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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.

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.

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.

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.

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.

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.

An engine torque modulation methodology is developed for use in engines equipped with electronic throttle control, ETC. It is shown through simulation that an engine with ETC is capable of following a transmission torque modulation command that includes a linearly increasing torque during the torque phase, a torque reduction during the inertia phase and a post-shift torque increase. It is also shown that such a strategy can be used to minimize the transmission output torque variation during a shift. The impact of engine indicated mean effective pressure, IMEP, variation on vehicle longitudinal acceleration is analyzed using Monte-Carlo simulation. It is shown that the IMEP induced variation in the vehicle acceleration is largely due to end of shift engine torque output timing errors. It is then recommended that during a shift the engine IMEP variation be held to a one-sigma variation of three percent or less.

Direct injection stratified charge gasoline engines are becoming increasingly popular due to their potential for improved fuel economy and emissions. However, the benefits are restricted to low speed and load conditions due to the large air requirements during stratified operation. With boost, the air flow can be increased, extending the stratified operating regime and potentially the fuel economy and emissions benefits as well. This study assesses the feasibility of this technology using a variable geometry turbocharger and a supercharger as boost devices. The effect of boost on fuel economy, delivery of recirculated exhaust gas, and exhaust gas temperature are considered.

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.

Dynamic simulation models of turbocharged Diesel and gasoline engines are increasingly being used for design and initial testing of engine control strategies. The turbocharger submodel is a critical part of the overall model, but its control-oriented modeling has received limited attention thus far. Turbocharger performance maps are typically supplied in table form, however, for inclusion into engine simulation models this form is not well suited. Standard table interpolation routines are not continuously differentiable, extrapolation is unreliable and the table representation is not compact. This paper presents an overview of curve fitting methods for compressor and turbine characteristics overcoming these problems. We include some background on compressor and turbine modeling, limitations to experimental mapping of turbochargers, as well as the implications of the compressor model choice on the overall engine model stiffness and simulation times.

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.