Model-Based Control via Balanced Realization for Automotive Cold Start Hydrocarbon Reduction

This paper presents a framework for model order reduction based on Balanced Truncation (BT) method and Artificial Neural Networks (ANN). As social, architectural and technical models are witnessing rapid complexity increase, it has become essential to find solutions to obtain a lower complex version of models while still preserving the model integrity. The main idea of the proposed framework is to reduce uncertainty by cascading two different Model Order Reduction (MOR) approaches, the BT technique and the Jordan Recurrent Neural Network (JRNN). The reduced model obtained by the JRNN network is estimated using the Instrument Variable (IV) estimation approach. Finally, the BT reduced model and the estimated JRNN reduced model are cascaded to obtain the final reduced order version of the original plant. The cascading procedure takes the powerfulness of both techniques and present them in a single system capable to compensate for possible errors and uncertainties. Simulation results prove the efficiency of the proposed framework in terms of the deduction strength and the integrity level.

Wind generation has gained widespread use as a renewable energy source. Most wind turbines and other renewables connected to the grid through converters result in a reduction in the natural inertial response to grid frequency changes. The doubly-fed induction generator (DFIG) can be controlled to compensate for this reduction and, in fact, provide faster response than traditional synchronous machines. This paper proposes to design observer based output feedback linear quadratic regulator (LQR) and H∞ control laws to realize the inertia emulation function and deliver fast frequency support. The aim is to track the reference speed by a diesel synchronous generator (DSG) in order to reach the desired inertia. The control signal is computed based on a reduced order model using the balanced truncation technique. A comparison with selective modal analysis (SMA) and balanced truncation model reduction techniques is presented. Comprehensive results show the effective emulation of synthetic inertia by implementing the control laws on a nonlinear three-phase diesel-wind system. The proposed technique is analyzed for different short circuit ratio (SCR) scenarios.

Wind generation has gained widespread use as a renewable energy source. Most wind turbines and other renewables connected to the grid through converters result in a reduction in the natural inertial response to grid frequency changes. The doubly-fed induction generator (DFIG) can be controlled to compensate for this reduction and, in fact, provide faster response than traditional synchronous machines. This paper proposes to design observer based output feedback linear quadratic regulator (LQR) and H control laws to realize the inertia emulation function and deliver fast frequency support. Furthermore, the aim is to track the reference speed by a diesel synchronous generator (DSG) in order to reach the desired inertia. The control signal is computed based on a reduced order model using the balanced truncation technique. A comparison with selective modal analysis (SMA) and balanced truncation model reduction techniques is presented. Comprehensive results show the effective emulation of synthetic inertia by implementing the control laws on a nonlinear three- phase diesel-wind system. The proposed technique is analyzed for different short circuit ratio (SCR) scenarios.

Balanced truncation (BT) model order reduction (MOR) is known for its superior accuracy and computable error bounds. Balanced stochastic truncation (BST) is a particular BT procedure that provides a general, structure-independent MOR framework to preserve both passivity and stability of original models. Its application toward large scale systems, however, has been limited by the complexity of solving large size continuous time algebraic Riccati equations (CAREs). This paper introduces a novel quadratic extension of the alternating direction implicit (ADI) iteration, called QADI, that efficiently solves a CARE. A Cholesky factor variant of QADI, called CFQADI, further exploits low rank matrices and and produces solution in factor form that greatly accelerates BST. Remarkable efficiency of the proposed BST/(CF)QADI integration is demonstrated with numerical examples.

The cold start hydrocarbon emission from the increasing population of two wheelers in countries like India is one of the research issues to be addressed. This work describes the prediction of cold start hydrocarbon emissions from air cooled spark ignition engines through fuzzy logic technique. Hydrocarbon emissions were experimentally measured from test engines of different cubic capacity, at different lubricating oil temperature and at different idling speeds with and without secondary air supply in exhaust. The experimental data were used as input for modeling average hydrocarbon emissions for 180 seconds counted from cold start and warm start of gasoline bike engines. In fuzzy logic simulation, member functions were assigned for input variables (cubic capacity and idling rpm) and output variables (average hydrocarbon emission for first 180 seconds at cold start and warm start). The knowledge based rules were adopted from the analyzed experimental data and separate simulations were carried out for predicting hydrocarbon emissions from engines equipped with and without secondary air supply. The simulation yielded the average hydrocarbon emissions of air cooled gasoline engine for a set of given input data with accuracy over 90%.

