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Electric vehicles offer cleaner transportation with lower emissions, thus their increased popularity. Although, electric powertrains contribute to quieter vehicles, the shift from internal combustion engines to electric powertrains presents new Noise, Vibration, and Harshness challenges. Unlike traditional engines, electric powertrains produce distinctive tonal noise, notably from motor whistles and gear whine. These tonal components have frequency content, sometimes above 10 kHz. Furthermore, the housing of the powertrain is the interface between the excitation from the driveline via the bearings and the radiated noise (NVH). Acoustic features of the radiated noise can be predicted by utilising the transmitted forces from the bearings. Due to tonal components at higher frequencies and dense modal content, full flexible multibody dynamics simulations are computationally expensive.

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.

Vehicle emissions and fuel economy in real-world driving conditions are currently under considerable scrutiny. Key to achieving optimum performance for a given hardware set and control scheme is a calibration that optimizes controller gains such that inputs are scheduled over the operating space to minimize emissions and maximize fuel economy. Generating a suitable calibration requires data that is both precise and accurate, this data is used to generate models that are deployed as part of the calibration optimization process. This paper evaluates the repeatability of typical steady-state measurements used for calibration of engine controllers that will ultimately determine vehicle level emissions for homologation include Real Driving Emissions (RDE). Stabilization requirements as indicated by three different measurements are evaluated and shown to be different within the same experiment, depending on the metric used.

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.

A model-based urea-dosing controller has been developed for the selective catalytic reduction (SCR) units on a diesel engine exhaust aftertreatment system (EATS). The SCR units consist of an integrated SCR-coated filter and then followed by a flow-through SCR catalyst. The controller was developed based on an analysis of the data generated from a Millbrook London Transport Bus (MLTB) test cycle fed into a validated model of the SCR-filter and SCR units. The critical system parameters that showed strong correlation with outlet nitrogen oxides (NOx) and ammonia (NH3) emissions were first identified, and then the sensitivity of those parameters was analyzed. The most sensitive system parameters were configured as the controller gain parameters. A proportional controller based on the key parameters with optimized gains settings for the MLTB cycle delivered over a 10% reduction in cumulative NOx emission over the cycle compared to a fixed NH3/NOx ratio (ANR) controller.

Modern thermal propulsion systems (TPS) as part of hybrid powertrains are becoming increasingly complex. They have an increased number of components in comparison to traditionally powered vehicles leading to increased demand in packaging requirements. Many of the components in these systems relate to achieving efficiency gains, weight saving and pollutant reduction. This includes turbochargers and diesel or gasoline particulate filters for example and these are known to be very sensitive to inlet boundary conditions. When overcoming packaging requirements, sub-optimal flow distributions throughout the TPS can easily occur. Moreover, the individual components are often designed in isolation assuming relatively flat and artificially quiescent inlet flow conditions in comparison to those they are actually presented with. Thus, some of the efficiency benefits are lost through reduced component aerodynamic efficiency.

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.

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.

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.

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.

The worldwide introduction of new emission standards and new, more encompassing, legislating cycles have led to a need to increase both a selective catalytic reduction (SCR) system’s capacity and conversion efficiency. To this end, it is important for an SCR system to operate to the extremes of its temperature range which in many systems is currently limited by the temperature at which diesel exhaust fluid (DEF) can easily decompose without the formation of deposits. This paper analyses a new system for low-temperature ammonia provision to the SCR reaction. Ammonia Creation and Conversion Technology (ACCT) uses pressure controlled thermal decomposition of DEF followed by re-formation to form a fluid with greater volatility and the same ammonia density as DEF conforming to ISO 22241. A dosing strategy can then be employed where any combination of DEF or ACCT solution can be used to provide ammonia as a reductant over the whole activity temperature range of a catalyst.

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 paper presents a method to achieve a low order system model of the urea-based SCR catalyst coated filter (SCR-in-DPF or SCRF or SDPF), while preserving a high degree of fidelity. Proper orthogonal decomposition (POD), also known as principal component analysis (PCA), or Karhunen-Loéve decomposition (KLD), is a statistical method which achieves model order reduction by extracting the dominant characteristic modes of the system and devises a low-dimensional approximation on that basis. The motivation for using the POD approach is that the low-order model directly derives from the high-fidelity model (or experimental data) thereby retains the physics of the system. POD, with Galerkin projection, is applied to the 1D + 1D SCR-in-DPF model using ammonia surface coverage and wall temperature as the dominant system states to achieve model order reduction.

