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Vehicle emissions and fuel economy in real-world driving conditions are currently under considerable scrutiny. Key to achieving optimum performance for a given hardware set and control scheme is a calibration that optimizes controller gains such that inputs are scheduled over the operating space to minimize emissions and maximize fuel economy. Generating a suitable calibration requires data that is both precise and accurate, this data is used to generate models that are deployed as part of the calibration optimization process. This paper evaluates the repeatability of typical steady-state measurements used for calibration of engine controllers that will ultimately determine vehicle level emissions for homologation include Real Driving Emissions (RDE). Stabilization requirements as indicated by three different measurements are evaluated and shown to be different within the same experiment, depending on the metric used.

This paper presents a novel approach to augment existing engine calibrations to deliver improved engine performance during a transient, through the application of multi-objective optimization techniques to the calibration of the Variable Valve Timing (VVT) system of a 1.0 litre gasoline engine. Current mature calibration approaches for the VVT system are predominantly based on steady state techniques which fail to consider the engine dynamic behaviour in real world driving, which is heavily transient. In this study the total integrated fuel consumption and engine-out NOx emissions over a 2-minute segment of the transient Worldwide Light-duty Test Cycle are minimised in a constrained multi-objective optimisation framework to achieve an updated calibration for the VVT control. The cycle segment was identified as an area with high NOx emissions.

Thermoelectric generator has very quickly become a hot research topic in the last five years because its broad application area and very attractive features such as no moving parts, low maintenance, variety of thermoelectric materials that total together cover a wide temperature range. The biggest disadvantage of the thermoelectric generator is its low conversion efficiency. So that when design and manufacture a thermoelectric generator for exhaust waste heat recovery from an automotive engine, the benefit of fuel consumption from applying a thermoelectric generator would be very sensitive to the weight, the dimensions, the cost and the practical conversion efficiency. Additionally, the exhaust gas conditions vary with the change of engine operating point. This creates a big challenge for the design of the hot side heat exchanger in terms of optimizing the electrical output of the thermoelectric generator during an engine transient cycle.

The application of state-of-art thermoelectric generator (TEG) in automotive engine has potential to reduce more than 2% fuel consumption and hence the CO2 emissions. This figure is expected to be increased to 5%~10% in the near future when new thermoelectric material with higher properties is fabricated. However, in order to maximize the TEG output power, there are a few issues need to be considered in the design stage such as the number of modules, the connection of modules, the geometry of the thermoelectric module, the DC-DC converter circuit, the geometry of the heat exchanger especially the hot side heat exchanger etc. These issues can only be investigated via a proper TEG model. The authors introduced four ways of TEG modelling which in the increasing complexity order are MATLB function based model, MATLAB Simscape based Simulink model, GT-power TEG model and CFD STAR-CCM+ model. Both Simscape model and GT-Power model have intrinsic dynamic model performance.

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.

Two identical commercial Thermo-Electric Modules (TEMs) were assembled on a plate type heat exchanger to form a Thermoelectric Generator (TEG) unit in this study. This unit was tested on the Exhaust Gas Recirculation (EGR) flow path of a test engine. The data collected from the test was used to develop and validate a steady state, zero dimensional numerical model of the TEG. Using this model and the EGR path flow conditions from a 30% torque Non-Road Transient Cycle (NRTC) engine test, an optimization of the number of TEM units in this TEG device was conducted. The reduction in fuel consumption during the transient test cycle was estimated based on the engine instantaneous Brake Specific Fuel Consumption (BSFC). The perfect conversion of TEG recovered electrical energy to engine shaft mechanical energy was assumed. Simulations were performed for a single TEG unit (i.e. 2 TEMs) to up to 50 TEG units (i.e. 100 TEMs).

An automotive engine can be more efficient if thermoelectric generators (TEG) are used to convert a portion of the exhaust gas enthalpy into electricity. Due to the relatively low cost of the incoming thermal energy, the efficiency of the TEG is not an overriding consideration. Instead, the maximum power output (MPO) is the first priority. The MPO of the TEG is closely related to not only the thermoelectric materials properties, but also the operating conditions. This study shows the development of a numerical TEG model integrated with a plate-fin heat exchanger, which is designed for automotive waste heat recovery (WHR) in the exhaust gas recirculation (EGR) path in a diesel engine. This model takes into account the following factors: the exhaust gas properties’ variation along the flow direction, temperature influence on the thermoelectric materials, thermal contact effect, and heat transfer leakage effect. Its accuracy has been checked using engine test data.

A full calibration exercise of a diesel engine air path can take months to complete (depending on the number of variables). Model-based calibration approach can speed up the calibration process significantly. This paper discusses the overall calibration process of the air-path of the Cat® C7.1 engine using statistical machine learning tool. The standard Cat® C7.1 engine's twin-stage turbocharger was replaced by a VTG (Variable Turbine Geometry) as part of an evaluation of a novel air system. The changes made to the air-path system required a recalculation of the air path's boost set point and desired EGR set point maps. Statistical learning processes provided a firm basis to model and optimize the air path set point maps and allowed a healthy balance to be struck between the resources required for the exercise and the resulting data quality.

For diesel engines, fuel path control plays a key role in achieving optimal emissions and fuel economy performance. There are several fuel path parameters that strongly affect the engine performance by changing the combustion process, by modifying for example, start of injection and fuel rail pressure. This is a multi-input multi-output problem. Linear Model Predictive Control (MPC) is a good approach for such a system with optimal solution. However, fuel path has fast dynamics. On-line optimisation MPC is not the good choice to cope with such fast dynamics. Explicit MPC uses off-line optimisation, therefore, it can be used to control the system with fast dynamics.