Edward Winward

An Explicit Model Predictive Control Framework for Turbocharged Diesel Engines

The turbocharged diesel engine is a typical multi-input multioutput system with strong couplings, actuator constraints, and fast dynamics. This paper addresses the exhaust emission regulation in turbocharged diesel engines using an explicit model predictive control (EMPC) approach, which allows tracking of the time-varying setpoint values generated by the supervisory level controller while satisfying the actuator constraints. The proposed EMPC framework consists of calibration, engine model identification, controller formulation, and state observer design. The proposed EMPC approach has a low computation requirement and is suitable for implementation in the engine control unit on board. The experimental results on a turbocharged Cat C6.6 diesel engine demonstrate that the EMPC controller significantly improves the tracking performance of the exhaust emission variables in comparison with the decoupled single-input single-output control methods.

Real-Time Energy Management for Diesel Heavy Duty Hybrid Electric Vehicles

In this paper, a fuzzy-tuned equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for a conceptual diesel engine-equipped heavy duty hybrid electric vehicle (HEV). In the HEV, two electric motors/generators are mounted on the turbocharger shaft and engine shaft, respectively, which can improve fuel efficiency by capturing and storing energy from both regenerative braking and otherwise wasted engine exhaust gas. The heavy duty HEV frequently involved in duty cycles characterized by start-stop events, especially in off-road applications, whose dynamics is analyzed in this paper. The on-line optimization problem is formulated as minimizing a cost function in terms of weighted fuel power and electric power. In the cost function, a cost factor is defined for both improving energy transmission efficiency and maintaining the battery energy balance. To deal with the nonexplicit relationship between HEV fuel economy, battery state of charge (SOC), and control variables, the cost factor is fuzzy tuned using expert knowledge and experience. In relation to the fuel economy, the air-fuel ratio is an important factor. An online search for capable optimal variable geometry turbocharger (VGT) vane opening and exhaust gas recirculation (EGR) valve opening is also necessary. Considering the exhaust emissions regulation in diesel engine control, the boundary values of VGT and EGR actuators are identified by offline design-of-experiment tests. An online rolling method is used to implement the multivariable optimization. The proposed method is validated via simulation under two transient driving cycles, with the fuel economy benefits of 4.43% and 6.44% over the nonhybrid mode, respectively. Compared with the telemetry equivalent consumption minimization strategy, the proposed F-ECMS shows better performance in the sustainability of battery SOC under driving conditions with the rapid dynamics often associated with off-road applications.

Explicit model predictive control on the air path of turbocharged diesel engines

The turbocharged diesel engine is a typical multi-input multi-output (MIMO) system with strong couplings, actuator constraints, and fast dynamics. This paper addresses the air path regulation in turbocharged diesel engines using an explicit model predictive control (EMPC) approach, which allows tracking of the time-varying setpoint values generated by the supervisory level controller while satisfying the actuator constraints. The proposed EMPC framework consists of calibration, engine model identification, controller formulation, and state observer design. The proposed EMPC approach has a low computation requirement and is suitable for implementation in the engine control unit (ECU) on board. The experimental results on a turbocharged Cat® C6.6 diesel engine illustrate that the EMPC controller significantly improves the tracking performance of the exhaust emission variables against the decentralized single-input single-output (SISO) control method.

Improved decision support for engine-in-the-loop experimental design optimization

Abstract Experimental optimization with hardware in the loop is a common procedure in engineering and has been the subject of intense development, particularly when it is applied to relatively complex combinatorial systems that are not completely understood, or where accurate modelling is not possible owing to the dimensions of the search space. A common source of difficulty arises because of the level of noise associated with experimental measurements, a combination of limited instrument precision, and extraneous factors. When a series of experiments is conducted to search for a combination of input parameters that results in a minimum or maximum response, under the imposition of noise, the underlying shape of the function being optimized can become very difficult to discern or even lost. A common methodology to support experimental search for optimal or suboptimal values is to use one of the many gradient descent methods. However, even sophisticated and proven methodologies, such as simulated annealing, can be significantly challenged in the presence of noise, since approximating the gradient at any point becomes highly unreliable. Often, experiments are accepted as a result of random noise which should be rejected, and vice versa. This is also true for other sampling techniques, including tabu and evolutionary algorithms. After the general introduction, this paper is divided into two main sections (sections 2 and 3), which are followed by the conclusion. Section 2 introduces a decision support methodology based upon response surfaces, which supplements experimental management based on a variable neighbourhood search and is shown to be highly effective in directing experiments in the presence of a significant signal-to-noise ratio and complex combinatorial functions. The methodology is developed on a three-dimensional surface with multiple local minima, a large basin of attraction, and a high signal-to-noise ratio. In section 2, the methodology is applied to an automotive combinatorial search in the laboratory, on a real-time engine-in-the-loop application. In this application, it is desired to find the maximum power output of an experimental single-cylinder spark ignition engine operating under a quasi-constant-volume operating regime. Under this regime, the piston is slowed at top dead centre to achieve combustion in close to constant volume conditions. As part of the further development of the engine to incorporate a linear generator to investigate free-piston operation, it is necessary to perform a series of experiments with combinatorial parameters. The objective is to identify the maximum power point in the least number of experiments in order to minimize costs. This test programme provides peak power data in order to achieve optimal electrical machine design. The decision support methodology is combined with standard optimization and search methods — namely gradient descent and simulated annealing— in order to study the reductions possible in experimental iterations. It is shown that the decision support methodology significantly reduces the number of experiments necessary to find the maximum power solution and thus offers a potentially significant cost saving to hardware-in-the-loop experimentation.

