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Gasoline particulate filters (GPF) are becoming a standard technology in gasoline engines because of the need for compliance with particulate matter (PM) emissions requirement. Generally, GPFs can be placed after a three-way catalysts (TWC) in the same can, or in a separate can downstream. As typical wall-flow filters, particulate matter is removed from the exhaust by physical filtration using a honeycomb structure similar to an emissions catalyst substrate but with the channels blocked at alternate ends. The goal of work was to the study the filtration efficiency and regeneration capacity of the GPF. Laboratory emission tests were carried out for one 2.0L gasoline turbo direct injection (GTDI) prototype vehicle according to the new World Harmonized Light Vehicle Test Cycle (WLTC). The data showed that the vehicle has a reliable and high GPF filtration efficiency to intercept the particulate pollution from the exhaust. Meanwhile, five 2.0L GTDI prototype cars from respective China stage 6 platforms were used for further study of soot accumulation and regeneration capacity. The results, in terms of modeled emissions in tests under real driving conditions of China cities (Nanjing and Hainan), showed that the heavy traffic road conditions lead to a low probability of soot regeneration. However, the GPF regeneration capacity of the test convoy was still adequate. In addition, the drive cycle exhibiting good road conditions with more aggressive driving behaviors offered a better regeneration environment due to the elevated GPF temperature and more frequent occurrence of deceleration fuel shut off (DFSO) creating an environment of excess oxygen content.

The problem of controlling traffic using connected automated vehicles is approached by utilizing Lagrangian traffic models. A continuum model with time delay is introduced in the Lagrangian frame in order to capture the open loop dynamics of the traffic behind a vehicle of prescribed motion. The stability of the open loop system is analyzed and compared to that of a benchmark car-following model. Finally, the Lagrangian traffic models are used to propose a longitudinal controller for connected automated vehicles that allows them to respond to connected vehicles behind to stabilize the upstream traffic in a closed loop fashion.

During cold-starts, diesel engines equipped with aftertreatment systems typically use combustion phasing retard to increase exhaust gas enthalpy to hasten catalyst light-off, resulting in lower tailpipe emissions. Although later combustion phasing can help achieve faster catalyst light-off, combustion variability increases which can physically manifest as vibration and erratic engine behavior. To achieve faster catalyst light-off while remaining within combustion variability constraints, the premise of closed-loop control of Coefficient of Variation of IMEP (COVIMEP) to a target value using feedback from an in-cylinder pressure sensor has been explored. COVIMEP controllers have been designed using a model and validated via simulation and experiment at steady-state. The simulation and experimental results indicate that closed-loop COVIMEP control is a viable technique for retarding combustion phasing to the combustion variability limit at steady-state conditions.

In this paper, the development of a control-oriented piston temperature model for diesel engines is discussed. Using the underlying energy balance at the piston, a one-state piston temperature model was developed based on a thermal resistance concept. The model is composed of five sub-models: an engine model, a heat distribution model, a piston temperature model, an initial piston temperature model, and a maximum piston temperature model. In the engine model, the combustion heat transferred to the engine is calculated based on the energy balance in the cylinder chamber. The heat distribution model, which is a main feature in this model, determines the heat transferred to the piston using two maps as a function of engine speed and fuel depending on the piston cooling jet (PCJ) operation. The energy balance at the piston is applied to calculate the mean piston temperature, and the initial piston temperature is determined by the arbitration between the piston and the oil temperatures. The maximum piston temperature is estimated using a simple linear correction to the mean piston temperature. Integrating all sub-models in the Simulink platform, the model was identified and validated using piston temperature measurements under steady-state fuel steps as well as transient tests. There is a good agreement between the modeled and the measured piston temperatures with less than 4.1°C of root-mean-square-error (RMSE) over transient emissions cycles (FTP-75, LA92, and HWEFT). The modeled piston temperature can be used as an input to the control strategy of variable cooling devices, such as a variable displacement oil pump.

