Optimal control and real-time optimization of mechanical multi-body systems

2003 ◽  
Vol 83 (10) ◽  
pp. 705-719 ◽  
Author(s):  
M. Gerdts
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Time Optimization ◽  
Multi Body ◽  
Real Time Optimization

Real-Time Implementation of Optimal Energy Management in Hybrid Electric Vehicles: Globally Optimal Control of Acceleration Events

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Zachary D. Asher ◽  
David A. Trinko ◽  
Joshua D. Payne ◽  
Benjamin M. Geller ◽  
Thomas H. Bradley
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Fuel Economy ◽  
Control Strategy ◽  
Limited Information ◽  
Optimal Control Strategy ◽  
Time Optimization ◽  
Vehicle Operation ◽  
Operation Knowledge ◽  
Real Time Optimization

Abstract Widely published research shows that significant fuel economy improvements through optimal control of a vehicle powertrain are possible if the future vehicle velocity is known and real-time optimization calculations can be performed. In this research, however, we seek to advance the field of optimal powertrain control by limiting future vehicle operation knowledge and using no real-time optimization calculations. We have realized optimal control of acceleration events (AEs) in real-time by studying optimal control trends across 384 real world drive cycles and deriving an optimal control strategy for specific acceleration event categories using dynamic programming (DP). This optimal control strategy is then applied to all other acceleration events in its category, as well as separate standard and custom drive cycles using a look-up table. Fuel economy improvements of 2% average for acceleration events and 3.9% for an independent drive cycle were observed when compared to our rigorously validated 2010 Toyota Prius model. Our conclusion is that optimal control can be implemented in real-time using standard vehicle controllers assuming extremely limited information about future vehicle operation is known such as an approximate starting and ending velocity for an acceleration event.

scholarly journals Experimental real-time optimization of a solid oxide fuel cell stack via constraint adaptation

Energy ◽  
2012 ◽  
Vol 39 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Gene A. Bunin ◽  
Zacharie Wuillemin ◽  
Grégory François ◽  
Arata Nakajo ◽  
Leonidas Tsikonis ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Real Time ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Fuel Cell Stack ◽  
Time Optimization ◽  
Real Time Optimization

Real-Time Optimization of Wind Farm Energy Capture With Delay Compensated Nested-Loop Extremum Seeking Control

2017 ◽  
Author(s):  
Zhongyou Wu ◽  
Yaoyu Li
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Wind Farm ◽  
Optimization Techniques ◽  
Extremum Seeking ◽  
Extremum Seeking Control ◽  
Energy Capture ◽  
Time Optimization ◽  
Real Time Optimization ◽  
Nested Loop

Real-time optimization of wind farm energy capture for below rated wind speed is critical for reducing the levelized cost of energy (LCOE). Performance of model based control and optimization techniques can be significantly limited by the difficulty in obtaining accurate turbine and farm models in field operation, as well as the prohibitive cost for accurate wind measurements. The Nested-Loop Extremum Seeking Control (NLESC), recently proposed as a model free method has demonstrated its great potential in wind farm energy capture optimization. However, a major limitation of previous work is the slow convergence, for which a primary cause is the low dither frequencies used by upwind turbines, primarily due to wake propagation delay through the turbine array. In this study, NLESC is enhanced with the predictor based delay compensation proposed by Oliveira and Krstic [1], which allows the use of higher dither frequencies for upwind turbines. The convergence speed can thus be improved, increasing the energy capture consequently. Simulation study is performed for a cascaded three-turbine array using the SimWindFarm platform. Simulation results show the improved energy capture of the wind turbine array under smooth and turbulent wind conditions, even up to 10% turbulence intensity. The impact of the proposed optimization methods on the fatigue loads of wind turbine structures is also evaluated.

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