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With the electrification of the automotive industry, electric motors have emerged as pivotal components. A profound understanding of their vibrational behaviour stands as a cornerstone for guaranteeing not only the optimal performance and reliability of vehicles in terms of noise, vibration, and harshness (NVH), but also the overall driving experience. The use of conventional finite element analysis (FEA) techniques for identification of the natural frequencies characteristics of electric motors often imposes significant computational loads, particularly when accurate material and geometrical properties and wider frequency ranges are considered. On the other hand, traditional reduced order vibroacoustic methodologies utilising simplified 2D representations, introduce several assumptions regarding boundary conditions and properties, leading to sacrifices in the accuracy of the results.

The primary function of automotive windscreen wipers is to remove excess water and debris to secure a clear view for the driver. Their successful operation is imperative to vehicle occupants’ safety. To avoid reliance on experimental testing there is a need to develop physics-based models that can quantify the effects of design-based decisions on automotive wipers. This work presents a suite of evaluative tools that can provide quantitative data on the effects of design decisions. We analyse the complex non-linear contact interaction between the wiper blade and the automotive screen using finite element analysis, assessing the impact of blade geometry on the contact distribution. The influence of the evolution of normal applied load by the wiper arm is also investigated as to how it impacts the contact distribution evolution. The dynamics of the blade are subsequently analysed using a multiple connected mass spring damper system.

Considerations of surface contamination and airborne spray are becoming increasingly significant throughout the automotive design process. Advanced driver assistance systems, such as autonomous cruise control, are growing in popularity. These systems rely on external sensors, the performance of which may be impaired by both direct obstruction and spray. Existing experimental methods of assessing front-end surface contamination and wiper performance have typically utilised fixed spray-grids positioned upstream of the vehicle. The resulting spray is largely steady in nature, in contrast to the unsteady flow-field and tyre spray that would be produced by preceding vehicles. This paper presents the numerical analysis of the spray ejected downstream of a simplified automotive body. The continuous phase (air) is solved using a DDES-based approach coupled with a Lagrangian representation of the dispersed phase (water).

This paper presents the experimental work carried out on single-lap joints fastened together with bolts and nuts to investigate the contribution of mode shapes, and the effect that bolt sizes has in dissipating energy in built-up structures. Five different bolt sizes are chosen to assemble five single-bolted single-lap joints using aluminum plates. An analogous monolithic solid piece carved from the same aluminum material is used to determine the material damping and compare it against the damping from bolted joints. The dynamic response of all structures is captured under free-free boundary conditions, and the common modes are analyzed to understand the contribution and primary source of damping in the same range of the sampling frequency.

The replacement of the ICE engine with an electric motor has led to a significant reduction in vibration and noise. The characteristics of the electric motor as part of the powertrain still need consideration from an NVH perspective, as there are still two highly tonal components generating noise to the cabin, albeit at higher frequencies. The radial electromagnetic force causes a structural vibration on the casing which changes with motor speed and can be used to indicate vehicle speed. The current excitation causes a primarily tangential force on the poles of the motor at a specific frequency, but both are narrow band and can cause annoyance. The traditional approach to predicting the sound radiation of electric motors is usually based on finite element analysis (FEA). While this method has the capability to estimate the time response, it is computationally too demanding and does not allow for early investigations at systems level.

Low temperature combustion (LTC) of diesel fuel offers a path to low engine emissions of nitrogen oxides (NOx) and particulate matter (PM), especially at low loads. Borescopic optical imaging offers insight into key aspects of the combustion process without significantly disrupting the engine geometry. To assess LTC combustion, two-colour pyrometry can be used to quantify local temperatures and soot concentrations (KL factor). High sensitivity photo-multiplier tubes (PMTs) can resolve natural luminosity down to low temperatures with adequate signal-to-noise ratios. In this work the authors present the calibration and implementation of a borescope-based system for evaluating low luminosity LTC using spatially resolved visible flame imaging and high-sensitivity PMT data to quantify the luminous-area average temperature and soot concentration for temperatures from 1350-2600 K.

Sensor selection for the control of modern powertrains is a recognised technical challenge. The key question is which set of sensors is best suited for an effective control strategy? This paper addresses the question through probabilistic modelling and Bayesian analysis. By quantifying uncertainties in the model, the propagation of sensor information throughout the model can be observed. The specific example is an abstract model of the slip behaviour of Selective Catalytic Reduction (SCR) DeNOx aftertreatment systems. Due to the ambiguity of the sensor reading, linearization-based approaches including the Extended Kalman Filter, or the Unscented Kalman Filter are not successful in resolving this problem. The stochastic literature suggests approximating these nonlinear distributions using methods such as Markov Chain Monte Carlo (MCMC), which is able in principle to resolve bimodal or multimodal results.

