Search Results

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.

Injector nozzle deposits can have a profound effect on particulate emissions from vehicles fitted with Gasoline Direct Injection (GDI) engines. Several recent publications acknowledge the benefits of using Deposit Control Additives (DCA) to maintain or restore injector cleanliness and in turn minimise particulates, but others claim that high levels of DCA could have detrimental effects due to the direct contribution of DCA to particulates, that outweigh the benefits of injector cleanliness. Much of the aforementioned work was conducted in laboratory scenarios with model fuels. In this investigation a fleet of 7 used GDI vehicles were taken from the field to determine the net impact of DCAs on particulates in real-world scenarios. The vehicles tested comprised a range of vehicles from different manufacturers that were certified to Euro 5 and Euro 6 emissions standards.

The rotor and stator of electric motors consist of multiple materials, of which steel forms the majority of mass and volume. Steel in electric motors is commonly in the form of thin sheets (laminations), stacked along the axis of the rotor. The structural integrity of such a stack can be ensured using bolting, welding or bonding of the laminations. Predictive mechanical finite element simulations of these laminated stacks can become computationally intense because the steel sheets are thin, and the motor often contains hundreds of them. If the laminations are modelled individually, the size of the elements is very small compared to the overall dimensions and the interface between the laminations need to be modelled as well. In this paper, we present an alternate method of modelling this laminated stack as a single solid body using homogeneous and orthotropic material property, instead of representing each lamination.

The modern luxurious electric vehicle (EV) demands high torque and high-speed requirements with increased range. Fulfilling these requirements gives rise to the need for increased efficiency and power density of the motors in the Electric Drive Unit (EDU). Internal Permanent Magnet (IPM) motor is one of the best suited options in such scenarios because of its primary advantages of higher efficiency and precise control over torque and speed. In the IPM motor, permanent magnets are mounted within the rotor body to produce a resultant rotating magnetic field with the 3-phase AC current supply in the stator. IPM configuration provides structural integrity and high dynamic performance as the magnets are inserted within the rotor body. Adhesive glue is used to install the magnets within the laminated stack of rotor.

This paper outlines the history and background of the NLGI (formerly known as the National Lubricating Grease Institute) lubricating grease specifications, GC-LB classification of Automotive Service Greases as well as details on the development of new requirements for their High-Performance Multiuse (HPM) grease certification program. The performance of commercial lubricating grease formulations through NLGI's Certification Mark using the GC-LB Classification system and the recently introduced HPM grease certification program will be discussed. These certification programs have provided an internationally recognized specification for lubricating grease and automotive manufacturers, users and consumers since 1989. Although originally conceived as a specification for greases for the re-lubrication of automotive chassis and wheel bearings, GC-LB is today recognized as a mark of quality for a variety of different applications.

1Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure.

In this work, a novel bearing test rig was used to evaluate the impact of oil viscoelasticity on friction torque and oil film thickness in a hydrodynamic journal bearing. The test rig used an electric motor to rotate a test journal, while a hydraulic actuator applied radial load to the connecting rod bearing. Lubrication of the journal bearing was accomplished via a series of axial and radial drillings in the test shaft and journal, replicating oil delivery in a conventional engine crankshaft. Journal bearing inserts from a commercial, medium duty diesel engine (Cummins ISB) were used. Oil film thickness was measured using high precision eddy current sensors. Oil film thickness measurements were taken at two locations, allowing for calculation of minimum oil film thickness. A high-precision, in-line torque meter was used to measure friction torque. Four test oils were prepared and evaluated.

It is useful for research purposes to define simple surrogate gasoline compositions that can replicate the chemical and physical properties of more complex mixtures. Ethanol is used in commercially available gasolines around the world as part of the pathway to the decarbonization of the transportation sector. In this study equations were developed to predict the Research Octane Number (RON), Motor Octane Number (MON) and Dry Vapour Pressure Equivalent (DVPE) of gasoline surrogates containing ethanol (10-25 vol%), isopentane, n-heptane, isooctane and toluene. The non-linear blending behaviour associated with ethanol is found to necessitate coefficients in the equations developed for MON that are a function of ethanol content, whereas surprisingly the equations for RON and DVPE do not need this added level of complexity.

This paper presents an experimental method for measuring transmission paths from the exterior to the interior of a passenger vehicle using a reciprocal approach: A production vehicle was placed in a semi-anechoic environment; artificial noise sources were placed at the location of the occupant’s ear(s) inside the vehicle and beamforming arrays with a total of more than 300 microphones were used to observe apparent noise sources on the vehicle exterior resulting from transmission paths. This makes it possible to quickly measure transmission paths over the whole vehicle body. One of the motivations for this work is the monitoring of sealing quality on production vehicles. Artificial seal breaches were introduced on the vehicle and a number of excitation signals were assessed to develop a method to detect and localise leakage noise sources.

