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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.

GTDI engines are becoming more efficient, whether individually or part of a HEV (Hybrid Electric Vehicle) powertrain. For the latter, this efficiency manifests itself as increase in zero emissions vehicle mileage. An ideal device for energy recovery is a turbogenerator (TG), and, when placed downstream the conventional turbine, it has minimal impact on catalyst light-off and can be used as a bolt-on aftermarket device. A Ricardo WAVE model of a representative GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated using steady state mapping data. This was integrated into a co-simulation environment with a SISO (Single-Input, Single-Output) dynamic controller developed in SIMULINK for the actuator control (with BMEP, manifold air pressure and TG pressure ratio as the controlled variables).

In modern powertrains systems, sensors are critical elements for advanced control. The identification of sensing requirements for such highly nonlinear systems is technically challenging. To support the sensor selection process, this paper proposes a methodology to quantify the information gained from sensors used to control nonlinear dynamic systems using a dynamic probabilistic framework. This builds on previous work to design a Bayesian observer to deal with nonlinear systems. This was applied to a bimodal model of the SCR aftertreatment system. Despite correctly observing the bimodal distribution of the internal Ammonia-NOx Ratio (ANR) state, it could not distinguish which state is the true state. This causes issues for a control engineer who is less interested in how precise a measurement is and more interested in the location within control parameter space. Information regarding the dynamics of the systems is required to resolve the bimodality.

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).

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.

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.

Modern control strategies for internal combustion engines use increasingly complex networks of sensors and actuators to measure different physical parameters. Often indirect measurements and estimation of variables, based off sensor data, are used in the closed loop control of the engine and its subsystems. Thus, sensor fusion techniques and virtual instrumentation have become more significant to the control strategy. With the large volumes of data produced by the increasing number of sensors, the analysis of sensor networks has become more important. Understanding the value of the information they contain and how well it is extracted through uncertainty quantification will also become essential to the development of control architecture. This paper proposes a methodology to quantify how valuable a sensor is relative to the architecture. By modelling the sensor network as a Bayesian network, Bayesian analysis and control metrics were used to assess the value of the sensor.

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.

Efficiency and durability are key areas of research and development in modern racing drivetrains. Stringent regulations necessitate the need for components capable of operating under highly loaded conditions whilst being efficient and reliable. Downsizing, increasing the power-to-weight ratio and modification of gear teeth geometry to reduce friction are some of the actions undertaken to achieve these objectives. These approaches can however result in reduced structural integrity and component durability. Achieving a balance between system reliability and optimal efficiency requires detailed integrated multidisciplinary analyses, with the consideration of system dynamics, contact mechanics/tribology and stress analysis/structural integrity. This paper presents an analytical model to predict quasi-static contact power losses in lubricated spur gear sets operating under the Elastohydrodynamic regime of lubrication.

The TC48 project is developing a state-of-the-art, exceptionally low cost, 48V Plug-in hybrid electric (PHEV) demonstration drivetrain suitable for electrically powered urban driving, hybrid operation, and internal combustion engine powered high speed motoring. This paper explains the motivation for the project, and presents the layout options considered and the rationale by which these were reduced. The vehicle simulation model used to evaluate the layout options is described and discussed. The modelling work was used in order to support and justify the design choices made. The design of the vehicle's control systems is discussed, presenting simulation results. The physical embodiment of the design is not reported in this paper. The paper describes analysis of small vehicles in the marketplace, including aspects of range and cost, leading to the justification for the specification of the TC48 system.

This paper presents the findings from a numerical study of a gasoline direct injection engine flow using the Large Eddy Simulation (LES) modelling technique. The study is carried out over 30 successive engine cycles. The study illustrates how the more simple but robust Smagorinsky LES sub-grid scale turbulence model can be applied to a complex engine geometry with realistic engineering mesh size and computational expense whilst still meeting the filter width requirements to resolve the majority of large scale turbulent structures. Detailed description is provided here for the computational setup, including the initialisation strategy. The mesh is evaluated using a turbulence resolution parameter and shows the solution to generally resolve upwards of 80% of the turbulence kinetic energy.