<div class="section abstract"><div class="htmlview paragraph">Reducing the greenhouse emissions from on-road freight vehicles to meet the climate change mitigation objectives, has become a prime focus of regulatory authorities all over the world. Besides India, the United States, the European Union, Canada, Japan, and China have already established or planned heavy-duty vehicle efficiency regulations addressing CO<sub>2</sub> and NO<sub>X</sub> emissions. In addition, Argentina, Brazil, Mexico, and South Korea are all in various stages of developing policies to improve the efficiency of their commercial vehicle fleets. For CO<sub>2</sub> emissions reduction standards, the U.S. mandates 27% reduction by 2027, EU is calling for 15% reduction by 2025, China for 27% by 2019 over 2012 levels, and India is mandating 10%-15% reduction by 2021 for phase 2 of the new standard. There has also been considerable focus on further reduction in NO<sub>X</sub> emissions from current levels (0.2 g/hp-hr), to the proposed ultra-low NOx standards (0.02 g/hp-hr) in the U.S. for heavy duty engines by 2024.</div><div class="htmlview paragraph">Given these planned and proposed regulatory standards being implemented around the globe, there have been substantial studies and publications focusing on exploring and evaluating technologies that can help deliver the lower tailpipe NOx targets and understand the CO<sub>2</sub> impact associated with it. Majority of the NO<sub>X</sub> emissions from engine, occur during the cold-start portion of the transient regulatory cycles, like HD FTP and WHTC. This is because, a typical heavy-duty diesel aftertreatment system does not achieve substantial NO<sub>X</sub> reduction until approximately 400-500 seconds into the cold-start cycle due to lack of heat from the engine. The result is untreated NOx escaping through to tailpipe. To achieve low NOx emission levels over the composite transient cycles, the engine must provide rapid exhaust heat energy, during the cold-start portion, to reduce the time required by the SCR catalyst to reach catalyst light-off temperature, while controlling the NO<sub>X</sub> emissions. Moreover, high NO<sub>X</sub> conversion efficiency must be maintained during the hot-start portion of cycle. For a conventional heavy-duty engine, providing rapid exhaust heat while controlling NOx emissions has been a challenge, because these are competing demands. Implementing secondary or auxiliary heat sources downstream in the exhaust after treatment system (ATS) comes at CO<sub>2</sub> penalty and adds significant cost and complexity. This has been established in recent publications by organization like SwRI [<span class="xref">1</span>], CARB and Bosch [<span class="xref">2</span>].</div><div class="htmlview paragraph">Achates Power Opposed Piston (OP) engine technology provides ideal solution to this challenge. The opposed-piston engine has several inherent advantages over conventional four-stroke engines, like higher BTE (15-30% higher), higher power density, an air-system that results in reduced pumping work, the ability to control residual combustion gases, two fuel injectors per cylinder providing greater timing flexibility, and the ability to provide rapid engine out heat and temperature rise for the exhaust emission system while maintaining low engine out NO<sub>X.</sub></div><div class="htmlview paragraph">This paper demonstrates results from cold and hot start transient WHTC testing and WHSC testing, conducted at Achates Power, on a three-cylinder opposed-piston engine. Results show that the Achates Power OP Engine can deliver engine out heat and temperature rise that exceeded and sustained catalyst light-off temperature thresholds (250°C) within the first 60-100 seconds in the cold start cycle, while controlling engine out NOx to lower levels when compared to a conventional four-stroke heavy-duty diesel engine. As a result, the OP Engine not only meets current BS-VI and future regulatory emissions requirements but is also able to do so with a significant CO<sub>2</sub> emissions advantage. Furthermore, the inherent advantages of the OP Engine offer unique aftertreatment optimization and downsizing opportunities thereby enabling cost reduction.</div></div>

In this paper a novel model order reduction method for nonlinear models, based on decoupled multi-model, via trajectory piecewise-linearization is proposed. Through different strategies in trajectory piecewise-linear model reduction, model order reduction of a trajectory piecewise-linear model based on output weighting (TPWLOW), has been developed by authors of current work. The structure of mentioned work was founded based on Krylov subspace method, which is appropriate for high order models. Indeed the size of the Krylov subspaces may increase with the number of inputs of the system. As a result, the use of Krylov subspace method may become deficient the case for multi-input systems of order few decades. This paper aims to generalize the idea of model reduction of TPWLOW model by concentrating on balanced truncation technique which is appropriate for medium size systems. In addition, the proposed method either guarantees or provides guaranteed stability in some mentioned conditions. Finally, applicability of the reduced model, instead of high-order decoupled multi-model in weighting multi-model controllers, is investigated through the control of a nonlinear Alstom gasifier benchmark.

In this paper, a model reduction method for a linear time invariant stable system whose poles are located in a specified disk on the complex left half plane is considered. This method is based on the balanced truncation technique that is often used for reducing a high order stable system to low order stable one. A number of properties of the balanced truncation technique are maintained. The error bound is evaluated using Hankel singular values of the original system. As one of applications of the method, the authors tackled a reduction problem of an H/sub /spl infin// controller.

