The ICIR of Beijing Jiaotong University

Achievements

1.Modeling and Simulation on Recycling of Electric Vehicle Battery - Using Agent Approach
Abstract:This study investigates electric vehicles battery recycling problem. In this study, based on Agent theory and Anylogic platform, Agent model of battery recycling is built; And we do simulation for electric vehicle batteries recycling: this paper analyzes the influence that factors (battery renovation rate, quantities of electric vehicles, electric vehicle lifetime, battery lifetime, battery renovation time) does on recycling (quantities of wasted batteries, quantities of reused batteries, optimal quantities of batteries). Through simulation, this study shows that factors’ influence on recycling depends on the relative life RL greatly; When renovation rate changes in the interval [0.7, 0.8], the results fluctuate greatly, such as optimal quantities of batteries will decrease about 10%, quantities of reused batteries can increase about 30%, and quantities of wasted batteries will be a sharp decline by about 40%; the model is optimal until battery renovation times are increased to three, et al.
International Journal of Simulation Modelling, Vol. 13, No. 1, 2014, pp. 79-92.(SCI Index)
2.A Holographic-based Model for Logistics Resources Integration
Abstract:This study investigates the logistics resource integration problem. Based on a comprehensive literature review, we find that there is much room for improvement regarding the robustness problems in logistics resources integration. Logistics resources integration should especially consider uncertainties. In this study, we propose a holographic-based model (Internet of Things and Neural Network) to illustrate the problem. Internet of Things (IoT) is able to receive real-time data (including uncertainty information) in logistics systems and is equivalent to the perception subsystem. Neural Network, on the other hand, can determine the overall operation state for logistics resources integration and plays the role of analysis and assessment. Through simulation, this study shows that real-time data in logistics systems are transmitted based on protocols, so that uncertainty information can be received by the IoT model. The Neural Network model can comprehensively evaluate uncertainties through the neural network algorithm. Therefore, the robustness of logistics resources integration can be ensured in the logistics system.
Studies in Informatics and Control, Vol. 22, No. 4, 2013, pp. 367-376.(SCI Index)