Full hybrid electric vehicles are usually defined by their capability to drive in a fully electric mode, offering the advantage that they do not produce any emissions at the point of use. This is particularly important in built up areas, where localized emissions in the form of NOx and particulate matter may worsen health issues such as respiratory disease. However, high degrees of electrification also mean that waste heat from the internal combustion engine is often not available for heating the cabin and for maintaining the temperature of the powertrain and emissions control system. If not managed properly, this can result in increased fuel consumption, exhaust emissions, and reduced electric-only range at moderately high or low ambient temperatures negating many of the benefits of the electrification. This paper describes the development of a holistic, modular vehicle model designed for development of an integrated thermal energy management strategy.

Significant aerodynamic drag reduction is obtained on a bluff body by tapering the rear body. In the 1930’s it was found that a practical low drag car body could be achieved by cutting off the tail of a streamlined shape. The rear end of a car with a truncated tail is commonly referred to as a Kamm back. It has often been interpreted as implying that the drag of this type of body is almost the same as that for a fully streamlined shape. From a review of the limited research into truncated streamlined tails it is shown in this paper that, while true for some near axisymmetric bodies, it is not the case for many more car-like shapes. For these shapes the drag reduction from an elongated tail varies almost linearly with the reduction in cross section area. A CFD simulation to determine the drag reduction from a truncated streamlined tail of variable length on the simple Windsor Body is shown by way of confirmation.

Understanding and predicting in-cylinder flow structures that occur within compression-ignition engines is vital if further optimisation of combustion systems is to be achieved. To enable this prediction, fully validated computational models of the complex turbulent flow-fields generated during the intake and compression process are needed. However, generating, analysing and interpreting experimental data to achieve this validation remains a complex challenge due to the variability that occurs from cycle to cycle. The flow-velocity data gathered in this study, obtained from a single-cylinder CI engine with optical access using high-speed PIV, demonstrates that significantly different structures are generated over different cycles, resulting in the mean flow failing to adequately reflect the typical flow produced in-cylinder.

Proton exchange membrane fuel cell (PEMFC) provides a promising future low carbon automotive powertrain solution. The catalyst layer (CL) is its core component which directly influences the output performance. PEMFC performance can be greatly improved by the effective optimization of CL composition. This work demonstrates a deep optimization of CL composition for improving the PEMFC performance, including the platinum (Pt) loading, Pt percentage of carbon-supported Pt and ionomer to carbon ratio of the anode and the cathode,. The simulation results by a PEMFC three-dimensional (3D) computation fluid dynamics (CFD) model coupled with the CL agglomerate model is used to train the artificial neural network (ANN) which can efficiently predict the current density under different CL composition. Squared correlation coefficient (R-square) and mean percentage error in the training set and validation set are 0.9867, 0.2635% and 0.9543, 1.1275%, respectively.

The process of engine mapping in the automotive industry identifies steady-state engine responses by running an engine at a given operating point (speed and load) until its output has settled. While the time simulating this process with a computational model for one set of parameters is relatively short, the cumulative time to map all possible combinations becomes computationally inefficient. This work presents an alternative method for mapping out the steady-state response of an engine in simulation by applying bifurcation theory. The bifurcation approach used in this work allows the engine’s steady-state response to be traced through the model’s state-parameter space under the simultaneous variation of one or more model parameters. To demonstrate this approach, a bifurcation analysis of a simplified nonlinear engine model is presented.

Many modern I.C. engines rely on some form of active control of injection, timing and/or ignition timing to help combat tailpipe out emissions, increase the fuel economy and improve engine drivability. However, development of these strategies is often optimised to suit the average cycle at each condition; an assumption that can lead to sub-optimal performance, especially an increase in particulate (PN) emissions as I.C. engine operation, and in-particular its charge motion is subject to cycle-to-cycle variation (CCV). Literature shows that the locations of otherwise repeatable large-scale flow structures may vary by as much 25% of the bore dimension; this could have an impact on fuel break-up and distribution and therefore subsequent combustion performance and emissions.

