scholarly journals Energy Saving Optimization of High Speed Train Based on Speed Prediction Control Curve

2020 ◽  
Author(s):  
Jie Li ◽  
Aihong Zhu ◽  
Yuqiong Duan ◽  
Jing Zhang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Predictive Control ◽  
High Speed ◽  
Control Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Bee Colony ◽  
High Speed Trains ◽  
Saving Mode

In order to study the energy-saving operation of high-speed trains, the energy consumption of trains is taken as the goal, and the speed at the transition point of the operating conditions is the optimization variable, an artificial bee colony algorithm is used to optimize the speed curve across the entire line, the purpose is to obtain the first stage optimization speed curve. On this basis, the conditions of the actual running line are fully considered, and the predictive control algorithm is used to optimize the local prediction of the speed, the purpose is to obtain the second stage optimization speed curve. The simulation results show that compared with the energy consumption in the time-saving mode, the energy consumption after the second prediction optimization is reduced by 19.29%. It is verified that the secondary speed curve obtained by the combination of the global artificial bee colony algorithm and the predictive control algorithm has better performance in energy saving effect. This paper can provide good reference value and practical significance for the energy-saving operation of other vehicles.

scholarly journals Study on Simplified Model and Numerical Solution of High-Speed Angular Contact Ball Bearing

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Guang Zeng ◽  
Chunjiang Zhao ◽  
Xiaokai Yu ◽  
Biao Sun ◽  
Zhigang Xiao ◽  
...  
Keyword(s):  
High Speed ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Ball Bearing ◽  
Calculation Model ◽  
Ball Bearings ◽  
Initial Value ◽  
Angular Contact Ball Bearing ◽  
Bee Colony ◽  
Simplified Calculation

For the calculation model of high-speed angular contact bearing has many variables, the large root difference exists, and the Newton iterative method solving the convergence depends on the initial value problems; thus, the simplified calculation model is proposed and the algorithm is improved. Firstly, based on the nonlinear equations of variables recurrence method of the high-speed angular contact ball bearing calculation model, it is proved that the ultimate fundamental variables of calculation model are the actual inner and outer contact angles, the axial and radial deformations. According to this reason, the nonlinear equations are deformed and deduced, and the number of equations is reduced from 4Z + 2 to 2Z + 2 (Z represents the number of rolling bodies); a simplified calculation model is formed. Secondly, according to the small dependence of the artificial bee colony algorithm on the initial value, an improved artificial bee colony algorithm is proposed for the large root difference characteristics of high-speed ball bearings. The validity of the improved algorithm is verified by standard test function. The algorithm is used to solve the high-speed angular contact ball bearing calculation model. Finally, the deformations of high-speed angular contact ball bearings are compared and verified by experiments, and the results of improved algorithm show good agreement with the experiments results.

scholarly journals Metaheuristic algorithm for solving the multi-objective vehicle routing problem with time window and drones

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092003
Author(s):  
Yun-qi Han ◽  
Jun-qing Li ◽  
Zhengmin Liu ◽  
Chuang Liu ◽  
Jie Tian
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Window ◽  
Total Energy Consumption ◽  
Routing Problem ◽  
Bee Colony

In some special rescue scenarios, the needed goods should be transported by drones because of the landform. Therefore, in this study, we investigate a multi-objective vehicle routing problem with time window and drone transportation constraints. The vehicles are used to transport the goods and drones to customer locations, while the drones are used to transport goods vertically and timely to the customer. Three types of objectives are considered simultaneously, including minimization of the total energy consumption of the trucks, total energy consumption of the drones, and the total number of trucks. An improved artificial bee colony algorithm is designed to solve the problem. In the proposed algorithm, each solution is represented by a two-dimensional vector, and the initialization method based on the Push-Forward Insertion Heuristic is embedded. To enhance the exploitation abilities, an improved employed heuristic is developed to perform detailed local search. Meanwhile, a novel scout bee strategy is presented to improve the global search abilities of the proposed algorithm. Several instances extended from the Solomon instances are used to test the performance of the proposed improved artificial bee colony algorithm. Experimental comparisons with the other efficient algorithms in the literature verify the competitive performance of the proposed algorithm.