Predictive models of engine-out NOx emissions continue to be a topic of active research in the diesel powertrain community. Renewed interest in the use of in-cylinder pressure sensor has presented ...

A real-time implementable, zero-dimensional model for predicting engine-out emissions of nitrogen oxides using in-cylinder pressure measurements is developed. The model is an extension of existing works in open literature that align well with the objectives of real-time implementation. The proposed model uses a simplified Zeldovich NOx mechanism that uses combustion-related parameters derived from simplified thermodynamic and combustion sub-models. The performance of the model is discussed for both a heavy-duty and a light-duty diesel engines. The model behavior is evaluated under input uncertainty so as to provide realistic performance bounds.

Heterogeneous traffic with a mixture of human-driven and connected automated vehicles is discussed to study how the penetration rate and the control design of connected automated vehicles affect the traffic flow on a large scale. Continuum traffic models are constructed by incorporating time delays to take into account the reaction time of human drivers and the delays in the control loops of connected automated vehicles. It is shown that Lagrangian delayed continuum models are suitable for studying heterogeneity, introducing delay, and taking into account the on-board traffic data used by the controllers of connected automated vehicles. We show that these models possess realistic stability properties and are capable of capturing the large-scale dynamics of vehicle automation-induced and connectivity-induced heterogeneity.

In this paper we build Lagrangian continuum traffic flow models that are able to utilize trajectory information transmitted between connected vehicles via vehicle-to-everything (V2X) connectivity. These models capture three important features of traffic flow: (i) the propagation of congestions in time, (ii) the propagation of congestions in space, (iii) the string instability (or stability) of traffic that is related to the amplification (or decay) of traffic waves. The proposed models have only three tunable parameters to capture these three features. One of these parameters is the time delay that models the actuator lag in vehicle dynamics, the reaction time of human drivers, and the communication and feedback delays of connected and automated vehicles. The proposed Lagrangian continuum traffic models with delays establish a framework for traffic prediction and control. On one hand, connected vehicles may use predictions about the future motion of neighboring vehicles or their own. On the other hand, the continuum nature of these models allows one to study the large-scale impact of connected vehicles on the traffic flow. This opens the path for Lagrangian (vehicle-based) traffic control that supplements existing Eulerian (location-based) traffic control techniques.

Diesel engine emission cycle data shows that major portions of cycle emissions are produced at the beginning of the test, when the aftertreatment is not at operational temperature (prior to “light-off”) [1]. To reduce diesel emissions, aggressive combustion phasing retard via injection timing can be used to achieve faster aftertreatment light-off, but this method is limited because of vibration and harshness concerns associated with the combustion variability induced by the late combustion phasing. In order to achieve aggressive exhaust heating while mitigating combustion variability concerns, the premise of controlling combustion variability is explored. In particular, a controller will use real-time measurements of combustion features and control injection timing to maintain an acceptable level of combustion variability. The closed loop controller tuning requires an understanding of combustion variability behavior as a function of combustion phasing retard. The characterization of combustion variability using engine experiments is presented, and the findings are used to develop a control-oriented combustion variability model consisting of regressions of the statistics of IMEP as a function of fuel and timing offsets.

Urea based selective catalytic reduction (SCR) control strategies regulate urea dosing in diesel powertrains with the goal of achieving NOx emissions targets while being robust to transient disturbances such as exhaust temperature. A model based approach is typically pursued, where an estimated quantity of NH3 stored on the SCR catalyst is controlled to a temperature dependent setpoint. The choice of setpoint, however, is typically defined heuristically which leads to suboptimal NOx and NH3 emission trade-offs. Alternatively, this paper approaches SCR control from a stochastic dynamic programming (SDP) perspective. The dosing control law is formulated by solving an infinite-horizon stochastic optimization problem using a probabilistic model of the future SCR disturbances. Simulations over standard drive cycles show the SDP based SCR control laws outperform a standard model based PI control approach, in terms of total NOx emissions for a given level of NH3 slip, by between 5% and 15%. The SDP controllers are shown to move closer to the global optimal result found via deterministic dynamic programming. Additionally, the SDP approach provides a framework to evaluate potential controller performance improvements made available by adding parameters to the control law. In this way, the incremental benefit of having an additional state in the state-feedback control law can be weighed against the increased memory cost of the extra dimension.

