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The use of Hybrid Predictive Control for model-based propulsion control applications is considered. A Variable Displacement Engine (VDE) control problem is considered, involving both continuous-time dynamics and discrete control actions in the form of activating/deactivating the engine cylinders. Hybrid Model Predictive Control is one of the most successful hybrid control schemes and builds upon predictive control methods developed for engine torque management. The ways in which preview information can be used to improve controller performance are considered, as well as simplifications to the hybrid control algorithms to reduce the computational burden. Several hybrid control design approaches are compared using a simulation of a VDE engine. The aim is to optimize the total system behaviour to provide good torque tracking, reduced fuel consumption and smooth cylinder switching. The main contribution is the demonstration that hybrid predictive control can provide a practical solution to an engine control application with the potential to enhance performance and with options to reduce complexity.

With advances in sensing, computing and communication technologies, Connected and Automated Vehicles (CAVs) are becoming feasible. The advent of CAVs presents new opportunities to improve the energy efficiency of individual vehicles. However, testing and verifying energy-efficient autonomous driving systems are difficult due to safety considerations and repeatability. In this paper, we present a co-simulation platform to develop and test novel vehicle eco-autonomous driving technologies named InfoRich, which incorporates the information from on-board sensors, V2X communications, and map database. The co-simulation platform includes eco-autonomous driving software, vehicle dynamics and powertrain (VD&PT) model, and a traffic environment simulator. Also, we utilize synthetic drive cycles derived from real-world driving data to test the strategies under realistic driving scenarios. To build road networks from the real-world driving data, we develop an Automated Parser and Calculator for Map/Scenario named AutoPASCAL. Overall, the simulation platform provides a realistic vehicle model, powertrain model, sensor model, traffic model, and road-network model to enable the evaluation of the energy efficiency of eco-autonomous driving.

This paper presents a control-oriented, reaction-based diesel combustion model that predicts the time-based rate of combustion, in-cylinder gas temperature, and pressure over one engine cycle. The model, based on the assumption of a homogeneous thermodynamic combustion process, uses a two-step chemical reaction mechanism that consists of six species: diesel fuel (C10.8H18.7), oxygen (O2), carbon dioxide (CO2), water (H2O), nitrogen (N2), and carbon monoxide (CO). The temperature variation rate is calculated based on the rate of change of species concentrations; the heat loss correlation is also used to study the model performance. The accuracy of the model is evaluated using test data from a GM 6.6 L, eight-cylinder Duramax engine. The main contribution is the model calibration under different key operational conditions over a large engine speed and load range as well as different injection timings and exhaust gas recirculation rates by solving the optimal calibration problem. The calibrated reaction-based model accurately predicts the indicated mean effective pressure, while keeping the errors of in-cylinder pressure and temperature small, and, at the same time, significantly reduces the calibration effort, especially when the engine is operated under multiple fuel injection operations compared with Wiebe-based combustion models. The calibrated model parameters have a strong correlation to engine speed, load, and injection timings, and, as a result, a universal parameter calibration structure is proposed for entire operational conditions.

Among all advanced energy improvement technologies sought after to reduce the energy footprint, connected and automated vehicle (CAV) technologies hold a special promise of simultaneously improving energy efficiency, safety, and mobility. This work explored the significant opportunities exist in reducing the overall vehicle energy consumption through optimizing the vehicle dynamics and powertrain controls in a coordinated fashion utilizing preview information. A practical solution of Eco-Approach, -Departure, and-Cruise were proposed in this paper. These eco-applications were then integrated with autonomous driving modules. The integrated driver model can perform those eco-applications while navigating through a comprehensive traffic scenario designed on a high-fidelity virtual simulator developed for this project. Overall performance was compared with that of the baseline autonomous driver model to show the fuel economy improvement.

This paper aims at developing integrated onboard diagnosis and fault-tolerant control methods with experimental validation for a urea selective catalyst reduction (SCR) aftertreatment system to reduce vehicle tailpipe emissions. Diagnostics are performed for an SCR urea injection system by estimating and monitoring the injected urea mass flow with no need for a costly physical flow sensor. The estimation is derived from a first-principle-based urea injection system model, and the model parameters are identified by using system identification. During vehicle transient maneuvers, a Kalman filter (KF) is formulated to further reduce the estimation noise and improve diagnostic robustness. Once an injection fault is detected, an adaptation control algorithm is applied to compensate the urea injection command, thus correcting certain types of urea under/overdosing faults and maintaining the SCR $\mbox{NO}_{x}$ conversion performance. These methods have been validated through vehicle tests by utilizing an onboard rapid prototyping control system.

With the rapid growth in the amount of computations that need to be performed by modern electronic control units and in the complexity of the algorithms, there is a pressing need to develop approaches to reduce chronometric loading in automotive vehicles. This paper presents a cyberphysical systems framework for the development of stochastic optimal decision policies that trigger the computations online. For a specific case study, we consider online triggering of the computations involved in obtaining a linearized model in a setting when such a linearized model is required by control or estimation algorithms. The objective is to define a policy for triggering the linearization that balances the average model accuracy (or expected closed loop performance) with the average computational cost. The problem is formulated as a stochastic optimal control problem and solved using stochastic dynamic programming (SDP). The approach is described, then illustrated with three examples, a pendulum, a turbocharged diesel engine, and a turbocharged spark ignition engine, that illustrate the trade-off between the computational cost and expected linearized model accuracy.

A control-oriented reaction-based combustion model is implemented and used to simulate the combustion process in a diesel engine. The model integrates a homogeneous thermodynamic system with a two-step chemical reaction mechanism that consists of six species. The accuracy of the model is evaluated by comparing with experimental data from a GM 6.6L, 8 cylinder Duramax engine. The model is calibrated for different key points over the entire engine map as well as various injection timings and exhaust gas recirculation (EGR) ratio using an automated calibration algorithm. The reaction-based model is shown to provide accurate predictions of incylinder pressure, temperature, mass-fraction-burned and heat release rate. As an alternative to Wiebe-based method, this approach could lead to a better model with less calibration effort. The improvement is due to the fact that the burn rate is online calculated based upon the dominated fuel chemical components and combustion chamber properties, such as temperature, oxygen and burned gas concentration, etc.

The role that supervisory control or upper-level multi-objective optimization can play in total engine control is considered. This involved the control of an SI engine for a combination of air-fuel ratio, torque, fuel consumption and residual gas fraction. The upper level optimization algorithm, based on Model Predictive Control principles, makes use of a ‘reference model.’ This is constructed as a closed-loop interconnection of the engine model and the lower-level classical torque/air charge and lambda controller. This model is derived in a linear parameter varying form, which facilitates the application of the predictive control law. The main contribution is the proposed hierarchical control structure, which has been assessed in simulation and on a test track, and provides a useful separation in tasks and functionality. The solution is also compared briefly with MPC at both upper and lower levels, using different sample rates.