Reduced order model (ROM) of a pouch type lithium polymer battery based on electrochemical thermal principles for real time applications

2013 ◽  
Vol 97 ◽  
pp. 66-78 ◽  
Author(s):  
Xueyan Li ◽  
Meng Xiao ◽  
Song-Yul Choe
Keyword(s):  
Real Time ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Lithium Polymer Battery ◽  
Real Time Applications ◽  
Polymer Battery

scholarly journals A Reduced-order Model for Real-time NMPC of Ethanol Steam Reformers

2021 ◽  
Vol 54 (3) ◽  
pp. 103-108
Author(s):  
Pedro Reyero ◽  
Xinwei Yu ◽  
Carlos Ocampo-Martinez ◽  
Richard D. Braatz
Keyword(s):  
Real Time ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Steam Reformers ◽  
Ethanol Steam

scholarly journals Model Order Reduction for Stochastic Optimal Control

2012 ◽  
Author(s):  
Ngoc-Hien Nguyen ◽  
Karen Willcox ◽  
Boo Cheong Khoo
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Real Time ◽  
Stochastic Optimal Control ◽  
Reduced Order Model ◽  
Full Model ◽  
Order Model ◽  
Reduced Order ◽  
Flow Quality

This work presents an approach to solve stochastic optimal control problems in the application of flow quality management in reservoir systems. These applications are challenging because they require real-time decision-making in the presence of uncertainties such as wind velocity. These uncertainties must be accounted for as stochastic variables in the mathematical model. In addition, computational costs and storage requirements increase rapidly due to the stochastic nature of the simulations and optimisation formulations. To overcome these challenges, an approach is developed that uses the combination of a reduced-order model and an adjoint-based method to compute the optimal solution rapidly. The system is modelled by a system of stochastic partial differential equations. The finite element method together with collocation in the stochastic space provide an approximate numerical solution—the “full model”, which cannot be solved in real-time. The proper orthogonal decomposition and Galerkin projection technique are applied to obtain a reduced-order model that approximates the full model. The conjugate-gradient method with Armijo line-search is then employed to find the solution of the optimal control problem under the uncertainty of input parameters. Numerical results show that the stochastic control yields solutions that are above the bound of the set solutions of the deterministic control. Applying the reduced model to the stochastic optimal control problem yields a speed-up in computational time by a factor of about 80 with acceptable accuracy in comparison with the full model. Application of the optimal control strategy shows the potential effectiveness of this computational modeling approach for managing flow quality.

Predicting home thermal dynamics using a reduced-order model and automated real-time parameter estimation

2019 ◽  
Vol 198 ◽  
pp. 305-317 ◽  
Author(s):  
Junke Wang ◽  
Choon Yik Tang ◽  
Michael R. Brambley ◽  
Li Song
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Time Parameter ◽  
Reduced Order Model ◽  
Order Model ◽  
Thermal Dynamics ◽  
Reduced Order
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