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Jaguar Land Rover (JLR) has designed and developed a new inline 4 cylinder engine family, branded Ingenium. In addition to delivering improved emissions and fuel economy over the outgoing engine, another key aim from the outset of the program was to reduce the combustion noise. This paper details the NVH development of the lead engine in this family, a 2.0 liter common rail turbo diesel. The task from the outset of this new program was to reduce the mass of the engine by 21.5 kg, whilst also improving the structural attenuation of the engine by 5 dB in comparison to the outgoing engine. Improving the structural attenuation by 5 dB was not only a key enabler in reducing combustion noise, but also helped to achieve a certified CO2 performance of 99 g/km in the all-new Jaguar XE model, by allowing more scope for increasing cylinder pressure forcing without compromising NVH.

Tightening emissions regulations are driving increasing focus on both equipment and measurement capabilities in the test cell environment. Customer expectations are therefore rising with respect to data uncertainty. Key critical test cell parameters such as load, fuel rate, air flow and emission measurements are more heavily under scrutiny and require real time methods of verification over and above the traditional test cell calibration in 40CFR1065 regulation. The objective of this paper is to develop a system to use a carbon dioxide (CO2) based balance error and an oxygen (O2) based balance error for diagnosing the main measurement system error in the test cell such as fuel rate meter, air flow meter, emission sample line, pressure transducer and thermocouples. The general combustion equation is used to set up the balance equations with assumptions. To validate the air fuel ratio balance model an experimental investigation was carried out for D2 5 mode and C1 8 mode cycle test.

A prototype multi-hole diesel injector operating with n-heptane fuel from a high-pressure common rail system is used in a high-pressure and high-temperature test rig capable of reaching 1100 Kelvin and 150 bar under different oxygen concentrations. A novel optical set-up capable of visualizing the soot cloud evolution in the fuel jet from 30 to 85 millimeters from the nozzle exit with the high-speed color diffused back illumination technique is used as a result of the insertion of a high-pressure window in the injector holder opposite to the frontal window of the vessel. The experiments performed in this work used one wavelength provide information about physical of the soot properties, experimental results variating the operational conditions show the reduction of soot formation with an increase in injection pressure, a reduction in ambient temperature, a reduction in oxygen concentration or a reduction in ambient density.

This paper documents some of the findings from a joint JLR and AVL project which was conducted at the JLR Gaydon test facility in the UK. A testing and development efficiency concept is presented and test data quality is identified as a key factor. In support of this methods are proposed to correctly measure and set targets for data quality with high confidence. An illustrative example is presented involving a Diesel passenger car calibration process which requires response surface models (RSMs) of key engine measured quantities e.g. engine-out emissions and fuel consumption. Methods are proposed that attempt to quantify the relationships between RSM statistical model quality metrics, test data variability measures and design of experiment (DOE) formulation. The methods are tested using simulated and real test data.

The importance of designing and sizing a thermal management system for electric vehicle powertrains cannot be overstated. Traditional approaches often rely on model-based system design using supplier reference component data, which can inadvertently lead to undisclosed errors arising from the interactions between the components and the environment. This paper introduces a novel test facility for battery electric vehicle thermal management technology, which has been designed for neural network virtual sensor and non-linear multi-in multi-out control development. The paper demonstrates how a digital twin of the test bench can used to support the development of such technology. Additionally, this paper presents preliminary results from the test bench revealing insights into the performance and interactions of key components. For instance, there is an observed 30% reduction in the maximum flow rate of the pump integrated into the test bench compared to the specified value.