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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.

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.

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.

Thermoelectric generator (TEG) has received more and more attention in its application in the harvesting of waste thermal energy in automotive engines. Even though the commercial Bismuth Telluride thermoelectric material only have 5% efficiency and 250°C hot side temperature limit, it is possible to generate peak 1kW electrical energy from a heavy-duty engine. If being equipped with 500W TEG, a passenger car has potential to save more than 2% fuel consumption and hence CO2 emission reduction. TEG has advantages of compact and motionless parts over other thermal harvest technologies such as Organic Rankine Cycle (ORC) and Turbo-Compound (TC). Intense research works are being carried on improving the thermal efficiency of the thermoelectric materials and increasing the hot side temperature limit. Future thermoelectric modules are expected to have 10% to 20% efficiency and over 500°C hot side temperature limit.

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.

The pursuit of improved fuel economy is becoming an increasingly important objective for automotive manufacturers. The field of thermo-electrics is highlighted as a promising technology. The figure of merit, Z is the primary measure of the effectiveness of a thermo-electric material, and the values now being offered by researchers have reached the level where new applications become attractive. It is feasible to consider such modules incorporated into a thermoelectric generator to recover waste heat from exhaust gas flow – an available energy stream that has traditionally been neglected as unusable. As a precursor to a costly experimental study it is desirable to accurately simulate the application of a thermo-electric system to a vehicle exhaust to understand both the feasibility and potential drawbacks.

China is the world’s largest automotive producer and has the world’s biggest automobile market. However, in the past decades, the development of China’s automotive industry has depended primarily on the foreign direct investment; domestic automakers have struggled in the lower ranks of car producers. In recent years, China’s automotive industry, supported by government policies, has been improving their Research and Development (R&D) capacity, to compete with their international peers. Against this background, China’s automotive industry requires a large number of R&D professionals who have not only a higher degree but also the applied and practical knowledge and skills of research. For the purpose of meeting the industry’s needs, a new Professional Automotive Engineering Masters Programme was launched in 2009, which aims to deliver the Masters to be the R&D professionals in the future.

This paper presents the implementation of a vehicle and powertrain model of the parallel hybrid electric vehicle which can be used for several purposes: as a model for estimating fuel consumption, as a model for estimating performance, and as a control model for the hybrid powertrain optimisation. The model is specified as a multi-domain physical model in MATLAB Simscape, which captures the key electrical, mechanical and thermal energy flows in the vehicles. By applying hand crafted boundary conditions, this model can be simulated either in the forwards or backwards direction, and it can easily be simplified as required to address specific control problems. Modelling in the forwards direction, the driver inputs are specified, and the vehicle response is the model output. In the backwards direction, the vehicle velocity as a function of time is the specified input, and the engine torque, and fuel consumption are the model outputs.

For the vehicles with frequent stop-start operations, fuel consumption can be reduced significantly by implementing stop-start operation. As one way to realize this goal, the pneumatic hybrid technology converts kinetic energy to pneumatic energy by compressing air into air tanks installed on the vehicle. The compressed air can then be reused to drive an air starter to realize a regenerative stop-start function. Furthermore, the pneumatic hybrid can eliminate turbo-lag by injecting compressed air into manifold and a correspondingly larger amount of fuel into the cylinder to build-up full-load torque almost immediately. This paper takes the pneumatic hybrid engine as the research object, focusing on evaluating the improvement of fuel economy of multiple air tanks in different test cycles. Also theoretical analysis the benefits of extra boost on reducing turbo-lag to achieve better performance.