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Creators/Authors contains: "Onori, Simona"

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  1. Abstract

    Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

     
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  2. 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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  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. 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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