Quasi-constant volume (QCV) spark ignition combustion

The Otto cycle delivers theoretical maximum thermal efficiency. The traditional design of internal combustion engines using a simple slide-crank mechanism gives no time for a constant volume combustion which significantly reduces the cycle efficiency. In this study, using a high torque, high bandwidth, permanent magnet electric drive system attached to the crankshaft, variable angular velocities of the engine crankshaft were implemented. The system enabled reductions in piston velocity around the top dead centre region to a fraction of its value at constant crankshaft angular velocity typical in conventional engines. A quasi-constant volume combustion has thus been successfully achieved, leading to improvements in engine fuel consumption and power output which are discussed in detail.

Decoupling control of electrified turbocharged diesel engines

Engine electrification is a critical technology in the promotion of engine fuel efficiency, among which the electrified turbocharger is regarded as a promising solution for its advantages in engine downsizing and exhaust gas energy recovery. By installing electrical devices on the turbocharger, the excess energy can be captured, stored, and re-used. The control of the energy flows in an electrified turbocharged diesel engine (ETDE) is still in its infancy. Developing a promising multi-input multi-output (MIMO) control strategy is essential in exploring the maximum benefits of electrified turbocharger. In this paper, the dynamics in an ETDE, especially the couplings among multiple loops in the air path are analyzed. Based on the analysis, a model-based MIMO decoupling control framework is designed to regulate the air path dynamics. The proposed control strategy can achieve fast and accurate tracking on selected control variables and is successfully validated on a physical model in simulations.

Modelling the Exhaust Gas Recirculation Mass Flow Rate in Modern Diesel Engines

The intrinsic model accuracy limit of a commonly used Exhaust Gas Recirculation (EGR) mass flow rate model in diesel engine air path control is discussed in this paper. This EGR mass flow rate model is based on the flow of a compressible ideal gas with unchanged specific heat ratio through a restriction cross-area within a duct. A practical identification procedure of the model parameters is proposed based on the analysis of the engine data and model structure. This procedure has several advantages which include simplicity, low computation burden and low engine test cost. It is shown that model tuning requires only an EGR valve sweep test at a few engine steady state operating points. It is also shown that good model accuracy can be achieved when the control variables of other air path devices, e.g. The vane position of a Variable Geometry Turbocharger (VGT) and the torque demand of an Electric Turbo Assist (ETA), are kept constant during the EGR valve sweep test used to tune the model. Two different diesel engines are used in this work to demonstrate the model tuning procedure and the model validation results. Both engines are equipped with a high pressure external EGR system and a VGT. One of the engines has a relatively new air system device - an ETA. The model validation results of both engines show good model accuracy not only at steady state engine operating points but also during engine transients.

Control-oriented dynamics analysis for electrified turbocharged diesel engines

Engine electrification is a critical technology in the promotion of engine fuel efficiency, among which the electrified turbocharger is regarded as the promising solution in engine downsizing. By installing electrical devices on the turbocharger, the excess energy can be captured, stored, and re-used. The electrified turbocharger consists of a variable geometry turbocharger (VGT) and an electric motor (EM) within the turbocharger bearing housing, where the EM is capable in bi-directional power transfer. The VGT, EM, and exhaust gas recirculation (EGR) valve all impact the dynamics of air path. In this paper, the dynamics in an electrified turbocharged diesel engine (ETDE), especially the couplings between different loops in the air path is analyzed. Furthermore, an explicit principle in selecting control variables is proposed. Based on the analysis, a model-based multi-input multi-output (MIMO) decoupling controller is designed to regulate the air path dynamics. The dynamics analysis and controller are successfully validated through experiments and simulations.

Robust control of electrified turbocharged diesel engines

Electrified turbocharger is a critical technology for engine downsizing and is a cost-effective solution for exhaust gas energy recovery. In conventional turbocharged diesel engines, the air path holds strong nonlinearity since the actuators are all driven by the exhaust gas. In an electrified turbocharged diesel engine (ETDE), the coupling is more complex, due to the electric machine mounted on the turbine shaft impacts the exhaust manifold dynamics as well. In distributed single-input single-output control methods, the gains tuning is time consuming and the couplings are ignored. To control the performance variables independently, developing a promising multi-input multi-output control method for the ETDE is essential. In this paper, a model-based multi variable robust controller is designed to control the performance variables in a systematic way. Both simulation and experimental results verified the effectiveness of the proposed controller.

An Integrated Framework on Characterization, Control, and Testing of an Electrical Turbocharger Assist

Electrical turbocharger assist is one of the most critical technologies in improving fuel efficiency of conventional powertrain vehicles. However, strong challenges lie in high efficient operations of the device due to its complexity. In this paper, an integrated framework on characterization, control, and testing of the electrical turbocharger assist is proposed. Starting from a physical characterization of the engine, the controllability and the impact of the electrical turbocharger assist on fuel economy and exhaust emissions are both analyzed. A multivariable robust controller is designed to regulate the dynamics of the electrified turbocharged engine in a systematic approach. To minimize the fuel consumption in real time, a supervisory level controller is designed to update the setpoints of key controlled variables in an optimal way. Furthermore, a cutting-edge experimental platform based on a heavy-duty diesel engine is built. The proposed framework has been evaluated in simulations, physical simulations, and experiments. Results are presented for the developed system and the proposed framework that demonstrate excellent tracking performance, high robustness, and the potential for improvements in fuel efficiency.