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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.

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.

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.

The TC48 project is developing a state-of-the-art, exceptionally low cost, 48V Plug-in hybrid electric (PHEV) demonstration drivetrain suitable for electrically powered urban driving, hybrid operation, and internal combustion engine powered high speed motoring. This paper explains the motivation for the project, and presents the layout options considered and the rationale by which these were reduced. The vehicle simulation model used to evaluate the layout options is described and discussed. The modelling work was used in order to support and justify the design choices made. The design of the vehicle's control systems is discussed, presenting simulation results. The physical embodiment of the design is not reported in this paper. The paper describes analysis of small vehicles in the marketplace, including aspects of range and cost, leading to the justification for the specification of the TC48 system.

Design of Experiments (DOE) introduces a number of design types such as space filling design and optimal design. However, optimal design type is best for a system with high prior knowledge. Meanwhile, space-filling design is good for unknown systems, which is normal for engine calibration. It would be best to have a design that can support constructive model building, where a block of engine test is run for most of the day and followed by engine modeling at the end of the day. However, this needs separate space filling design for each day and separate design is susceptible to redundant test points. Among of the five space-filling design type, Sobol sequences and Halton sequences can support constructive model building due to the deterministic random sequence characteristic. When the model is good enough for system prediction, the remaining engine test can stop and proceed to model optimization.

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.

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.

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.

Measurement accuracy and repeatability for fuel rate is the key to successfully improve fuel economy of diesel engines as fuel economy could only be achieve by precisely controlling air/fuel ratio and monitor real-time fuel consumption. The volumetric and gravimetric measurement principles are well-known methods to measure the fuel consumption of internal combustion engines. However, the fuel flow rate measured by these methods is not suitable for either real-time control or real-time measurement purposes. The problem concerning discontinuous data of fuel flow rate measured by using an AVL 733s fuel meter was solved for the steady state scenario by using neural networks. It is easier to choose inputs of the neural networks for the steady state scenario because the inputs could be chosen as the particular inputs which excited the system in the application.

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.

The aim of this paper is to compile the state of the art of engine control and develop scenarios for improvements in a number of applications of engine control where the pace of technology change is at its most marked. The first application is control of downsized engines with enhancement of combustion using direct injection, variable valve actuation and turbo charging. The second application is electrification of the powertrain with its impact on engine control. Various architectures are explored such as micro, mild, full hybrid and range extenders. The third application is exhaust gas after-treatment, with a focus on the trade-off between engine and after-treatment control. The fourth application is implementation of powertrain control systems, hardware, software, methods, and tools. The paper summarizes several examples where the performance depends on the availability of control systems for automotive applications.

More and more stringent emission regulations require advanced control technologies for combustion engines. This goes along with increased monitoring requirements of engine behaviour. In case of emissions behaviour and fuel consumption the actual combustion efficiency is of highest interest. A key parameter of combustion conditions is the in-cylinder pressure during engine cycle. The measurement and detection is difficult and cost intensive. Hence, modelling of in-cylinder conditions is a promising approach for finding optimum control behaviour. However, on-line controller design requires real-time scenarios which are difficult to model and current modelling approaches are either time consuming or inaccurate. This paper presents a new approach of in-cylinder condition prediction. Rather than reconstructing in-cylinder pressure signals from vibration transferred signals through cylinder heads or rods this approach predicts the conditions.

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.

This work describes the application of Non-Linear Autoregressive Models with Exogenous Inputs (NLARX) in order to predict the NOx emissions of heavy-duty diesel engines. Two experiments are presented: 1.) a Non-Road-Transient-Cycle (NRTC) 2.) a composition of different engine operation modes and different engine calibrations. Data sets are pre-processed by normalization and re-arranged into training and validation sets. The chosen model is taken from the MATLAB Neural Network Toolbox using the algorithms provided. It is teacher forced trained and then validated. Training results show recognizable performance. However, the validation shows the potential of the chosen method.

Fuel cell hybrid systems have emerged rapidly in efforts to reduce emissions. The success of these systems mainly depends on implementation of suitable control architectures. This paper presents a control system design for a novel fuel cell - IC Engine hybrid power system. Control oriented models of the system components are developed and integrated. Based on the simulation results of the system model, the control variables are identified. The main objective for the control design is to manage fuel, air and exhaust flows in a way to deliver the required load on the system within local constraints. The controller developed for regulating flows in the system is based on model predictive control theory. The performance of the overall control system is assessed through simulations on a nonlinear dynamic model.

Traditionally, university research in engine technology has been focused on fundamental engine phenomena. Increasingly however, research topics are developing in the form of systems issues. Examples include air and exhaust gas recirculation (EGR) management, after-treatment systems, engine cooling, hybrid systems and energy recovery. This trend leads to the need for engine research to be conducted using currently available products and components that are re-configured or incrementally improved to support a particular research investigation. A production engine will include an electronic control unit (ECU) that must be understood and utilised or simply removed and circumvented. In general the intellectual property (IP) limitations places on ECUs by their suppliers mean that they cannot be used. The supplier of the ECU is usually unable to reveal any detail of the implementation. As a consequence any research using production hardware is seriously disadvantaged from the beginning.

Energy recovery from IC engines has proved to be of considerable interest across the range of vehicle applications. The motivation is substantial fuel economy gain that can be achieved with a minimal affect on the “host” technology of the vehicle. This paper reviews the initial results of a research project whose objective has been to identify system concepts and control methods for thermal recovery techniques. A vapour power cycle is the means of energy transfer. The architecture of the system is considered along with support of the fuel economy claims with the results of some hybrid vehicle modelling. An overview of the latest experimental equipment and design of the heat exchanger is presented. The choice of control architecture and strategy, whose goal is overall efficiency of the engine system, is presented and discussed. Some initial control results are presented.

This paper presents a control system design strategy for a novel fuel cell - internal combustion engine hybrid power system. Dynamic control oriented models of the system components are developed. The transient behavior of the system components is investigated in order to determine control parameters and set-points. The analysis presented here is the first step towards development of a controller for this complex system. The results indicate various possibilities for control design and development. A control strategy is discussed to achieve system performance optimization.

The idea of thermal energy recovery from vehicle engine exhaust flow is now well supported and funded. Through a number of research projects, several component technologies have been identified. Rankine cycle, turbo-compounding and thermo-electric systems have all attracted interest. Fuel economy improvements vary depending on the drive cycle and the capability of the underlying technologies, but have been reported as high as 25%. Our work at Sussex on a form of Rankine cycle has revealed generic issues about the control of thermal recovery and the associated modelling requirements. Typical issues include the balancing the rate of heat input to the recovery system with the loss of useful work from large temperature differences. The size of components dictates the control authority over the system and consequently its ability to follow changing conditions.

The process of controlling tailpipe emissions leads to the need to understand the dynamic behaviour of the after-treatment devices. The model provides the basis for design prediction, on-line diagnosis and real time control. Although a number of models have been presented in the literature, their efficient performance continues to require further development and validation to meet increasingly demanding requirements. Models have been developed that use the basic physical framework including thermal behaviour, fluid mechanics and basic chemistries. As more demands are placed on models, more phenomena need to be taken into account and in particular, progressively more of the chemistry of the Three-Way Catalyst (TWC) itself. In this paper we present a black-box model for a three-way catalytic converter that has been developed and tested using real experimental data.