Real-time optimization control for parking a vehicle automatically

2002 ◽  
Author(s):  
T. Kobayashi ◽  
S. Majima
Keyword(s):  
Real Time ◽  
Time Optimization ◽  
Optimization Control ◽  
Real Time Optimization

scholarly journals Research on the Application of Artificial Intelligence Technology in Power System Intelligent Dispatching Automation

2021 ◽  
Vol 2083 (4) ◽  
pp. 042047
Author(s):  
Hongying Liu
Keyword(s):  
Real Time ◽  
Reactive Power ◽  
Power Grid ◽  
Control Mode ◽  
Particle Swarm Algorithm ◽  
Automation System ◽  
Regional Power ◽  
Time Optimization ◽  
Optimization Control ◽  
Real Time Optimization

Abstract From the perspective of meeting the power quality requirements of users, the article analyses the characteristics of traditional voltage and reactive power control mode and the regional power grid reactive voltage optimization centralized closed-loop control mode (AVC system) based on the dispatch automation system (SCADA/EMS) from the perspective of technical management. Combining the reactive power/voltage real-time optimization control model, a real-time optimization control method of the regional power grid based on the improved differential evolution algorithm is proposed. The particle swarm algorithm is combined with the characteristics of reactive power/voltage control to improve the initial particle quality, reduce the optimization space, and introduce a crossover operator to improve the calculation speed and efficiency of the algorithm. Taking an actual regional power grid as an example, the simulation calculation of reactive power/voltage real-time optimization is carried out. The results show that the proposed algorithm and control strategy are feasible and effective.

On-Board Real-Time Optimization Control for Turbo-Fan Engine Life Extending

2016 ◽  
Vol 0 (0) ◽  
Author(s):  
Qiangang Zheng ◽  
Haibo Zhang ◽  
Lizhen Miao ◽  
Fengyong Sun
Keyword(s):  
Real Time ◽  
Optimization Method ◽  
Support Vector ◽  
Thermal Mechanical Fatigue ◽  
Mechanical Fatigue ◽  
Time Optimization ◽  
Optimization Control ◽  
Conventional Optimization ◽  
Real Time Optimization ◽  
Engine Life

AbstractA real-time optimization control method is proposed to extend turbo-fan engine service life. This real-time optimization control is based on an on-board engine mode, which is devised by a MRR-LSSVR (multi-input multi-output recursive reduced least squares support vector regression method). To solve the optimization problem, a FSQP (feasible sequential quadratic programming) algorithm is utilized. The thermal mechanical fatigue is taken into account during the optimization process. Furthermore, to describe the engine life decaying, a thermal mechanical fatigue model of engine acceleration process is established. The optimization objective function not only contains the sub-item which can get fast response of the engine, but also concludes the sub-item of the total mechanical strain range which has positive relationship to engine fatigue life. Finally, the simulations of the conventional optimization control which just consider engine acceleration performance or the proposed optimization method have been conducted. The simulations demonstrate that the time of the two control methods from idle to 99.5 % of the maximum power are equal. However, the engine life using the proposed optimization method could be surprisingly increased by 36.17 % compared with that using conventional optimization control.

scholarly journals Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2278 ◽  
Author(s):  
Hsiu-Ying Hwang ◽  
Jia-Shiun Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Swarm Optimization ◽  
Time Optimization ◽  
Optimization Control ◽  
Power Split ◽  
Hybrid Electric ◽  
Real Time Optimization

This research focused on real-time optimization control to improve the fuel consumption of power-split hybrid electric vehicles. Particle swarm optimization (PSO) was implemented to reduce fuel consumption for real-time optimization control. The engine torque was design-variable to manage the energy distribution of dual energy sources. The AHS II power-split hybrid electric system was used as the powertrain system. The hybrid electric vehicle model was built using Matlab/Simulink. The simulation was performed according to US FTP-75 regulations. The PSO design objective was to minimize the equivalent fuel rate with the driving system still meeting the dynamic performance requirements. Through dynamic vehicle simulation and PSO, the required torque value for the whole drivetrain system and corresponding high-efficiency engine operating point can be found. With that, the two motor/generators (M/Gs) supplemented the rest required torques. The composite fuel economy of the PSO algorithm was 46.8 mpg, which is a 9.4% improvement over the base control model. The PSO control strategy could quickly converge and that feature makes PSO a good fit to be used in real-time control applications.

scholarly journals Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7919
Author(s):  
Penghui Qiang ◽  
Peng Wu ◽  
Tao Pan ◽  
Huaiquan Zang
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Hybrid Powertrain ◽  
Computational Load ◽  
Time Optimization ◽  
Equivalence Factor ◽  
Optimization Control ◽  
Parallel Hybrid ◽  
Equivalent Consumption Minimization Strategy ◽  
Real Time Optimization

Real-time energy management strategy (EMS) plays an important role in reducing fuel consumption and maintaining power for the hybrid electric vehicle. However, real-time optimization control is difficult to implement due to the computational load in an instantaneous moment. In this paper, an Approximate equivalent consumption minimization strategy (Approximate-ECMS) is presented for real-time optimization control based on single-shaft parallel hybrid powertrain. The quadratic fitting of the engine fuel consumption rate and the single-axle structure characteristics of the vehicle make the fitness function transformed into a cubic function based on ECMS for solving. The candidate solutions are thus obtained to distribute torque and the optimal distribution is got from the candidate solutions. The results show that the equivalent fuel consumption of Approximate-ECMS was 7.135 L/km by 17.55% improvement compared with Rule-ECMS in the New European Driving Cycle (NEDC). To compensate for the effect of the equivalence factor on fuel consumption, a hybrid dynamic particle swarm optimization-genetic algorithm (DPSO-GA) is used for the optimization of the equivalence factor by 9.9% improvement. The major contribution lies in that the Approximate-ECMS can reduce the computational load for real-time control and prove its effectiveness by comparing different strategies.

A System of Computer Real Time Optimization Control for Refining Industry

2013 ◽  
Vol 380-384 ◽  
pp. 342-346
Author(s):  
Jing Chen
Keyword(s):  
Real Time ◽  
Mathematic Model ◽  
Refining Industry ◽  
Oil Refining ◽  
Optimization Strategy ◽  
Oil Refineries ◽  
Time Optimization ◽  
Highly Nonlinear ◽  
Optimization Control ◽  
Real Time Optimization

The catalytic cracking unit, of oil refining industry, is a complex process characterized by its prolonged time delay and strong coupling ability. The unit is highly nonlinear and varies with time. Its mathematic model can be obtained online with difficulty, so as the parameters model. We describe the system of A System of Computer Real Time Optimization Control for Oil Refining Indus try ,and it is present that the system rely on coupling method of parameter identification and online simulation and synthesizing evolutionary technology of Multiple optimization strategy. It builds a single input, single output black box mathematic model. It is applied to predict the trend of system outputs, guiding the operations. This system has been adopted in several oil refineries, the benefits are pronounced.

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