Combustion phasing retard is commonly used on diesel engines to achieve faster aftertreatment light-off and lower tailpipe emissions. Aggressive combustion phasing retard can achieve higher exhaust gas enthalpy at the cost of increased combustion variability, which can create vehicle noise, vibration, and harshness (NVH) issues. To avoid such issues while maximising exhaust heating, feedback from cylinder pressure sensors can be used to control the coefficient of variation of the indicated mean effective pressure (CoVIMEP) to a maximum allowable value. Therefore, a control-oriented combustion model that captures the stochastic properties of the cycle-to-cycle variability for IMEP has been developed for control design. Unbiased estimation methods for the CoVIMEP using finite impulse response (FIR) and infinite impulse response (IIR) filters are discussed. Finally, the performance of the estimation strategy is tested during an engine warm-up phase to show that it is a suitable alternative for generating a feedback signal for control.

One potential method to reduce fuel consumption in diesel engines with variable geometry turbines (VGT) and exhaust gas recirculation (EGR) is to reduce the transient engine pumping work through improved EGR-VGT control. Numerical dynamic programming is applied to investigate optimal EGR-VGT control policies for reduced pumping work on a three-state model of a 6.7-liter medium-duty diesel engine. Optimality is defined by a multi-objective cost function that penalizes pumping work, EGR rate control error, and boost pressure control error. Multiple dynamic programs, each with a different set of cost function weights, are performed over an acceleration in the Heavy-Duty Federal Test Procedure cycle to generate the optimal trade-off between the stated objectives. Additionally, a production-representative EGR-VGT controller is simulated, and the resulting suboptimal performance is compared to the optimal frontier to establish the potential fuel consumption benefit of improved EGR-VGT control.

This paper presents a method to predict the transient turbocharger shaft speed for turbocharged diesel engines based on a dynamic observer. The observer uses the turbocharger torque balance as a dynamic term and the static turbo map as a static term. The latter includes the intake and exhaust side together with turbine shaft speed. The experimental results show that this observer can provide an estimate of turbocharger shaft speed without introducing large delays in transient operation and provide a good match with the turbo map data in steady state. However, this method has to calculate the turbine and compressor torque with a high accuracy which makes it almost unrealizable because of the complexity of turbine and compressor torque calculation. A novel method was designed to solve this problem by using an integral term at steady state to eliminate the torque modelling error.

In this paper we tackle issues relevant to model based control design for a Urea based Selective Catalytic Reduction (SCR) process relevant to automotive applications. A three state, control oriented, lumped parameter model of the system is used to investigate essential controllability and observability properties of the Urea-SCR plant. Results from the controllability and observability analysis of both nonlinear and linearized models are shown to have realistic implications. Observer design for predicting gas phase ammonia slip is outlined and results presented. An altered definition of the catalyst efficiency is used in control design. It is shown that this altered definition lends itself readily to control synthesis in the Sliding Mode framework while satisfying the dual control objectives of maximizing NOx reduction and minimizing ammonia slip.

This paper considers the problem of real-time trajectory generation and tracking for nonlinear control systems. We employ a two-degree-of-freedom approach that separates the nonlinear tracking problem into real-time trajectory generation followed by local (gain-scheduled) stabilization. The central problem which we consider is how to generate, possibly with some delay, a feasible state space and input trajectory in real time from an output trajectory that is given online. We propose two algorithms that solve the real-time trajectory generation problem for differentially flat systems with (possibly non-minimum phase) zero dynamics. One is based on receding horizon point to point steering, the other allows additional minimization of a cost function. Both algorithms explicitly address the tradeoff between stability and performance and we prove convergence of the algorithms for a reasonable class of output trajectories. To illustrate the application of these techniques to physical systems, we present experimental results using a vectored thrust flight control experiment built at Caltech. A brief introduction to differentially flat systems and its relationship with feedback linearization is also included. © 1998 John Wiley & Sons, Ltd.

