scholarly journals Energy Consumption Optimization Model of Multi-Type Bus Operating Organization Based on Time-Space Network

2019 ◽  
Vol 9 (16) ◽  
pp. 3352 ◽  
Author(s):  
Yuhuan Liu ◽  
Enjian Yao ◽  
Shasha Liu

As a new type of green bus, the pure electric bus has obvious advantages in energy consumption and emission reduction compared with the traditional fuel bus. However, the pure electric bus has a mileage range constraint and the amount of charging infrastructure cannot meet the demand, which makes the scheduling of the electric bus driving plans more complicated. Meanwhile, many routes are operated with mixing pure electric buses and traditional fuel buses. As mentioned above, we focus on the operating organization problem with the multi-type bus (pure electric buses and traditional fuel buses), aiming to provide guidance for future application of electric buses. We take minimizing the energy consumption of vehicles, the waiting and traveling time of passengers as the objectives, while considering the constraints of vehicle full load limitation, minimal departure interval, mileage range and charging time window. The energy consumption based multi-type bus operating organization model was formulated, along with the heuristic algorithm to solve it. Then, a case study in Beijing was performed. The results showed that, the optimal mixing ratio of electric bus and fuel bus vary according to the variation of passenger flow. In general, each fuel bus could be replaced by two pure electric buses. Moreover, in the transition process of energy structure in public transport, the vehicle scale keeps increasing. The parking yard capacity and the amount of charging facilities are supposed to be further expanded.

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yuhuan Liu ◽  
Enjian Yao ◽  
Muyang Lu ◽  
Ling Yuan

Currently, although pure electric buses have the advantage of environmental-friendly, its endurance mileage is insufficient and the charging pile is still far away from the actual demand, resulting in a more complicated scheduling. Given this, we studied the driving plan of a pure electric bus, aiming to support the promotion and application of the electric bus. Considering service quality, we built a regional pure electric bus driving plan model and designed an optimal solution based on packing idea and genetic algorithm, aiming at minimizing fleet size, charging facility, and empty driving mileage. We took the electric bus routes operated in a region of Beijing as an empirical example. Compared with the results from the greedy algorithm, we found that the total cost of 544 bus trips with tasks was reduced by 19.6%. Although the average empty driving mileage increased by approximately 20%, the number of pure electric bus vehicles and the required amount of charging infrastructure decreased by 19.7% and 33.3%, respectively. The cost of increasing empty driving mileage was lower than that of the reducing number of buses and charging facilities, indicating that the above three variables reached a balance, and the optimization algorithm is proved to be significantly effective.


2021 ◽  
Vol 13 (5) ◽  
pp. 128
Author(s):  
Jun Liu ◽  
Xiaohui Lian ◽  
Chang Liu

In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the movement of the task offloading process in the network. Then, the task offloading process is transformed into a Markov state transition process to build a model of the computational offloading decision process. Finally, the delay and energy consumption weights are introduced into the DQN algorithm to update the computation offloading decision process, and the optimal offloading decision under the low cost is achieved according to the task attributes. The simulation results show that compared with the traditional Lyapunov-based offloading decision scheme and the classical Q-learning algorithm, the delay and energy consumption are respectively reduced by 68.33% and 11.21%, under equal weights when the offloading task volume exceeds 500 Mbit. Moreover, compared with offloading to edge nodes or backbone nodes of the network alone, the proposed mixed offloading model can satisfy more than 100 task requests with low energy consumption and low delay. It can be seen that the computation offloading decision proposed in this paper can effectively reduce the delay and energy consumption during the task computation offloading in the Space–Air–Ground Integrated Network environment, and can select the optimal offloading sites to execute the tasks according to the characteristics of the task itself.


Author(s):  
Runjuan Cao ◽  
Yatong Ji ◽  
Taixing Han ◽  
Jingsong Deng ◽  
Liang Zhu ◽  
...  

To enhance the stability and pollutant removal performance of an aerobic granular sludge (AGS), four groups of AGS reactors with different pore sizes of mesh screen (R1 is control reactor,...


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 55586-55598 ◽  
Author(s):  
Klaus Kivekas ◽  
Jari Vepsalainen ◽  
Kari Tammi

Sign in / Sign up

Export Citation Format

Share Document