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Trajectory and velocity tracking are currently one of the core issues in autonomous vehicle control. However, most studies deal with them separately which may cause vehicle instability under extreme conditions. In this paper, a coupled longitudinal and lateral control strategy of trajectory tracking for autonomous vehicles is presented. A lateral controller is implemented with a Linear Time-Varying MPC (LTV-MPC) to generate the front steering angle required for trajectory tracking. The side-slip angle is constrained within an interval to prevent tire saturation. Furthermore, a velocity regulation module in which the reference velocity is calculated considering the curvature of the trajectory and the lateral stability criteria is designed. A longitudinal controller is proposed to provide the traction torque with the obtained reference velocity to cope with the longitudinal velocity tracking problem.

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.

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.

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.

In order to identify predictive models for a diesel engine combustion process, combustion cylinder pressure together with other fuel path variables such as rail pressure, injector current and sleeve pressure of 1000 continuous cycles were sampled and collected at high resolution. Using these engine steady state test data, three types of modeling approach have been studied. The first is the Auto-Regressive-Moving-Average (ARMA) model which had limited prediction ability for both peak combustion pressure (Pmax) and Indicated Mean Effective Pressure (IMEP). By applying correlation analysis, proper inputs were found for a linear predictive model of Pmax and IMEP respectively. The prediction performance of this linear model is excellent with a 30% fit number for both Pmax and IMEP. Further nonlinear modeling work shows that even a nonlinear Neural Network (NN) model does not have improved prediction performance compared to the linear predictive model.

Most modern diesel engines are equipped with common fuel rail system. The common fuel rail pressure and start of injection are two important fuel path control variables which are needed to be carefully calibrated over all engine operation range. They both have big effects on engine emissions, fuel consumptions and combustion noise performance. Though there are mature techniques such as design of experiment, model based calibration together with optimization method for engine calibration task, the engine test points are still many and the calibration costs are still high. Besides, the outputs of the calibration are look up tables or maps which are used in engine open loop control strategy in engine control system. Open loop control system has no adaptive and disturbance rejection ability. So the initially optimally calibrated look up control tables will gradually become less and less optimal when the engine is aging.

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.

Extensive research has been performed for on-board hydrogen generation, such as pyrolysis of metal hydrides (e.g., LiH, MgH₂), hydrogen storages in adsorption materials (e.g., carbon nanotubes and graphites), compressed hydrogen tanks and the hydrolysis of chemical hydrides. Among these methods, the hydrolysis of NaBH₄ has attracted great attention due to the high stability of its alkaline solution and the relatively high energy density, with further advantages such as moderate temperature range (from -5°C to 100°C) requirement, non-flammable, no side reactions or other volatile products, high purity H₂ output. The H₂ energy density contained by the system is fully depend on the solubility of the complicated solution contains reactant, product and the solution stabilizer. In this work, an approach based on thermodynamic equilibrium was proposed to model the relationship between the solubility of an electrolyte and temperature, and the effect of another component on its solubility.

One of the most critical challenges currently facing the diesel engine industry is how to improve fuel economy under emission regulations. Improvement in fuel economy can be achieved by precisely controlling Air/Fuel ratio and by monitoring fuel consumption in real time. Accurate and repeatable measurements of fuel rate play a critical role in successfully controlling air/fuel ratio and in monitoring fuel consumption. Volumetric and gravimetric measurements are well-known methods for measuring fuel consumption of internal combustion engines. However, these methods are not suitable for obtaining fuel flow rate data used in real-time control/measurement. In this paper, neural networks are used to solve the problem concerning discontinuous data of fuel flow rate measured by using an AVL 733 s fuel meter. The continuous parts of discontinuous fuel flow rate are used to train and validate a neural network, which can then be used to predict the discontinuous parts of the fuel flow rate.

New European emissions legislation (Euro5) specifies a limit for Particle Number (PN) emissions and therefore drives measurement of PN during vehicle development and homologation. Concurrently, the use of biofuel is increasing in the marketplace, and Euro5 specifies that reference fuel must contain a bio-derived portion. Work was carried out to test the effect of fuels containing different levels of Fatty Acid Methyl Ester (FAME) on particle number, size, mass and composition. Measurements were conducted with a Cambustion Differential Mobility Spectrometer (DMS) to time-resolve sub-micron particles (5-1000nm), and a Horiba Solid Particle Counting System (SPCS) providing PN data from a Euro5-compliant measurement system. To ensure the findings are relevant to the modern automotive business, testing was carried out on a Euro4 compliant passenger car fitted with a high-pressure common-rail diesel engine and using standard homologation procedures.

The recent development of diesel engine fuel injection systems has been dominated by how to manage the degrees of freedom that common rail multi-pulse systems now offer. A number of production engines already use four injection events while in research, work based on up to eight injection events has been reported. It is the degrees of freedom that lead to a novel experimental requirements. There is a potentially complex experimental program needed to simply understand how injection parameters influence the combustion process in steady state. Combustion behavior is not a continuum and as both injection and EGR rates are adjusted, distinct combustion modes emerge. Conventional calibration processes are severely challenged in the face of large number of degrees of freedom and as a consequence new development approaches are needed.

In modern production diesel engine control systems, fuel path control is still largely conducted through a system of tables that set mode, timing and injection quantity and with common rail systems, rail pressure. In the hands of an experienced team, such systems have proved so far able to meet emissions standards, but they lack the analytical underpinning that lead to systematic solutions. In high degree of freedom systems typified by modern fuel injection, there is substantial scope to deploy optimising closed loop strategies during calibration and potentially in the delivered product. In an optimising controller, a digital algorithm will explicitly trade-off conflicting objectives and follow trajectories during transients that continue to meet a defined set of criteria. Such an optimising controller must be based on a model of the system behaviour which is used in real time to investigate the consequences of proposed control actions.

Over the past few decades, Homogeneous Charge Compression Ignition (HCCI) or Controlled Auto-Ignition (CAI) if it is fuelled with gasoline type of fuels has shown its potential to overcome the limitations and environmental issue concerns of the Spark Ignition (SI) and Compression Ignition (CI) engines. However, controlling the ignition timing of a CAI engine over a wide range of speeds and loads is challenging. Combustion in CAI is affected by a number of factors; the local temperature, the local composition of the air/fuel mixture, time and to a lesser degree the pressure. The in-cylinder engine charge flow fields have significant influences on these factors, especially the local gas properties, which leads to the influences towards the CAI combustion. In this study, such influences were investigated using a Computational Fluid Dynamics (CFD) engine simulation package fitted with a real optical research engine geometry.

The Homogeneous Charge Compression Ignition (HCCI) engine combustion uses heat energy from trapped exhaust gases enhanced by the piston compression heating to auto ignite a premixed air/gasoline mixture. As the HCCI combustion is controlled by the charge temperature, composition and pressure, it therefore, prevents the use of a direct control mechanism such as in the spark and diesel combustion. Using a large amount of trapped residual gas (TRG), is seen as one of the ways to achieve and control HCCI in a certain operating range. By varying the amount of TRG in the fresh air/fuel mixture (inside the cylinder), the charge mixture temperature, composition and pressure can be controlled and hence, the auto ignition timing and heat release rate. The controlled auto ignition (HCCI) engine concept has the potential to be highly efficient and to produce low NOx, carbon dioxide and particulate matter emissions.