The use of differential e atness for computation of a nominal trajectory for fast transition between e ight modes of autonomous vehicles is investigated. Differential e atness of an approximate model of the longitudinal dynamics of a thrust-vectored aircraft is used to achieve fast switching between e ight modes. We conclude that steering to the trimmed state of the full model is of crucial importance for good performance. Simulations and experimental data for a thrust-vectored e ight-control experiment at Caltech are provided to validate the approach.

A thermodynamics-based computationally efficient mean value engine model that computes ignition delay, combustion phases, exhaust temperature, and indicated mean effective pressure has been developed for the use of control strategy development. The model is derived from the thermodynamic principles of ideal gas standard limited pressure cycle. In order to improve the fidelity of the model, assumptions that are typically used to idealize the cycle are modified or replaced with ones that more realistically replicate the physical process such as exhaust valve timing, in-cylinder heat transfer, and the combustion characteristics that change under varying engine operating conditions. The model is calibrated and validated with the test data from a Ford 6.7 liter diesel engine. The mean value model developed in this study is a flexible simulation tool that provides excellent computational efficiency without sacrificing critical details of the underlying physics of the diesel combustion process.

This paper considers the problem of real time trajectory generation and tracking for nonlinear control systems. We employ a two degree of freedom approach that separates the nonlinear tracking problem into real time trajectory generation followed by local (gain-scheduled) stabilization. The central problem which we consider is how to generate, possibly with some delay, a feasible state space and input trajectory in real time from an output trajectory that is given online. We propose two algorithms that solve the real time trajectory generation problem for differentially flat systems with (possibly non-minimum phase) zero dynamics. One is based on receding horizon point to point steering, the other allows additional minimization of a cost function. Both algorithms explicitly address the tradeoff between stability and performance and we prove convergence of the algorithms for a reasonable class of output trajectories. To illustrate the application of these techniques to physical systems, we present experimental results using a vectored thrust flight control experiment built at Caltech. A brief introduction to differentially flat systems and its relationship with feedback linearization is also included.

Identification of the injection timing drift in modern diesel engine is investigated. The cylinder balancing action which is most common in modern diesel engine is utilized in locating the injection timing (injection timing with the minimum cylinder balancing correction). To find the minimum, several approaches are examined, including a parameterization based approach, sliding mode based self optimizing control and the simplex search. The proposed approaches are evaluated with experimental data from a vehicle.Copyright © 2011 by ASME

The increasingly stringent emissions regulations and needs for higher power density for both turbo-diesel passenger vehicle and commercial vehicles have demanded significant alterations to the basic architecture of turbochargers. An attractive option for providing a high-boost system is the use of two-stage turbocharger which consists of two different size turbochargers connected in series that may or may not utilize bypass regulation. The exhaust mass flow is expanded by the high pressure turbine to the low pressure turbine, and on the other side the air flow is compressed through the low pressure compressor to the high pressure compressor. This increases the complexity of the air-charging system and requires new methodologies for modeling and control. A two-stage turbocharger model is presented in this paper. The total efficiency of the two-stage compressor, which poses the biggest problem in two-stage turbocharger modeling, was derived based on a second law analysis. A new parameter, compressor temperature ratio, was introduced as a linkage between the two stage compressors and also used to predict the two-stage compressor outlet temperature. Extrapolation to lower turbocharger speeds and compressor flow rates by using curve fitting methods was also discussed. The model for a two-stage turbine with a bypass valve is derived in the same way. Engine dynamometer tests have been performed to identify the model parameters and to validate the model structure. The test results show a good agreement between the model predictions and test data. In conclusion, this two stage turbocharger model is suitable for turbocharger control design and the estimation of some key turbocharger parameters.Copyright © 2007 by ASME

Diesel particulate filters (DPFs) are a technology likely to be deployed to meet future stringent emission levels for heavy and light duty diesel powertrains in North America and Europe. This paper discusses experimental results in the active regeneration of DPFs. Attention is given to the system components, the information based on which regeneration is triggered, and the means to achieve a regeneration. The paper will report on successful regenerations under several extreme conditions.

