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. Index Terms-Electrical turbocharger assist (ETA), multivariable control, real-time energy management, system characterization, testing framework design. NOMENCLATURE GHG Greenhouse gas. EM Electrical machine. ETA Electrical turbocharger assist. TDE Turbocharged diesel engine. ETDE Electrified turbocharged diesel engine.

This article discusses a body of ongoing work that seeks to reduce the time and effort required to create, reconfigure, and parameterize physically based models that are to be used as part of a model-based design ('MBD') process.Initially a new modular approach for system model creation is presented. The method facilitates rapid creation of simulation models to be used within an MBD process making use of applicationspecific submodels. It is shown that through the use of the submodels created using Simulink configurable subsystems, the number of degrees of freedom and thus fidelity of a model may be altered by a simple parameter change.The work continues, beyond the introduction of a number of plug-and-simulate drivetrain submodels, to investigate a means by which a model's parameters may be reduced to an absolute minimum. An algorithm capable of identifying parameters of potentially low significance to an output of interest is shown in the form of the parameter elimination and model evaluation algorithm ('PEMEA'). Simulation results that make use of the PEMEA suggest that a number of parameters may be eliminated. The parameter eliminations, conducted in situ in Simulink models, are shown to have minimum effect on the model outputs-of-interest and marginally decrease the computational overhead of the model.

High Cycle Fatigue (HCF) of turbine blades is a major cause of failure in turbochargers. In order to validate changes to blades intended to reduce fatigue failure, accurate measurement of blade dynamics is necessary. Strain gauging has limitations, so an alternative method is investigated.

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The electric turbocharger is a promising solution for engine downsizing. It provides great potential for vehicle fuel efficiency improvement. The electric turbocharger makes engines run as hybrid systems so critical challenges are raised in energy management and control. This paper proposes a real-time energy management strategy based on updating and tracking of the optimal exhaust pressure setpoint. Starting from the engine characterisation, the impacts of the electric turbocharger on engine response and exhaust emissions are analysed. A multivariable explicit model predictive controller is designed to regulate the key variables in the engine air system, while the optimal setpoints of those variables are generated by a high level controller. The two-level controller works in a highly efficient way to fulfill the optimal energy management. This strategy has been validated in physical simulations and experimental testing. Excellent tracking performance and sustainable energy management demonstrate the effectiveness of the proposed method. Index Terms-Electric turbocharger, real-time energy management, explicit model predictive controlDezong Zhao (M'12-SM'17) received the B.S. and M.S. degrees in control science and engineering from the School