In the present work, multiple non existing model order reduction (MOR) techniques for unstable second‐order form systems (SOSs) are proposed. For unstable SOSs, continuous‐time algebraic Lyapunov equations get unsolvable that halt the reduction process. To avoid this problem, unstable SOS is first decomposed into stable and unstable portions and balanced truncation is applied to the stable part. The obtained reduced order model (ROM) for the stable portion is augmented with the unstable portion to obtain the overall reduced system. It is observed that the second‐order structure in ROM for the first technique gets lost as well as augmented unstable dynamics degrade the ROM performance. To cater to these constraints, two structure‐preserving second‐order balanced truncation techniques for unstable SOSs are proposed in second part. System is first stabilized using the Bernoulli feedback stabilization procedure and then gramians are computed for the stabilized system. Further, gramians are partitioned into position and velocity portions to achieve structure preservation in ROM and balanced truncation is applied. As second technique retains second‐order structure as well as involves stabilized system dynamics, this technique far closely approximates original system behavior. Proposed techniques are tested on multiple systems and results certify the correct development of proposed techniques that can be utilized for MOR applications of unstable SOSs. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A new mixed method for relative error model order reduction is proposed. In the proposed method the frequency domain balanced stochastic truncation method is improved by applying the generalized singular perturbation method to the frequency domain balanced system in the reduction procedure. The frequency domain balanced stochastic truncation method, which was proposed in [15] and [17] by the author, is based on two recently developed methods, namely frequency domain balanced truncation within a desired frequency bound and inner-outer factorization techniques. The proposed method in this paper is a carry over of the frequency-domain balanced stochastic truncation and is of interest for practical model order reduction because in this context it shows to keep the accuracy of the approximation as high as possible without sacrificing the computational efficiency and important system properties. It is shown that some important properties of the frequency domain stochastic balanced reduction technique are extended to the proposed reduction method by using the concept and properties of the reciprocal systems. Numerical results show the accuracy, simplicity and flexibility enhancement of the method.

Emission characteristics of a diesel engine with an electrically heated catalyst under cold start conditions

Effects of split diesel injection strategies on combustion, knocking, cyclic variations and emissions of a natural gas-diesel dual fuel medium speed engine

CFD analysis of combustion and emission characteristics of biodiesel under conventional and late-injection based premixed combustion conditions

Quadrotors have become extremely popular in a number of areas with their ability to control their attitude. There are many control methodologies of state-feedback that are created to optimize nonlinear multivariable control. This paper presents a state estimation scheme founded on subspace identification (SI) in quadrotor attitude control, which is useful to work in the conditions of no explicit model and with noisy signal conditions. The main objective is to come up with an observer-based discrete state feedback control system that would satisfy the desired attitude control performance objectives. In order to have the correct modeling and estimation of state, a pseudo-random binary sequence (PRBS) is produced as the reference input, which is optimized according to the settling time and operating frequency identified with the help of closed-loop experiments. The given SI solution is aimed at overcoming the difficulty of estimating the yaw-axis state of the quadrotor attitude model with interference and measurement noise. A control framework (CF) model of high order is first being identified by the 4S identification technique and model order reduction by the balanced stochastic model truncation (BSMT) technique. Lastly, the similarity transform is employed to determine the relationship between the actual quadrotor model and the computer-based CF model. The analysis of the control loop performance and its validation are mainly in the yaw-axis attitude control loop.

<div class="section abstract"><div class="htmlview paragraph">This is a follow-up paper to the two previous reports [<span class="xref">1</span>, <span class="xref">2</span>] regarding the development of a zeolite-based, hydrocarbon (HC) trap-type cold-start catalyst (CSC) as a method to meet future vehicle tailpipe emission standards. In this paper, vehicle tests at a low ambient temperature of -7°C have been performed and the CSC has been shown to further decrease the tailpipe cold start non-methane hydrocarbon (NMHC) emissions by 59% when compared to a standard 23°C WLTC test. This work has proven that the increased presence of condensed water at low ambient temperatures within the exhaust system does not affect the ability to provide a NMHC reduction, in fact the lower ambient temperature enables an increase in the reduction capability due to the ability to retain and then release the stored NMHC in a more controlled manner. Additionally, the impact of the zeolite loading level was investigated and the high zeolite loading within a CSC did improve the cold-start NMHC but the benefits did become saturated as the total zeolite used was further increased. Furthermore, tests with aged CSC parts, that were purposely chosen to be aged at temperature (800°C) above those expected for the target vehicle application, have also been proven to retain the significant amount of the NMHC cold start emissions reduction for the order of 48% to 92% when compared with a baseline fresh sample. The performance retention implies that a CSC can be positioned in several potential locations within a target vehicle aftertreatment system without undue concerns for thermal deterioration. The limitation still being a close-coupled location because the CSC maximum NMHC storage capability is hindered by high temperature and a zeolite coating decreases the TWC washcoat loading in the CSC, which cannot offer the conversion capability for the additional exhaust pollutants for NOx and CO when compared to a standard three-way catalyst (TWC). Furthermore, for repeatability, the CSC must be in a position that will attain sufficient exhaust gas temperature above 550°C for soot reduction [<span class="xref">2</span>, <span class="xref">3</span>], to be able to release all the cold start stored NMHC (clean up deposit), to ensure it is always able to store when the next engine cold start occurs. As an addition to this work, the CSC technology has been applied to a current US Tier3 Bin30 hybrid electric vehicle (HEV), and it has been possible to show a decrease in the vehicle NMHC tailpipe emissions to achieve below Tier 3 Bin 20, thus showing the possibility of an emission improvement with no changes other than the addition of the CSC hardware.</div></div>