Modern Diesel engines are faced with two major emission challenges in their quest to become an environmentally compatible source of motive power, Nitrogen Oxides (NOx ) and Particulate Matter (PM). Advanced techniques, such as High Pressure Common Rail (HPCR) fuel injection combined with multiple injections per cycle, are commonly employed to minimize in-cylinder production of NOx and PM. However, to meet the EPA mandated standards it is essential that an aftertreatment system be used. Typical Diesel aftertreatment systems will employ some form of a NOx reducing catalyst and a particulate trap for PM removal. Lean NOx traps and Selective Catalytic Reduction (SCR) are examples of aftertreatment techniques frequently used in Diesel engine applications. Whatever the method of choice, knowledge of the feed-gas NOx concentration is essential for not only assessing the performance of the NOx reduction catalyst but also for defining the control strategy for the aftertreatment system with respect to the management of the reductant quantity to be injected. In the absence of a dynamic NOx emission model the control algorithm has to depend on either a NOx sensor upstream of the catalyst or a static map of the feedgas NOx level as some function of engine influence factors. While NOx sensors add to the overall system cost, creating an accurate and representative NOx map over the entire engine operating range can be a challenging task. A dynamic NOx model would, in theory solve, both of these problems, however it is essential that the model be simple and implementable in real time. A model that uses inputs that are not available from the standard measurement set is of little use for real time control applications as is a model that predicts the temporal and spatial NOx evolution in the engine combustion chamber as such models tend to be computationally expensive. However, it is essential that the model behave like a fast NOx sensor in predicting cycle averaged NOx emission. In this paper we present an approach to developing such a model and present results from model validation against vehicle data. The basic structure of the model relies on well-known mechanisms that describe the NOx creation and decomposition chemical kinetics. Simplifying assumptions are made to allow available measurements to be used as inputs to the model. This leads to a parametric model where the unknown parameters are estimated using Nelder Mead optimization routine available in Matlab®. Model validation against vehicle data is also presented.Copyright © 2003 by ASME

The leading aftertreatment technologies for NOx removal from the exhaust gas of lean burn engines, Diesels in particular, are urea based Selective Catalytic Reduction (SCR), Lean NOx Traps (LNT) and Active Lean NOx Catalysts (ALNC). It is generally believed that the SCR technique has the potential of providing the best NOx conversion efficiency relative to the other techniques. Nonetheless, it is crucial that the high conversion efficiencies be achieved with a minimum slippage of unreacted ammonia as tail pipe emissions. This necessitates a precise control over the urea injection process. The complex behavior of the catalyst substrate with respect to adsorption and desorption of ammonia in conjunction with a lack of “stored ammonia” sensing capabilities makes the control problem challenging. In this paper we present a model-based control design approach using a lumped parameter model of an SCR system that includes the essential dynamics of the plant. The model includes the adsorption, desorption and surface coverage dynamics, along with the NOx reduction and ammonia oxidation dynamics based on the relevant chemical reaction rates.

This paper introduces the concept of outer flatness, a derivative of differential flatness. Outer flatness describes a system that can be split in 2 subsytems, a non-flat inner system and a flat outer system. The outputs of the outer system are the tracking outputs of interest. The inputs of the outer system are the outputs of the inner system, and not subject to our direct control. The inputs of the inner system are the real actuator inputs. This system structure is also present in backstepping and dynamic inversion. We present two theorems on exponential and bounded tracking for outer flat systems, based on Lyapunoff arguments. We validate the approach with simulations and experiments on a model helicopter.