Analysis of energy consumption and powertrain parameters optimization of BEV based on running cycle

2010 ◽  
Author(s):  
Zhou Bing ◽  
Jiang Qinghua ◽  
Yang Yi ◽  
Wang Jisheng
Keyword(s):  
Energy Consumption ◽  
Parameters Optimization

scholarly journals DETERMINATION OF EXTREME TRAIN RUNNING PARAMETERS ALONG A RAILWAY LINE SEGMENT

Transport ◽  
2006 ◽  
Vol 21 (2) ◽  
pp. 123-130 ◽  
Author(s):  
Jonas Jonaitis
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Point Of View ◽  
Parameters Optimization ◽  
Optimal Parameters ◽  
Total Energy Consumption ◽  
Railway Line ◽  
Decision Variables ◽  
Motion Parameters

It is possible to select train‐car draft mass and traction vehicle parameters for technical speed νt along segment sk in such a way that unitary total energy consumption related to train‐car mass would be the lowest. Such running is called extreme running while traction vehicle parameters and train‐car mass are called optimal from the point of view of energy consumption. The optimization of traction vehicle parameters is possible if a mathematical traction vehicle model is added to the running program and then vehicle parameters are made variable and added to motion parameters as subsequent decision variables within train running program. Together with the train motion parameters optimization traction vehicle parameters should be optimized according to the criterion of unitary total energy consumption related to train‐car mass. Traction vehicle optimal parameters depend on train technical speed, length and profile of segment , as well as, on train‐car mass.

scholarly journals Humanoid Robot Cooperative Motion Control Based on Optimal Parameterization

2021 ◽  
Vol 15 ◽  
Author(s):  
Qiubo Zhong ◽  
Yaoyun Li ◽  
Caiming Zheng ◽  
Tianyao Shen
Keyword(s):  
Energy Consumption ◽  
Humanoid Robot ◽  
Control Method ◽  
Humanoid Robots ◽  
Parameters Optimization ◽  
Optimal Parameters ◽  
Cooperative Motion ◽  
Key Technologies ◽  
Consumption Index ◽  
The Stability

The implementation of low-energy cooperative movements is one of the key technologies for the complex control of the movements of humanoid robots. A control method based on optimal parameters is adopted to optimize the energy consumption of the cooperative movements of two humanoid robots. A dynamic model that satisfies the cooperative movements is established, and the motion trajectory of two humanoid robots in the process of cooperative manipulation of objects is planned. By adopting the control method with optimal parameters, the parameters optimization of the energy consumption index function is performed and the stability judgment index of the robot in the movement process is satisfied. Finally, the effectiveness of the method is verified by simulations and experimentations.

Optimization of Process Parameters on Surface Hardness and Energy Consumption in Milling of 7050 Aluminum Alloy using Enhanced NSGA-II

2021 ◽  
Author(s):  
Yang Yang ◽  
Chen Su ◽  
Hongsen Wang ◽  
Yuan Wang ◽  
Leshi Shu
Keyword(s):  
Aluminum Alloy ◽  
Energy Consumption ◽  
Surface Hardness ◽  
Population Diversity ◽  
Parameters Optimization ◽  
Numerical Control ◽  
Adaptive Mutation ◽  
Nsga Ii ◽  
Milling Parameters ◽  
7050 Aluminum Alloy

Abstract Aluminum alloy has high strength and light weight. It is widely used for aircraft fuselage, propellers and other parts which work under high load conditions. High-quality parts made of aluminum alloy processed by computerized numerical control (CNC) machine often have the characteristics of high cost in their processing. In order to achieve high surface quality and control processing costs, this article takes the workpiece surface hardness and machining energy consumption as targets. Intelligent optimization algorithm is used to find the optimal combination of milling parameters to obtain ideal targets. CNC milling parameter optimization is a multi-parameter, multi-objective, multi-constraint, discrete nonlinear optimization problem which is difficult to solve. For this challenge, an improved NSGA-II is presented, named enhanced population diversity NSGA-II (EPD-NSGA-II). EPD-NSGA-II is improved with the normal distribution crossover, adaptive mutation operator of differential evolution, crowding calculation method considering variance and modified elite retention strategy to achieve enhanced population diversity. 12 test functions are chosen for experimentation to verify the performance of the EPD-NSGA-II. The values of three evaluation indicators show that the proposed approach has good distribution and convergence performance. Finally, the approach is applied in the milling parameters optimization of 7050 aluminum alloy to get the optimal solutions. Results indicate that the EPD-NSGA-II is effective in optimizing the problem of milling parameters.

scholarly journals Parametric study to enhance performance of wastewater treatment process, by reverse osmosis-photovoltaic system

2020 ◽  
Vol 10 (10) ◽  
Author(s):  
Yousra Jbari ◽  
Souad Abderafi
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Reverse Osmosis ◽  
Desirability Function ◽  
Operating Conditions ◽  
Parameters Optimization ◽  
Physical Models ◽  
Photovoltaic System ◽  
Water Recovery ◽  
Battery Capacity

Abstract The presence of certain toxic pollutants in water and wastewater such as chlorophenol must be eliminated, as they have negative effects on human health and the environment. Based on the state of the art, the reverse osmosis (RO) coupled with photovoltaic (PV) was chosen for wastewater treatment. The aim of this article is to evaluate the optimal operating conditions of RO-PV system that maximize chlorophenol rejection with minimal energy consumption. Two complementary approaches were followed combining physical models with statistical ones. The physical model used for the simulation is based on the equations of diffusion and matter balance. After demonstrating the reliability of this model, it was used for parametric sensitivity analysis, performing numerical experiments using a program developed under Python. The data obtained were used for operating parameters optimization, using artificial neural network method coupled with the desirability function. The results showed that the optimal values obtained, relating to feed pressure of 9.713 atm, water recovery rate of 40%, operating flow rate of 10−4 m3/s and temperature of 40 °C could remove 91% of chlorophenol with an energy consumption of 0.8 kWh/m3. This consumption allowed us to deduce that photovoltaic solar panel with a peak power of 280 Wp and a battery capacity of 9.22 kWh is sufficient to produce 1 m3/day.

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