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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.

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 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.

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.

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.

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.

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.

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.