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Fuel efficiency and torque performance are two major challenges for highly downsized turbocharged engines. However, the inherent characteristics of the turbocharged SI engine such as negative PMEP, knock sensitivity and poor transient performance significantly limit its maximum potential. Conventional ways of improving the problems above normally concentrate solely on the engine side or turbocharger side leaving the exhaust manifold in between ignored. This paper investigates this neglected area by highlighting a novel means of gas exchange process. Divided Exhaust Period (DEP) is an alternative way of accomplishing the gas exchange process in turbocharged engines. The DEP concept engine features two exhaust valves but with separated function. The blow-down valve acts like a traditional turbocharged exhaust valve to evacuate the first portion of the exhaust gas to the turbine.

Throttling loss of downsized gasoline engines is significantly smaller than that of naturally aspirated counterparts. However, even the extremely downsized gasoline engine can still suffer a relatively large throttling loss when operating under part load conditions. Various de-throttling concepts have been proposed recently, such as using a FGT or VGT turbine on the intake as a de-throttling mechanism or applying valve throttling to control the charge airflow. Although they all can adjust the mass air flow without a throttle in regular use, an extra component or complicated control strategies have to be adopted. This paper will, for the first time, propose a de-throttling concept in a twin-charged gasoline engine with minimum modification of the existing system. The research engine model which this paper is based on is a 60% downsized 2.0L four cylinder gasoline demonstrator engine with both a supercharger and turbocharger on the intake.

Engines equipped with pressure charging systems are more prone to knock partly due the increased intake temperature. Meanwhile, turbocharged engines when operating at high engine speeds and loads cannot fully utilize the exhaust energy as the wastegate is opened to prevent overboost. The turboexpansion concept thus is conceived to reduce the intake temperature by utilizing some otherwise unexploited exhaust energy. This concept can be applied to any turbocharged engines equipped with both a compressor and a turbine-like expander on the intake loop. The turbocharging system is designed to achieve maximum utilization of the exhaust energy, from which the intake charge is over-boosted. After the intercooler, the turbine-like expander expands the over-compressed intake charge to the required plenum pressure and reduces its temperature whilst recovering some energy through the connection to the crankshaft.

Vehicle efficiency and range, along with the DC charging speed, are deemed as the most important criteria for an electric vehicle currently. The electric vehicle energy consumption is impacted by the change in temperature along with the driving style and average speed of a customer, all other factors being constant. Hence understanding the patterns and impact of different aspects of an EV range & charging speed is crucial in delivering an electric vehicle with robust efficiency across all weather conditions. In this paper we have analysed vehicle parameters of global Jaguar I-PACE customer data. We present and analyse the collated big data of around 50,000+ unique vehicles with a data aggregate of well over 482 million km. In moderate ambient conditions the analysis indicated a good correlation with 50th to 75th percentile drivers’ energy consumption to the EPA label figure.

Complexity of electronics and embedded software systems in automobiles has been increasing over the years. This necessitates the need for an effective and exhaustive development and validation process in order to deliver fault free vehicles at reduced time to market. Model-based Product Engineering (MBPE) is a new process for development and validation of embedded control software. The process is generic and defines the engineering activities to plan and assess the progress and quality of the software developed for automotive applications. The MBPE process is comprised of six levels (one design level and five verification and validation levels) ranging from the vehicle requirements phase to the start of production. The process describes the work products to be delivered during the course of product development and also aligns the delivery plan to overall vehicle development milestones.

The new fuel, 2, 5-dimenthylfuran, known as DMF, captured worldwide attention since the discovery of its new production method. As a potential bio-fuel, DMF is competitive to gasoline in many areas, such as energy density, combustion efficiency and emissions. However, little work has been performed on its unconventional combustion mode. In this work, high speed imaging and thermal investigation are carried out to study DMF and gasoline dual-injection on a single cylinder, direct injection spark ignition optical engine. This dual-injection strategy combines direct injection (DI) and port fuel injection (PFI) simultaneously which means two different fuels can blend in the cylinder with any ratio. It provides a flexible way to use bio-fuels with gasoline. DMF DI with gasoline PFI and ethanol DI with gasoline PFI are studied under different injection proportions (by volume) and IMEPs.

