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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.

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved.

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.

Driving when it is raining can be a stressful experience. Having a clear unobstructed view of the vehicles and road around you under these conditions is especially important. Heavy rain conditions can however overwhelm water management devices resulting in water rivulets flowing over the vehicle's side glass. These rivulets can significantly impair the driver's ability to see the door mirror, and laterally onto junctions. Designing water management features for vehicles is a challenging venture as testing is not normally possible until late in the design phase. Additionally traditional water management features such as grooves and channels have both undesirable design and wind noise implications. Having the ability to detect water management issues such as A-pillar overflow earlier in the design cycle is desirable to minimize the negative impact of water management features. Numerical simulation of windscreen water management is desirable for this reason.

A model for the evaporation of a multi-component fuel droplet is presented that takes account of temperature dependent fuel and vapour properties, evolving droplet internal temperature distribution and composition, and enhancement to heat and mass transfer due to droplet motion. The effect on the internal droplet mixing of non-ideal fluid diffusion is accounted for. Activity coefficients for vapour-liquid equilibrium and diffusion coefficients are determined using the UNIFAC method. Both well-mixed droplet evaporation (assuming infinite liquid mass diffusivity) and liquid diffusion-controlled droplet evaporation (iteratively solving the multi-component diffusion equation) have been considered. Well-mixed droplet evaporation may be applicable with slow evaporation, for example early gasoline direct injection; diffusion-controlled droplet evaporation must be considered when faster evaporation is encountered, for example when injection is later, or when the fuel mixture is non-ideal.

Little research has been done on spray characteristics of 2,5-dimethylfuran (DMF), since the breakthrough in its production method as an alternative fuel candidate. In this paper, the spray characteristics of pure fuels (DMF, Isooctane) and DMF-Isooctane blends under different ambient pressures (1 bar, 3 bar and 7 bar) and injection pressures (50 bar, 100 bar and 150 bar) were studied using Phase Doppler Particle Analyzer (PDPA) and high speed imaging. Droplet velocity, size distribution, spray angle and penetration of sprays were examined. Based on the results, DMF had larger SMD and penetration length than isooctane. The surface tension of fuel strongly influenced spray characteristics. Increasing the surface tension by 26 % resulted in 12 % increase in SMD. Higher ambient pressure increased the drag force, but SMD was not influenced by the increased drag force. However, the increased ambient pressure reduced the injection velocity and We number resulting in higher SMD.

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.

This study presents a one-dimensional model for the prediction of the pressure loss across a wall-flow gasoline particulate filter (GPF). The model is an extension of the earlier models of Bissett [1] and Konstandopoulos and Johnson [2] to the turbulent flow regime, which may occur at high flow rates and temperatures characteristic of gasoline engine exhaust. A strength of the proposed model is that only one parameter (wall permeability) needs to be calibrated. An experimental study of flow losses for cold and hot flow is presented, and a good agreement is demonstrated. Unlike zero-dimensional models, this model provides information about the flow along the channels and thus can be extended for studies of soot and ash accumulation, heat transfer and reaction kinetics.

In an era of rapidly increasing vehicle electrification, the gasoline engine remains a vital part of the passenger car powertrain portfolio. Lean-burn combustion is a formidable means for reducing the CO2 emissions of gasoline engines but demands the use of sophisticated emissions control. A 2.0 litre turbocharged direct-injection gasoline engine has been developed with a lean homogeneous combustion system matched to a robust lean and stoichiometric-capable exhaust aftertreatment. The aftertreatment system includes an SCR system and a GPF with filtration down to 10 nm particle size. The engine is equipped with a continuously variable valve-lift system, high-tumble ports and a high-energy ignition system; the boosting system comprises a variable geometry turbocharger and a 48 V electrical supercharger. The work reported formed part of the PaREGEn (Particle Reduced, Efficient Gasoline Engines) project under the Horizon 2020 framework programme.

The power demand of air conditioning in PHEVs is known to have a significant impact on the vehicle’s fuel economy and performance. Besides the cooling power associated to the passenger cabin, in many PHEVs, the air conditioning system provides power to cool the high voltage battery. Calculating the cooling power demands of the cabin and battery and their impact on the vehicle performance can help with developing optimum system design and energy management strategies. In this paper, a representative vehicle model is used to calculate these cooling requirements over a 24-hour duty cycle. A number of pre-cooling and after-run cooling strategies are studied and effect of each strategy on the performance of the vehicle including, energy efficiency, battery degradation and passenger thermal comfort are calculated. Results show that after-run cooling of the battery should be considered as it can lead to significant reductions in battery degradation.

Predicting Exterior Water Management is important for developing vehicles that meet customer expectations in adverse weather. Fluid film methods, with Lagrangian tracking, can provide spray and surface water simulations for complex vehicle geometries in on-road conditions. To cope with this complexity and provide practical engineering simulations, such methods rely on empirical sub-models to predict phenomena such as the film stripping from the surface. Experimental data to develop and validate such models is difficult to obtain therefore here a high-fidelity Coupled Level-set Volume of Fluid (CLSVOF) simulation is carried out. CLSVOF resolves the interface of the liquid in three dimensions; allowing direct simulation of film behaviour and interaction with the surrounding air. This is used to simulate a simplified wing-mirror, with air flow, on which water is introduced.

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.

The field of vision of the driver during wet road conditions is essential for safety at all times. Additionally, the safe use of the increasing number of sensors integrated in modern cars for autonomous driving and intelligent driver assistant systems has to be ensured even under challenging weather conditions. To fulfil these requirements during the development process of new cars, experimental and numerical investigations of vehicle soiling are performed. This paper presents the surface contamination of self- and foreign-soiling tested in the wind tunnel. For these type of tests, the fluorescence method is state-of-the-art and widely used for visualizing critical areas. In the last years, the importance of parameters like the contact angle have been identified when designing the experimental setup. In addition, new visualization techniques have been introduced.

A novel semi-analytical solution has been developed for the calculation of the static and dynamic response of an off road tire interacting with a deformable terrain, which utilizes soil parameters independent of the size of the contact patch (size-independent). The models involved in the solution presented, can be categorized in rigid and/or pneumatic tires, with or without tread pattern. After a concise literature review of related methods, a detailed presentation of the semi-analytical solution is presented, along with assumptions and limitations. A flowchart is provided, showing the main steps of the numerical implementation, and various test cases have been examined, characterized in terms of vertical load, tire dimensions, soil properties, deformability of the tire, and tread pattern. It has been found that the proposed model can qualitatively capture the response of a rolling wheel on deformable terrain.

The Range Rover Evoque is a compact luxury SUV, first introduced by Land Rover in 2012. Almost 800,000 units of the first-generation vehicle were sold. This paper explores some of the challenges entailed in developing the next generation of this successful product, maintaining key design cues while at the same time improving its aerodynamic efficiency. A development approach is outlined that made use of both numerical simulation and full-scale moving ground wind tunnel testing. A drag coefficient of 0.32 was obtained for the best derivative by paying particular attention to: the integration of active grille shutters; the front bumper and tyre package; brake cooling; underfloor design; wake control strategy; and detail optimization. This approach delivered the most aerodynamic Range Rover at the time of its introduction. The impact of these design changes on the aerodynamic flow field and consequently drag is highlighted.

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.

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.