in the application of MES is the ability to match the material-flow model with the production processes. A dynamic material-flow model is proposed in this paper after an analysis of the material-flow characteristics of the production process in a petrochemical industry. The main material-flow events are described, including the movement, storage, shifting, recycling, and elimination of the materials. The spatial and temporal characters of the material-flow events are described, and the material-flow model is constructed. The dynamic material-flow model introduced herein is the basis for other subsystems in the MES. In addition, it is the subsystem with the least scale in MES. The dynamic-modeling method of material flow has been applied in the development of the SinoMES model. It helps the petrochemical plant to manage the entire flow information related to tanks and equipments from the aspects of measurement, storage, movement, and the remaining balance of the material. As a result, it matches the production process by error elimination and data reconciliation. In addition, it facilitates the integration of application modules into the MES and guarantees the potential development of SinoMES in future applications.
基金supported by the National High Technology Research and Development Program("863" Project)(Grant No.2011AA05A109)the International Science and Technology Cooperation Program of China(Grant Nos.2011DFA70570,2010DFA72760)the National Natural Science Foundation of China(Grant No.51007088)
文摘For safe and reliable operation of lithium-ion batteries in electric vehicles,the real-time monitoring of their internal states is important.The purpose of our study is to find an easily implementable,online identification method for lithium-ion batteries in electric vehicles.In this article,we propose an equivalent circuit model structure.Based on the model structure we derive the recursive mathematical description.The recursive extended least square algorithm is introduced to estimate the model parameters online.The accuracy and robustness are validated through experiments and simulations.Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%.In addition,it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.