This paper presents the development and implementation of an Analytical Target Cascading (ATC) Multi-disciplinary Design Optimisation (MDO) framework for the steady state engine calibration optimisation problem. The case is made that the ATC offers a convenient framework for the engine calibration optimisation problem based on steady state engine test data collected at specified engine speed / load points, which is naturally structured on 2 hierarchical levels: the ‘Global’ level, associated with performance over a drive cycle, and ‘Local’ level, relating to engine operation at each speed / load point. The case study of a diesel engine was considered to study the application of the ATC framework to a calibration optimisation problem. The paper describes the analysis and mathematical formulation of the diesel engine calibration optimisation as an ATC framework, and its Matlab implementation with gradient based and evolutionary optimisation algorithms.

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.

Brake disc materials are being utilised that have low noise/dust properties, but are sensitive to contamination by surface water. This drives large dust shields, making brake cooling increasingly difficult. However, brake cooling must be delivered without compromising aerodynamic drag and hence CO2 emissions targets. Given that front brake discs sit in a region of geometric, packaging and flow complexity optimization of their performance requires the analysis of thermal, aerodynamic and multi-phase flows. Some of the difficulties inherent in this task would be alleviated if the complete analysis could be performed in the same CAE environment: utilizing common models and the same solver technology. Hence the project described in this paper has sought to develop a CFD method that predicts the amount of contamination (water) that reaches the front brake discs, using a standard commercial code already exploited for both brake disc thermal and aerodynamics analysis.

For electric vehicles (EVs), driving range is one of the major concerns for wider customer acceptance and the cabin climate system represents the most significant auxiliary load for battery consumption. Unlike internally combustion engine (ICE) vehicles, EVs cannot utilize the waste heat from an engine to heat the cabin through the heating, ventilation and air conditioning (HVAC) system. Instead, EVs use battery energy for cabin heating, this reduces the driving range. To mitigate this situation, one of the most promising solutions is to optimize the recirculation of cabin air, to minimize the energy consumed by heating the cold ambient air through the HVAC system, whilst maintaining the same level of cabin comfort. However, the development of this controller is challenging, due to the coupled, nonlinear and multi-input multi-output nature of the HVAC and thermal systems.

Flash-boiling of sprays may occur when a superheated liquid is discharged into an ambient environment with lower pressure than its saturation pressure. Such conditions normally exist in direct-injection spark-ignition engines operating at low in-cylinder pressures and/or high fuel temperatures. The addition of novel high volatile additives/fuels may also promote flash-boiling. Fuel flashing plays a significant role in mixture formation by promoting faster breakup and higher fuel evaporation rates compared to non-flashing conditions. Therefore, fundamental understanding of the characteristics of flashing sprays is necessary for the development of more efficient mixture formation. The present computational work focuses on modelling flash-boiling of n-Pentane and iso-Octane sprays using a Lagrangian particle tracking technique.

A potentially important, but inadequately studied, source of passengers' exposure to pollutants when a road vehicle is stationary, with an idling engine, results from the ingestion of a vehicle's own exhaust into the passenger compartment through the HVAC intake. We developed and applied a method to determine the fraction of a vehicle's exhaust entering the cabin by this route. Further the influence of three parameters: ambient tail-wind speed, vehicle ground clearance and tail pipe angle, is assessed. The study applies Computational Fluid Dynamic (CFD) simulation to the distribution of exhaust gasses around a vehicle motorized with a 2.2 liter Diesel engine. The simulation employs efficient meshing techniques and realistic loading conditions to develop a general knowledge of the distribution of the gasses in order to inform engineering design.