Thermal Based Optimum Design of Tapered Roller Bearing Through Evolutionary Algorithm

2013 ◽  
Author(s):  
Rajiv Tiwari ◽  
Rahul Chandran
Keyword(s):  
Evolutionary Algorithm ◽  
Optimum Design ◽  
High Speed ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Roller Bearing ◽  
Maximum Temperature ◽  
Dynamic Capacity ◽  
Bee Colony ◽  
Tapered Roller

In high-speed applications the maximum temperature in bearings are a crucial concern. In some applications the bearing is the prime source of heat, the temperature at which a bearing operates dictates the type and amount of lubricant and the material for the fabrication of the bearing components. In the present work a thermal based optimum design of tapered roller bearings has been presented. Internal geometry of the bearing has been optimized based by evolutionary algorithm. Constraints are geometrical, kinematical, strength and thermal in nature. Optimum designs have been found to have better performance parameters. Artificial bee colony algorithm has been used for the present optimization problem, for solving constrained non-linear optimization formulations. A total of nine design variables corresponding to the bearing geometry and constraint factors have been considered. A convergence study has been carried and optimum designs based on temperature is compared with the optimized values based on dynamic capacity, both using artificial bee colony algorithm. There is an excellent improvement found in the optimized bearing designs based on temperature when compared with the optimized results based on dynamic capacity in respect of the maximum temperature in the bearing with the artificial bee colony algorithm.

scholarly journals An Online Energy-Saving Driving Strategy for Metro Train Operation Based on the Model Predictive Control of Switched-Mode Dynamical Systems

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4933 ◽  
Author(s):  
Fei Shang ◽  
Jingyuan Zhan ◽  
Yangzhou Chen
Keyword(s):  
Energy Consumption ◽  
Model Predictive Control ◽  
Energy Saving ◽  
Predictive Control ◽  
Control Algorithm ◽  
Optimization Problem ◽  
Online Optimization ◽  
Mode Switching ◽  
Train Control ◽  
Train Operation

With the rapid development of urban rail transit systems and the consequent sharp increase of energy consumption, the energy-saving train operation problem has been attracting much attention. Extensive studies have been devoted to optimal control of a single metro train in an inter-station run to minimize the energy consumption. However, most of the existing work focuses on offline optimization of the energy-saving driving strategy, which still needs to be tracked in real train operation. In order to attain better performance in the presence of disturbances, this paper studies the online optimization problem of the energy-saving driving strategy for a single metro train, by employing the model predictive control (MPC) approach. Firstly, a switched-mode dynamical system model is introduced to describe the dynamics of a metro train. Based on this model, an MPC-based online optimization problem is formulated for obtaining the optimal mode switching times with minimal energy consumption for a single train in an inter-station run. Then we propose an algorithm to solve the constrained optimization problem at each time step by utilizing the exterior point penalty function method. The proposed online optimal train control algorithm which determines the mode switching times can not only improve the computational efficiency but also enhances the robustness to disturbances in real scenarios. Finally, the effectiveness and advantages of this online optimal train control algorithm are illustrated through case studies of a single train in an inter-station run.

High-efficient energy saving processing of big data of communication under mobile cloud computing

2019 ◽  
Vol 10 (04) ◽  
pp. 1950025 ◽  
Author(s):  
Yazhen Liu ◽  
Pengfei Fan ◽  
Jiyang Zhu ◽  
Liping Wen ◽  
Xiongfei Fan
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Migration Strategy ◽  
Bee Colony

From 21st century, it is hard for traditional storage and algorithm to provide service with high quality because of big data of communication which grows rapidly. Thus, cloud computing technology with relatively low cost of hardware facilities is created. However, to guarantee the quality of service in the situation of the rapid growth of data volume, the energy consumption cost of cloud computing begins to exceed the hardware cost. In order to solve the problems mentioned above, this study briefly introduced the virtual machine and its energy consumption model in the mobile cloud environment, introduced the basic principle of the virtual machine migration strategy based on the artificial bee colony algorithm and then simulated the performance of processing strategy to big data of communication based on artificial bee colony algorithm in mobile cloud computing environment by CloudSim3.0 software, which was compared with the performance of two algorithms, resource management (RM) and genetic algorithm (GA). The results showed that the power consumption of the migration strategy based on the artificial bee colony algorithm was lower than the other two strategies, and there were fewer failed virtual machines under the same number of requests, which meant that the service quality was higher.

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