The reduction in wind noise is increasingly important to vehicle designers as overall vehicle refinement increases. Customers often fit accessories such as roof bars to vehicles, with the aerodynamic interaction of these components generating aeroacoustic noise sources. These are often tonal in nature and of particular annoyance to occupants. Sensors for automated driving fitted to future vehicles may also have a similar detrimental effect on vehicle refinement. Therefore, careful design of such components is important to minimise dissatisfaction. This paper presents the combined application of acoustic beamforming in a full-scale aeroacoustic wind tunnel and the use of a Lattice Boltzmann Method CFD code to characterise the aeroacoustic performance of a roof bar design when fitted to a production vehicle.

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.

As passenger cars are progressively moving towards more electrification, Plug-in Hybrid Electric Vehicles (PHEVs) may play a greater role. Several questions arise regarding their performance in real-world conditions, their optimal configuration - in terms of battery capacity, fuel and powertrain used - and their pollutant emissions. In this context, two PHEVs complying with Euro 6d standards were evaluated on a chassis-dyno and on-road using the same road profile, complying with RDE requirements. The two vehicles differ only by their powertrain, one being diesel-fueled, and the other being gasoline-fueled. The vehicles were tested under various conditions, including charge depleting and charge sustaining modes (i.e., tests respectively starting with a fully charged battery and a discharged battery), with various fuel compositions including traditional fossil-based fuels, 100% renewable Hydrotreated Vegetable Oil (HVO) and 100% renewable gasoline, blended with 20% v/v ethanol (E20).

Exhaust Gas Recirculation (EGR) is employed in diesel engines to reduce engine-out NOx emissions. Despite the concerted design efforts of manufacturers, high-pressure Exhaust Gas Recirculation (HP-EGR) systems can be susceptible to fouling as the particulate matter, hydrocarbons and other entrained species deposit from the exhaust gas flow as it cools on its passage through the EGR system. Such deposits can lead to a number of problems including deterioration of emissions, fuel efficiency, performance and drivability, as well as breakdowns. The development of an engine test method to enable the study of the impact of fuel on deposits in the HP-EGR system was reported in 2020. In the test, a 4-cylinder light-duty diesel engine of 1.6L displacement runs at conditions conducive to EGR deposit formation over 24 hours and the impact of fuels on deposit formation is determined through weighing of the EGR system components before and after the test.

Design efforts to improve the hydraulic efficiency of high-pressure diesel fuel systems and thus further improve overall engine efficiency have resulted in the utilisation of low-spill control valves and reduced injector component clearances to reduce general leakage losses. Overall, these advances have contributed significantly to the high efficiency diesel engines of today. However, the combination of very high fuel pressures, cavitation and low fuel leakage volumes increases the heating of the remaining fuel, increasing temperature and, in turn, the propensity for deposits to form inside the injector. This deposit phenomenon is commonly known as Internal Diesel Injector Deposits (IDID) and can cause rough engine running and failed engine starts requiring injector cleaning or replacement. Methods studying this phenomenon are under development in the industry.

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.

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.

The transport of fuel-borne additives into the engine oil is a critical factor for the efficacy with which the additive functionality can be imparted on the engine. This paper describes the combination of Laser Induced Fluorescence (LIF) and Liquid Chromatography (LC) to determine the real-time additive concentrations and transfer ratios in a spark-ignition, 2-liter GM LHU engine. The current research used a continuous sample circuit from the engine sump which passed through an integrating cavity flow cell to enhance the LIF signal. In the absence of a fluorescence signature of any of the native additive species, a suitable fluorescing dye was selected to simulate the additive. After establishing rigorous calibration curves, LC was employed as a referee method to do a direct comparison with the LIF determined dye concentrations.

Government regulations and consumer needs are driving automotive manufacturers to reduce vehicle energy consumption. However, this forms part of a complex landscape of regulation and customer needs. For instance, when reducing aerodynamic drag or vehicle weight for efficiency other important factors must be taken into account. This is seen in vehicle bonnet design. The bonnet is a large unsupported structure that is exposed to very high and often fluctuating aerodynamic loads, due to travelling in the wake of other vehicles. When travelling at high speed and in close proximity to other vehicles this unsteady aerodynamic loading can force the bonnet structure to vibrate, so-called “bonnet flutter”. A bonnet which is stiff enough to not flutter may be either too heavy for efficiency or insufficiently compliant to meet pedestrian safety requirements. On the other hand, a bonnet which flutters may be structurally compromised or undermine customer perceptions of vehicle quality.

Accurate modelling for spray vapour fields is critical to enable adequate predictions of spray ignition and combustion characteristics of non-premixed reacting diesel sprays. Spray vapour characteristics are in turn controlled by liquid atomization and the KH-RT liquid jet break-up model is regularly used to predict this: with the KH model used for predicting primary break-up given its definition as a surface wave growth model, and the RT model used for predicting secondary break-up due to it being a drag based, stripping model. This paper investigates how the alteration of the switching position of the KH and RT sub-models within the KH-RT model impacts the resulting vapour field and ignition characteristics. The combustion prediction is handled by the implementation of a 54 species, 269 reaction skeletal mechanism utilising a Well Stirred Reactor model within the Star-CD CFD code.