Dezong Zhao

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.

Systematic control on Energy recovery of electrified turbocharged diesel engines

Recovering energy from exhaust gas is seen as the promising solution to save fuel consumption of diesel engines, where the key issue in maximizing fuel economy benefits is the management of energy flows in the optimal way. This paper proposes a systematic control strategy on both energy management and air path regulation of an electrified turbocharged diesel engine (ETDE). The Energy management and air path regulation is formulated as a multi-variable online optimization problem with constraints. The equivalent consumption minimization strategy (ECMS) is employed as the supervisory level controller, to calculate the optimal energy flow distribution. An explicit model predictive controller (EMPC) is developed as the low level controller to implement the optimal energy flow distribution. The two controllers work together as cascaded modules in real-time, while simulation results based on a physical model show the superior performance over the conventional distributed single-input single-output (SISO) control method.

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.

Integrated Feedback Scheduling and Control Codesign for Motion Coordination of Networked Induction Motor Systems

This paper investigates the codesign of remote speed control and network scheduling for motion coordination of multiple induction motors through a shared communication network. An integrated feedback scheduling algorithm is designed to allocate the optimal sampling period and priority to each control loop to optimize the global performance of a networked control system (NCS), while satisfying the constraints of stability and schedulability. A speed synchronization method is incorporated into the scheduling algorithm to improve the speed synchronization performance of multiple induction motors. The rational gain of the network speed controllers is calculated using the Lyapunov theorem and tuned online by fuzzy logic to guarantee the robustness against complicated variations on the communication network. Furthermore, a state predictor is designed to compensate the time delay which occurred in data transmission from the sensor to the controller, as a part of the networked controller. Simulation results support the effectiveness of the proposed control-and-scheduling codesign approach.

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.

Real-Time Optimal Energy Management of Heavy Duty Hybrid Electric Vehicles

The performance of energy flow management strategies is essential for the success of hybrid electric vehicles (HEVs), which are considered amongst the most promising solutions for improving fuel economy as well as reducing exhaust emissions. The heavy duty HEVs engaged in cycles characterized by start-stop configuration has attracted widely interests, especially in off-road applications. In this paper, a fuzzy equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for heavy duty HEVs. The online optimization problem is formulated as minimizing a cost function, in terms of weighted fuel power and electrical power. A fuzzy rule-based approach is applied on the weight tuning within the cost function, with respect to the variations of the battery state-of-charge (SOC) and elapsed time. Comparing with traditional real-time supervisory control strategies, the proposed F-ECMS is more robust to the test environments with rapid dynamics. The proposed method is validated via simulation under two transient test cycles, with the fuel economy benefits of 4.43% and 6.44%, respectively. The F-ECMS shows better performance than the telemetry ECMS (T-ECMS), in terms of the sustainability of battery SOC.

The Potential of Thermoelectric Generator in Parallel Hybrid Vehicle Applications

This paper reports on an investigation into the potential for a thermoelectric generator (TEG) to improve the fuel economy of a mild hybrid vehicle. A simulation model of a parallel hybrid vehicle equipped with a TEG in the exhaust system is presented. This model is made up by three sub-models: a parallel hybrid vehicle model, an exhaust model and a TEG model. The model is based on a quasi-static approach, which runs a fast and simple estimation of the fuel consumption and CO2 emissions. The model is validated against both experimental and published data. Using this model, the annual fuel saving, CO2 reduction and net present value (NPV) of the TEG’s life time fuel saving are all investigated. The model is also used as a flexible tool for analysis of the sensitivity of vehicle fuel consumption to the TEG design parameters. The analysis results give an effective basis for optimization of the TEG design.