Simulation of local flow structures in the A-pillar/side glass region of bluff SUV geometries, typical of Land Rover vehicles, presents a considerable challenge. Features such as relatively tight A-pillar radii and upright windscreens produce flows that are difficult to simulate. However, the usefulness of aerodynamics simulations in the early assessment of wind noise depends particularly on the local accuracy obtained in this region. This paper extends work previously published by the author(1) with additional data and analysis. An extended review of the relevant published literature is also provided. Then the degree to which a commercial Lattice-Boltzman solver (Exa PowerFLOW™) is currently able to capture both the local flow structure and surface pressure distribution (both time averaged and unsteady) is evaluated. Influential factors in the simulation are shown to be spatial resolution, turbulence and boundary layer modelling.

Modern diesel cars, fitted with state-of-the-art aftertreatment systems, have the capability to emit extremely low levels of pollutant species at the tailpipe. However, diesel aftertreatment systems can represent a significant cost, packaging and maintenance requirement. Reducing engine-out emissions in order to reduce the scale of the aftertreatment system is therefore a high priority research topic. Engine-out emissions from diesel engines are, to a significant degree, dependent on the detail of fuel/air interactions that occur in-cylinder, both during the injection and combustion events and also due to the induced air motion in and around the bowl prior to injection. In this paper the effect of two different piston bowl shapes are investigated.

Diesel engine designers often use swirl flaps to increase air motion in cylinder at low engine speeds, where lower piston velocities reduce natural in-cylinder swirl. Such in-cylinder motion reduces smoke and CO emissions by improved fuel-air mixing. However, swirl flaps, acting like a throttle on a gasoline engine, create an additional pressure drop in the inlet manifold and thereby increase pumping work and fuel consumption. In addition, by increasing the fuel-air mixing in cylinder the combustion duration is shortened and the combustion temperature is increased; this has the effect of increasing NOx emissions. Typically, EGR rates are correspondingly increased to mitigate this effect. Late inlet valve closure, which reduces an engine’s effective compression ratio, has been shown to provide an alternative method of reducing NOx emissions.

Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach.

This paper describes the findings of a design, simulation and test study into how to reduce particulate number (Pn) emissions in order to meet EU6c legislative limits. The objective of the study was to evaluate the Pn potential of a modern 6-cylinder engine with respect to hardware and calibration when fitted to a full size SUV. Having understood this capability, to redesign the combustion system and optimise the calibration in order to meet an engineering target value of 3×1011 Pn #/km using the NEDC drive cycle. The design and simulation tasks were conducted by JLR with support from AVL. The calibration and all of the vehicle testing was conducted by AVL, in Graz. Extensive design and CFD work was conducted to refine the inlet port, piston crown and injector spray pattern in order to reduce surface wetting and improve air to fuel mixing homogeneity. The design and CFD steps are detailed along with the results compared to target.

The paper discusses the effects of various charging system technologies on the performance and fuel consumption of a modern supercharged engine, the Jaguar Land Rover AJ126 3.0 litre V6. The goal of the project was to improve performance and reduce the fuel consumption of the standard engine by researching new technologies around the supercharger. As standard the AJ126 engine uses an Eaton R1320 supercharger with a fixed ratio drive from the crankshaft and no clutch.

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.

Hybrid technologies enable the reduction of noxious tailpipe emissions and conformance with ever-decreasing allowable homologation limits. The complexity of the hybrid powertrain technology leads to an energy management problem with multiple energy sinks and sources comprising the system resulting in a high-dimensional time dependent problem for which many solutions have been proposed. Methods that rely on accurate predictions of potential vehicle operations are demonstrably more optimal when compared to rule-based methodology [1]. In this paper, a previously proposed energy management strategy based on an offline optimization using dynamic programming is investigated. This is then coupled with an online model predictive control strategy to follow the predetermined optimal battery state of charge trajectory prescribed by the dynamic program.