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Lean NOx Trap (LNT) is an aftertreatment device typically used to reduce oxides of nitrogen (NOx) emissions for a lean burn engine. NOx is stored in the LNT during the lean operation of an engine. When the air-fuel ratio becomes rich, the stored NOx is released and catalytically reduced by the reductants such as CO, H2 and HC. Tailpipe NOx emissions can be significantly reduced by properly modulating the lean (storage) and rich (purge) periods. A control-oriented lumped parameter model is presented in this paper. The model captures the key steady state and transient characteristics of an LNT and includes the effects of the important engine operating parameters. The model can be used for system performance evaluation and control strategy development.

This paper summarizes a systematic approach to control of nonlinear automotive systems exposed to fast transients. This approach is based on a combined application of hardware characterization, which inverts nonlinearities, and conventional Proportional-plus-Integral-plus-Derivative (PID) control. The approach renders the closed-loop system dynamics more transparent and simplifies the controller design and calibration for applications in automotive industry. The authors have found this approach effective in presenting and teaching PID controller design and calibration guidelines to automotive engineering audience, who at times may not have formal training in controls but need to understand the development and calibration process of simple controllers.

An electromechanically driven valve train offers unprecedented flexibility to optimize engine operation for each speed load point individually. One of the main benefits is the increased fuel economy resulting from unthrottled operation. The absence of a restriction at the entrance of the intake manifold leads to wave propagation in the intake system and makes a direct measurement of air flow with a hot wire air meter unreliable. To deliver the right amount of fuel for a desired air-fuel ratio, we therefore need an open loop estimate of the air flow based on measureable or commanded signals or quantities. This paper investigates various expressions for air charge in camless engines based on quasi-static assumptions for heat transfer and pressure.

For urea Selective Catalytic Reduction (SCR) systems, adaptive control is of interest to provide a capability of maintaining high NOx conversion efficiency and low ammonia slip in the presence of uncertainties in the system. In this paper, the dynamics of the urea SCR system are represented by a control-oriented model which is based on a linear transfer function, with parameters dependent on engine operating conditions. The parameters are identified from input-output data generated by a high fidelity full chemistry model of the urea SCR system. The use of the full chemistry model facilitated the representation of the dynamics of stored ammonia (not a directly measurable parameter) as well as post SCR NOx and ammonia slip. A closed-loop Proportional-plus-Integral (PI) controller was first designed using the estimate of stored ammonia as a feedback signal.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

Increasingly complex hybrid electric vehicle (HEV) powertrains are being developed to address the growing stringency of emissions regulations, fuel economy standards and drivability/performance requirements. Early in their design process, it is desirable to rapidly evaluate powertrain architectures and components using simulation models before committing to costly physical prototyping. However, HEV powertrains have multiple controlled degrees of freedom, such as power split, engine on/off and gear selection, the operation of which needs to be pre-determined before a meaningful performance evaluation can be carried out. In this paper, we describe a multistage mixed integer trajectory optimization methodology that allows design engineers to rapidly perform performance analyses of complex powertrains. The methodology can generate optimal input signals for both continuous (engine torque or motor power) and discrete (engine on/off or gear selection) degrees of freedom for a given scenario.

The paper presents the estimator design procedures for automotive power train systems based on the adaptive Kalman filter. The Kalman filter adaptation is based on a simple and robust algorithm that detects sudden changes of power train variables. The adaptive Kalman filter has been used to estimate the SI engine load torque and air mass flow, and also the tire traction force and road condition. The presented experimental results indicate that proposed estimators are characterized by favorable response speeds and good noise suppression abilities.

In this paper, we compare controllers for the electronic throttle and variable geometry turbocharger in boosted stoichiometric gasoline direct injection engines. The control objectives are fast response and small overshoot of the intake manifold pressure. The problem is treated within the multi-objective optimization framework, applied to a simulation model of the engine. Pareto optimal fronts are constructed for each of the controllers and compared to each other. The best controller is thereby identified and further options to improve its response via preview-based control are discussed.

Gliders can climb to substantial altitudes without employing any on-board energy resources but using proper piloting skills to utilize rising air currents called thermals. Recent experiments on small Unmanned Aerial Vehicles (UAVs) indicate a significant potential to increase both the flight velocity and the range of gliders by means of such maneuvers. In these experiments the velocity to approach a thermal has been recognized as a critical performance factor, and is chosen as the controlled variable. Accurate longitudinal controllers are required to track the optimal flight trajectories generated using path planning algorithms. These controllers are challenged by the presence of uncertain and time-varying aircraft dynamics, gust disturbances, and control actuator limitations.

The efficiency of power transmission through a Van Doorne type Continuously Variable Transmission (CVT) can be improved by allowing a small amount of relative slip between the engine and driveline side pulleys. However, excessive slip must be avoided to prevent transmission wear and damage. To enable fuel economy improvements without compromising drivability, a CVT control system must ensure accurate tracking of the gear ratio set-point while satisfying pointwise-in-time constraints on the slip, enforcing limits on the pulley forces, and counteracting driveline side and engine side disturbances. In this paper, the CVT control problem is approached from the perspective of Model Predictive Control (MPC). To develop an MPC controller, a low order nonlinear model of the CVT is established. This model is linearized at a selected operating point, and the resulting linear model is extended with extra states to ensure zero steady-state error when tracking constant set-points.