In-cylinder temperatures and their cyclic variations strongly influence many aspects of internal combustion engine operation, from chemical reaction rates determining the production of NOx and particulate matter to the tendency for auto-ignition leading to knock in spark ignition engines. Spatially resolved measurements of temperature can provide insights into such processes and enable validation of Computational Fluid Dynamics simulations used to model engine performance and guide engine design. This work uses a combination of Two-Colour Planar Laser Induced Fluorescence (TC-PLIF) and Laser Induced Grating Spectroscopy (LIGS) to measure the in-cylinder temperature distributions of a firing optically accessible spark ignition engine. TC-PLIF performs 2-D temperature measurements using fluorescence emission in two different wavelength bands but requires calibration under conditions of known temperature, pressure and composition.

Lightly damped non-linear systems such as vehicular drivelines undergo a plethora of Noise, Vibration and Harshness (NVH) problems. The clonk phenomenon is one concern which occurs as the result of impulsive torque input in the form of sudden clutch actuation or throttle tip-in and back-out. The resulting impact of meshing gear pairs propagate structural waves down the driveline. With lightly damped thin-walled tubes having high modal density, elasto-acoustic coupling occurs. High frequency noise emission is of metallic nature and quite disconcerting to vehicle occupants as well as passers-by. It is perceived as structural failure and/or poor-quality build. Therefore, the occurrence of the phenomenon is a concern to vehicle manufacturers and progressively constitutes a warranty concern. This paper investigates the clonk phenomenon through use of a long-wheel base rear drive light truck test rig.

Limiting problematic Noise, Vibration and Harshness (NVH) phenomena in the modern automotive drivetrain is a task of critical importance. The result of such phenomena is the aggravation of the driver, which results in a reduced perception of vehicle quality. Each phenomenon can be characterized by a distinct frequency range. The aim of the current study is to assess the influence of the interfacial frictional behavior of the dry friction clutch components on the drivetrain dynamic behavior. The dynamics of the system (in terms of its stability) are studied. Surface data from the clutch components are critical to understanding the complex engagement process. The coefficient of friction was measured using a rotary tribometer at representative slip speeds and contact pressures. To aid the analysis infinite focus microscopy was used to measure the geometric properties of the constituent components of the drivetrain.

Cars in several motor sports series, such as Formula 1, make use of multi-element front wings to provide downforce. These wings also provide onset flows to other surfaces that generate downforce. These elements are highly loaded to maximise their performance and are generally operating close to stall. Rubber debris, often known as marbles, created from the high slip experienced by the soft compound tyres can become lodged in the multiple elements of a front wing. This will lead to a reduction in the effectiveness of the wing over the course of a race. This work will study the effect of such debris, both experimentally and numerically, on an inverted double element wing in ground effect at representative Reynolds numbers. The wing was mounted at two different ride heights above a fixed false-floor in the Loughborough University wind tunnel and the effect of debris blockage modelled by closing sections of the gap between elements with tape.

This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and non-minimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited.

With the continuing improvements to thermoelectric (TE) materials and systems, their potential for both energy recovery and thermal management is increasingly apparent. Recent developments in materials and notably Skutterudites have allowed materials to be matched much more closely to the working temperatures of a light duty power-train. The choice of TE materials remains a substantial question in the design of a thermoelectric generator (TEG). While the quest for improvements in materials performance continues, the work reported in this paper is characterized by the decision to focus on the refinement of one class of TE materials: Skutterudites. In parallel, the engineering work on the integration of the TE materials into a heat exchanger could continue and be focused on the properties of this class of material. Skutterudites offer the combination of a high working temperature and a competitive electrical output (defined by ZT, the figure of merit).

Thermoelectric generator (TEG) has received more and more attention in its application in the harvesting of waste thermal energy in automotive engines. Even though the commercial Bismuth Telluride thermoelectric material only have 5% efficiency and 250°C hot side temperature limit, it is possible to generate peak 1kW electrical energy from a heavy-duty engine. If being equipped with 500W TEG, a passenger car has potential to save more than 2% fuel consumption and hence CO2 emission reduction. TEG has advantages of compact and motionless parts over other thermal harvest technologies such as Organic Rankine Cycle (ORC) and Turbo-Compound (TC). Intense research works are being carried on improving the thermal efficiency of the thermoelectric materials and increasing the hot side temperature limit. Future thermoelectric modules are expected to have 10% to 20% efficiency and over 500°C hot side temperature limit.