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  1. This paper develops a co-simulation framework based on the use of the package LiveLinkTMfor Matlab to perform parameters optimization of dynamical systems implemented in COMSOL Multiphysics. The identification problem is recast as an optimization problem which is solved in Matlab. Code for the key steps of the approach is described in detail, and an implementation based on the particle swarm optimization (PSO) algorithm is proposed. The effectiveness and general applicability of the framework are shown for two energy systems: lithium-ion battery (LIB) and gasoline particulate filter (GPF). Matlab codes and COMSOL models for both case studies are made publicly available and can be used as a starting point to solve parameter identification problems for systems beyond the case studies presented here.

     
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  2. The Current practice of air-fuel ratio control relies on empirical models and traditional PID controllers, which require extensive calibration to maintain the post-catalyst air-fuel ratio close to stoichiometry. In contrast, this work utilizes a physics-based Three-Way Catalyst (TWC) model to develop a model predictive control (MPC) strategy for air-fuel ratio control based on internal TWC oxygen storage dynamics. In this paper, parameters of the physics-based temperature and oxygen storage models of the TWC are identified using vehicle test data for a catalyst aged to 150,000 miles. A linearized oxygen storage model is then developed from the identified nonlinear model, which is shown via simulation to follow the nonlinear model with minimal error during nominal operation. This motivates the development of a Linear MPC (LMPC) framework using the linearized TWC oxygen storage model, reducing the requisite computational effort relative to a nonlinear MPC strategy. In this work, the LMPC utilizing a linearized physics-based TWC model is proven suitable for tracking a desired oxygen storage level by controlling the commanded engine air-fuel ratio, which is also a novel contribution. The offline simulation results show successful tracking performance of the developed LMPC framework.

     
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  3. We propose a new pore-scale/channel model, or hybrid model, for the fluid flow and particulate transport in gasoline particulate filters (GPFs). GPFs are emission control devices aimed at removing particulate out of the exhaust system of a gasoline direct injection engine. In this study, we consider a wall-flow uncoated GPF, which is made of a bundle of inlet and outlet channels separated by porous walls. The particulate-filled exhaust gas flows into the inlet channels, and passes through the porous walls before exiting out of the outlet channels. We model the flow inside the inlet and outlet channels using the incompressible Navier–Stokes equation coupled with the spatially averaged Navier–Stokes equation for the flow inside the porous walls. For the particulate transport, the coupled advection and spatially averaged advection–reaction equations are used, where the reaction term models the particulate accumulation. Using OpenFOAM, we numerically solve the flow and the transport equations and show that the concentration of deposited particles is nonuniformly distributed along the filter length, with an increase of concentration at the back end of the filter as Reynolds number increases. Images from X-ray computed tomography (XCT)-scanning experiments of the soot-loaded filter show that such a nonuniform distribution is consistent with the prediction obtained from the model. Finally, we show how the proposed model can be employed to optimize the filter design to improve filtration efficiency. 
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  4. Gasoline particulate filters (GPFs) are practically adoptable devices to mitigate particulate matter emissions from vehicles using gasoline direct ignition engines. This paper presents a newly developed control-oriented model to characterize the thermal and soot oxidation dynamics in a ceria-coated GPF. The model utilizes the GPF inlet exhaust gas temperature, exhaust gas mass flow rate, the initial GPF soot loading density, and air– fuel ratio to predict the internal GPF temperature and the amount of soot oxidized during regeneration events. The reaction kinetics incorporated in the model involve the rates of both oxygen- and ceria-initiated soot oxidation reactions. Volumetric model parameters are calculated from the geometric information of the coated GPF, while the air–fuel ratio is used to determine the volume fractions of the exhaust gas constituents. The exhaust gas properties are evaluated using the volume fractions and thermodynamic tables, while the cordierite specific heat capacity is identified using a clean experimental data set. The enthalpies of the regeneration reactions are calculated using thermochemical tables. Physical insights from the proposed model are thus enhanced by limiting the number of parameters obtained from fitting to only those which cannot be directly measured from experiments. The parameters of the model are identified using the particle swarm optimization algorithm and a cost function designed to simultaneously predict both thermal and soot oxidation dynamics. Parameter identification and model validation are performed using independent data sets from laboratory experiments conducted on a ceria-coated GPF. This work demonstrates that the proposed model can be successfully implemented to predict ceria-coated GPF dynamics under different soot loading and temperature conditions. 
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