The electrification of auxiliary systems in service vehicles can noticeably reduce the engine idling time and fuel consumption. To replace an engine-driven auxiliary system with the electric one, size of required components (a battery pack and a generator) should be determined based on the information that can be obtained from propulsion and auxiliary loads of a target vehicle. Propulsion and auxiliary loads are defined as the portion of engine power that is used for moving the vehicle and auxiliary devices, respectively. In this paper, a model-based estimation algorithm is developed to estimate auxiliary and propulsion loads. The algorithm is validated using a hardware-in-the-loop system. The results show that the proposed algorithm can accurately identify propulsion and auxiliary loads in service vehicles.

Under research is a switched reluctance machine for the powertrain of an electric vehicle. The research focuses on accurate estimation and optimisation of winding losses in switched reluctance machines. These losses result from both AC and DC resistance. An FEA-based procedure is described which computes both components of loss. Round and rectangular conductors are both analysed for a number of cases. Ultimately, rectangular conductors are chosen for more thorough analysis. Here, a number of winding geometries are simulated. Plotting conductor geometry versus conductor loss confirms a loss trend with a global minimum. For the motor design under consideration, this represents up to 4 kW reduction in loss over the initially simulated round conductors.

Low temperature combustion (LTC) engines show promise for fuel economy improvement, however, these engines suffer from limited operating range. In this study, a specific type of LTC engine, known as homogeneous charge compression ignition (HCCI), is used in a series hybrid and extended range electric vehicle (E-REV) powertrain to investigate the ultimate fuel saving of a purely dedicated HCCI mode powertrain. Three types of energy management control (EMC) strategies including rule-based, dynamic programing (DP), and model predictive control (MPC) are designed and implemented. The simulation results show 17.7% fuel economy improvement in series HEV and 18.0% fuel economy improvement in E-REV compared to a conventional HEV using an SI engine in urban dynamometer driving schedule (UDDS) driving cycle. In addition, simulation results show that the HCCI fuel economy improvement increases in the driving cycles with greater base average power.

Low temperature combustion (LTC) engines exhibit potential to significantly reduce fuel consumption and nitric oxide emissions over traditional spark ignited (SI) engines. A prior study has shown that thermal barrier coatings (TBCs) can increase the temperature swing during combustion, thus bolstering both low load LTC operation and its combustion efficiency. However, no attempt has been made so far to maximise the benefits through optimisation of TBC thermal and morphological properties. In this work, a finite element model was developed to expeditiously determine crank-angle resolved TBC surface temperatures across a spectrum of potential TBC thermal and morphological properties and facilitate exploration over a large design space. The simulation was validated using crank-angle resolved temperature and heat flux data from TBC coated, fast-response thermocouples. Experiments were carried out using a metal piston, and a TBC coated piston. Subsequent numerical study characterises the sensitivity of the temperature swing on the surface to TBC conductivity.

In the current work, a series hybrid vehicle has been constructed that utilises a dual-fuel, reactivity controlled compression ignition (RCCI) engine. Full vehicle testing was conducted on chassis dynamometers over the US Environmental Protection Agency Federal Test Procedure, Highway Fuel Economy Test and US06 using RCCI combustion with commercially available gasoline and ultra-low sulphur diesel. Fuel economy and emissions data were recorded over the specified test cycles. Testing revealed that engine-out emissions were similar to steady-state dynamometer engine testing. However, tailpipe-out emissions saw a reduction of HC and CO during the HWFET of 98.5%. Fuel economy was lower than expected due to the higher drivetrain losses of the series hybrid drivetrain. Finally, simulated changes to the drivetrain showed that it may be possible to increase fuel economy significantly, while meeting EPA emissions standards.

An experimental study is presented of a novel dynamic calibration methodology applied to the air-path of a Jaguar Land Rover turbocharged diesel engine. The calibration is obtained in a one-shot process solely from data obtained from dynamic dynamometer testing, without any in-vehicle tuning. Although limited here to control of only boost pressure and exhaust gas recirculation rate with constraints on NOx and particle emissions, the methodology has the potential for a complete engine calibration including emission constrained optimisation of fuel consumption. The approach combines state space neural network (SSNN) modelling and optimisation to determine a feedforward Hammerstein-Wiener control map from the synthesised optimal control behaviour. The controller performance was verified by vehicle testing at the Jaguar Land Rover Test Track, Gaydon, UK. The outcome demonstrates a method for systematically obtaining dynamic control calibrations for good driveability and reduced emissions from limited dynamometer testing time without the need for manual tuning.

Advanced combustion concepts like partially premixed combustion (PPC) are sensitive to inlet gas conditions especially inlet temperature. To estimate the combustion parameters like ignition delay on a cycle-to-cycle basis, both complementary filter and extended Kalman filter were designed to estimate the inlet temperature, and the ignition delay was calculated with properly estimated temperature. A two valve fast thermal management system was used to control the inlet temperature, and a PI controller with the lambda tuning method was used. The estimation and control algorithm were verified on a six-cylinder heavy duty engine.

This paper shows the application of linearised hierarchical models to the estimation of indicated torque data obtained from a large scale engine mapping experiment conducted on a turbocharged spark-ignition engine. Unlike previous studies that have utilised two-stage regression techniques for analysis, the use of linearised methods provides a framework to directly address the issues of sparseness of sweep-specific data and mixed effects modelling. In addition, spline models are presented at both levels of the model hierarchy that possess the required smoothness, are capable of capturing physical behaviour and simultaneously yield models sufficiently accurate for calibration work. The paper considers, at length, the required model fitting procedures which are founded on an iterative generalised least-squares approach. Further, a model building case study is presented addressing the issue of which factors should be modelled as fixed or mixed. This utilises information criteria to identify the most parsimonious model. Finally, the fit provided by the model to the data is demonstrated, which is seen to satisfy the engineering measure of success applicable to the application.