Urea-selective catalytic reduction (SCR) after-treatment systems are used for reducing oxides of nitrogen (NOx) emissions in medium and heavy duty diesel vehicles. This paper addresses control-oriented modeling, starting from first-principles, of SCR after-treatment systems. Appropriate simplifications are made to yield governing equations of the Urea-SCR. The resulting nonlinear partial differential equations (PDEs) are discretized and linearized to yield a family of linear finite-dimensional state-space models of the SCR at different operating points. It is further shown that this family of models can be reduced to three operating regions. Within each region, parametric dependencies of the system on physical mechanisms are derived. Further model reduction is shown to be possible in each of the three regions resulting in a second-order linear model with sufficient accuracy.

This paper describes and experimentally compares various strategies to control the gas flows of a high speed automotive diesel engine equipped with external exhaust gas recirculation (EGR) valve and variable geometry turbocharger (VGT). The strategies differ in their choice of controlled variables and design approaches. Some of the strategies rely on standard measurements such as intake manifold pressure and compressor mass air flow. Others measure or estimate less readily available quantities such as exhaust manifold pressure and EGR flow fraction. The focus of this paper is on the experimental details of controller implementation. The control schemes are evaluated on their emissions and fuel economy over the extra urban part of the new European drive cycle. The implementation effort and chronometric load are also compared.

Model Predictive Control (MPC) design methods are becoming popular among automotive control researchers because they explicitly address an important challenge faced by today’s control designers: How does one realize the full performance potential of complex multi-input, multi-output automotive systems while satisfying critical output, state and actuator constraints? Nonlinear MPC (NMPC) offers the potential to further improve performance and streamline the development for those systems in which the dynamics are strongly nonlinear. These benefits are achieved in the MPC framework by using an on-line model of the controlled system to generate the control sequence that is the solution of a constrained optimization problem over a receding horizon.

The development of advanced model-based engine control strategies, such as economic model predictive control (eMPC) for diesel engine fuel economy and emission optimization, requires accurate and low-complexity models for controller design validation. This paper presents the NOx and smoke emissions modeling of a light duty diesel engine equipped with a variable geometry turbocharger (VGT) and a high pressure exhaust gas recirculation (EGR) system. Such emission models can be integrated with an existing air path model into a complete engine mean value model (MVM), which can predict engine behavior at different operating conditions for controller design and validation before physical engine tests. The NOx and smoke emission models adopt an artificial neural network (ANN) approach with Multi-Layer Perceptron (MLP) architectures. The networks are trained and validated using experimental data collected from engine bench tests.

Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone.

In the paper we employ numerical optimal control techniques to define the best transient operating strategy for a turbocharger power assist system (TPAS). A TPAS is any device capable of bi-directional energy transfer to the turbocharger shaft and energy storage. When applied to turbocharged diesel engines, the TPAS results in significant reduction of the turbo-lag. The optimum transient strategy is capable of improving the vehicle acceleration performance with no deterioration in smoke emissions. These benefits can be attained even if the net energy contribution by the TPAS during the acceleration interval is zero, i.e., all energy is re-generated and returned back to the energy storage by the end of the acceleration interval. At the same time the total fuel consumption during the acceleration interval may be reduced.

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

This paper presents heat release regressions for the combustion modes in a gasoline direct injected (GDI) engines. These heat release regressions can be used in one-dimensional flow codes and mean value models as a simplified representation of combustion. The heat release profiles are approximated as functions of crank angle, with some free parameters. These parameters are fitted as functions of engine operating conditions, so that a continuous family of curves is obtained. GDI engines have four distinct modes of combustion: homogeneous rich, homogeneous stoichiometric, homogeneous lean and stratified lean, each requiring its own functional approximation. We present the functional approximations, the dependence of the parameters on engine operating conditions, and the quality of the fit on data from a 1.8 liter production GDI engine.

The topic of energy management in Hybrid Electric Vehicles (HEVs) has received a great deal of recent attention. Various methods have been proposed to develop algorithms which manage energy flows within HEVs so that to optimally exploit energy storage capability of the battery and reduce fuel consumption while maintaining battery State of Charge. In addition, to the rule-based approaches, systematic optimal control methods based on deterministic and stochastic dynamic programming have been explored for HEV energy management optimization. In this paper, another novel framework based on the application of game theory is proposed, in which the HEV operation is viewed as a non-cooperative game between the driver and the powertrain.

Increasing electronics content, growing computing power, and proliferation of opportunities for information connectivity (through improved sensors, GPS, road and traffic information systems, wireless internet access, vehicle-to-vehicle communication, etc.) are technology trends which can significantly transform and impact future automotive vehicle's control and diagnostic strategies. One aspect of the increasing vehicle connectivity is access to ambient and road condition information, such as ambient temperature, ambient pressure, humidity, % cloudiness, visibility, cloud ceiling, precipitation, rain droplet size, wind speed, and wind direction based on wireless internet access. The paper discusses the potential opportunities made available through wireless communication between the vehicle and the internet.