Model-based calibration (MBC) is a systematic method to calibrate an engine control unit (ECU) system. Due to the working principle of MBC, it is only being used for steady-state systems (time independent models). This limits the use of MBC; because an ECU contains statistical and dynamical systems. Due to the limitations of MBC, dynamical systems require manual tuning which may be time-consuming. With the increasing popularity in hybrid and electrical vehicle systems, most of them rely on dynamical systems. Therefore, MBC is about to be superseded by manual parameterization methods. Remarkably, MBC is not limited to the steady state systems. It can be achieved by separating the time factor of a system and extracting the statistical data from a time series measurement. Typically, MBC model is conceived as the representation of a system plant (i.e.: air path, fuel path, mean value engine model). As a matter of fact, MBC model is not limited to identification of system plant.

Hybrid electric vehicles offer significant fuel economy benefits, because battery and fuel can be used as complementing energy sources. This paper presents the use of dynamic programming to find the optimal blend of power sources, leading to the lowest fuel consumption and the lowest level of harmful emissions. It is found that the optimal engine behavior differs substantially to an on-line adaptive control system previously designed for the Lotus Evora 414E. When analyzing the trade-off between emission and fuel consumption, CO and HC emissions show a traditional Pareto curve, whereas NOx emissions show a near linear relationship with a high penalty. These global optimization results are not directly applicable for online control, but they can guide the design of a more efficient hybrid control system.

A full calibration exercise of a diesel engine air path can take months to complete (depending on the number of variables). Model-based calibration approach can speed up the calibration process significantly. This paper discusses the overall calibration process of the air-path of the Cat® C7.1 engine using statistical machine learning tool. The standard Cat® C7.1 engine's twin-stage turbocharger was replaced by a VTG (Variable Turbine Geometry) as part of an evaluation of a novel air system. The changes made to the air-path system required a recalculation of the air path's boost set point and desired EGR set point maps. Statistical learning processes provided a firm basis to model and optimize the air path set point maps and allowed a healthy balance to be struck between the resources required for the exercise and the resulting data quality.

Low temperature combustion (LTC) in diesel engines offers attractive benefits through simultaneous reduction of nitrogen oxides and soot. However, it is known that the in-cylinder conditions typical of LTC operation tend to produce high emissions of unburned hydrocarbons (UHC) and carbon monoxide (CO), reducing combustion efficiency. The present study develops from the hypothesis that this characteristic poor combustion efficiency is due to in-cylinder mixture preparation strategies that are non-optimally matched to the requirements of the LTC combustion mode. In this work, the effects of three key fuel path parameters - injection fuel quantity ratio, dwell and injection timing - on CO and HC emissions were examined using a Central Composite Design (CCD) Design of Experiments (DOE) method.

Piston compression rings are thin, incomplete circular structures which are subject to complex motions during a typical 4-stroke internal combustion engine cycle. Ring dynamics comprises its inertial motion relative to the piston, within the confine of its seating groove. There are also elastodynamic modes, such as the ring in-plane motions. A number of modes can be excited, dependent on the net applied force. The latter includes the ring tension and cylinder pressure loading, both of which act outwards on the ring and conform it to the cylinder bore. There is also the radial inward force as the result of ring-bore conjunctional pressure (i.e. contact force). Under transient conditions, the inward and outward forces do not equilibrate, resulting in the small inertial radial motion of the ring.

Fuel cell hybrid systems have emerged rapidly in efforts to reduce emissions. The success of these systems mainly depends on implementation of suitable control architectures. This paper presents a control system design for a novel fuel cell - IC Engine hybrid power system. Control oriented models of the system components are developed and integrated. Based on the simulation results of the system model, the control variables are identified. The main objective for the control design is to manage fuel, air and exhaust flows in a way to deliver the required load on the system within local constraints. The controller developed for regulating flows in the system is based on model predictive control theory. The performance of the overall control system is assessed through simulations on a nonlinear dynamic model.

This paper presents a control system design strategy for a novel fuel cell - internal combustion engine hybrid power system. Dynamic control oriented models of the system components are developed. The transient behavior of the system components is investigated in order to determine control parameters and set-points. The analysis presented here is the first step towards development of a controller for this complex system. The results indicate various possibilities for control design and development. A control strategy is discussed to achieve system performance optimization.