Energy Consumption Modelling for Air-Cushion Vehicles

2013 ◽  
Vol 483 ◽  
pp. 208-213 ◽  
Author(s):  
Shuo Xu ◽  
Jing Sun ◽  
Jie Xi Shen
Keyword(s):  
Energy Consumption ◽  
Electric Vehicles ◽  
Load Distribution ◽  
Operating Parameters ◽  
Slip Ratio ◽  
Drive Condition ◽  
Energy Consumption Optimization ◽  
Air Cushion ◽  
Air Cushion Vehicles ◽  
Independent Parameters

Air-cushion vehicles (ACVs) provide a solution to transportation on soft terrain, whereas they also bring a new problem of excessive energy consumption to be solved. The prerequisite for energy consumption optimization is its modelling and simplification with respect to vehicle independent operating parameters. In a scenario of steady-state longitudinal drive condition, by employing slip ratio and load distribution ratio as independent parameters, dependent parameters and energy consumption are inferred and finally expressed as functions of the independent parameters. The method and result can be taken reference by studies of energy consumption modelling for air-cushion vehicles in different structures and also extensively for common electric vehicles.

A Joint Energy Consumption Optimization Algorithm for Hybrid Air-Cushion Vehicles

2012 ◽  
Vol 239-240 ◽  
pp. 1501-1506 ◽  
Author(s):  
Shuo Xu ◽  
Cheng Kai Sun
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Complex Function ◽  
Function Optimization ◽  
Soil Conditions ◽  
Optimal Solutions ◽  
Energy Consumption Optimization ◽  
Consumption Index ◽  
Air Cushion ◽  
Air Cushion Vehicles

Hybrid air-cushion vehicles (ACVs) provide a solution to transportation on soft terrain, whereas they also bring a new problem of excessive energy consumption. In order to minimize energy consumption in a computational manner, a Genetic Algorithm (GA) - Neural Network (NN) joint optimization algorithm is proposed to online calculate the corresponding optimal vehicle running parameters for given soil conditions. This is realized in three steps. (1) The energy consumption index is firstly simplified as a constrained function with respect to only two independent vehicle running parameters. (2) The optimal solutions are figured out offline with respect to some specific soil conditions by the designed GA optimizer. (3) The optimal solutions are figured out online with respect to general soil conditions by the designed NN optimizer, which is trained using the above offline-obtained data. The feasibility of the joint algorithm is supported by experiments, whose results show an effective integration of the GA’s advantage in complex function optimization and the NN’s advantage in generalization ability and computing speed.

Stability of aerobic granular sludge under low energy consumption: Optimization of granular size distribution by a novel internal component

2021 ◽  
Author(s):  
Runjuan Cao ◽  
Yatong Ji ◽  
Taixing Han ◽  
Jingsong Deng ◽  
Liang Zhu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Granular Sludge ◽  
Pollutant Removal ◽  
Low Energy ◽  
Aerobic Granular Sludge ◽  
Mesh Screen ◽  
Energy Consumption Optimization ◽  
Low Energy Consumption ◽  
Internal Component ◽  
The Stability

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,...

Sign in / Sign up

Export Citation Format

Share Document