Sports Utility Vehicles (SUVs) often have blunt rear end geometries for design and practicality, which is not typically aerodynamic. Drag can be reduced with a number of passive and active methods, which are generally prioritised at zero yaw, which is not entirely representative of the “on road” environment. As such, to combine a visually square geometry (at rest) with optimal drag reductions at non-zero yaw, an adaptive system that applies vertical side edge tapers independently is tested statically. A parametric study has been undertaken in Loughborough University’s Large Wind Tunnel with the ¼ scale Windsor Model. The aerodynamic effect of implementing asymmetric side tapering has been assessed for a range of yaw angles (0°, ±2.5°, ±5° and ±10°) on the force and moment coefficients.

Clutches are commonly utilised in passenger type and off-road heavy-duty vehicles to disconnect the engine from the driveline and other parasitic loads. In off-road heavy-duty vehicles, along with fuel efficiency start-up functionality at extended ambient conditions, such as low temperature and intake absolute pressure are crucial. Off-road vehicle manufacturers can overcome the parasitic loads in these conditions by oversizing the engine. Caterpillar Inc. as the pioneer in off-road technology has developed a novel clutch design to allow for engine downsizing while vehicle’s performance is not affected. The tribological behaviour of the clutch will be crucial to start engagement promptly and reach the maximum clutch capacity in the shortest possible time and smoothest way in terms of dynamics. A multi-body dynamics model of the clutch system is developed in MSC ADAMS. The flywheel is introducing the same speed and torque as the engine (represents the engine input to the clutch).

In the wind tunnel the effect of a wind input on the aerodynamic characteristics of any road vehicle is simulated by yawing the vehicle. This represents a wind input where the wind velocity is constant with height above the ground. In reality the natural wind is a boundary layer flow and is sheared so that the wind velocity will vary with height. A CFD simulation has been conducted to compare the aerodynamic characteristics of a DrivAer model, in fastback and squareback form, subject to a crosswind flow, with and without shear. The yaw simulation has been carried out at a yaw angle of 10° and with one shear flow exponent. It is shown that the car experiences almost identical forces and moments in the two cases when the mass flow in the crosswind over the height of the car is similar. Load distributions are presented for the two cases. The implications for wind averaged drag are discussed.

This paper reports on an investigation into the potential for a thermoelectric generator (TEG) to improve the fuel economy of a mild hybrid vehicle. A simulation model of a parallel hybrid vehicle equipped with a TEG in the exhaust system is presented. This model is made up by three sub-models: a parallel hybrid vehicle model, an exhaust model and a TEG model. The model is based on a quasi-static approach, which runs a fast and simple estimation of the fuel consumption and CO2 emissions. The model is validated against both experimental and published data. Using this model, the annual fuel saving, CO2 reduction and net present value (NPV) of the TEG’s life time fuel saving are all investigated. The model is also used as a flexible tool for analysis of the sensitivity of vehicle fuel consumption to the TEG design parameters. The analysis results give an effective basis for optimization of the TEG design.

Since the first stop-start system introduced in 1983, more and more vehicles have been equipped with this kind of automatic engine control system. Recently, it was found that there is strong correlation between engine resting position and the subsequent engine start time. The utilization of the synchronization time working from a required engine stop position prior the engine start request was shown to reduce start times. Hence the position control of an engine during shutdown becomes more significant. A naturally aspirated engine was modelled using the GT-Suite modelling environment to facilitate the development of position controllers using Simulink ®. The use of respectively the throttle and a belt mounted motor generator to provide a control input was considered. Proportional-Integral-Differential (PID), sliding mode and deadbeat control strategies were each used in this study.

Sports Utility Vehicles (SUVs) typically have a blunt rear end shape (for design and practicality), however this is not beneficial for aerodynamic drag. Drag can be reduced by a number of passive and active methods such as tapering and blowing into the base. In an effort to combine these effects and to reduce the drag of a visually square geometry slots have been introduced in the upper side and roof trailing edges of a squareback geometry, to take air from the freestream and passively injects it into the base of the vehicle to effectively create a tapered body. This investigation has been conducted in the Loughborough University’s Large Wind Tunnel with the ¼ scale generic SUV model. The basic aerodynamic effect of a range of body tapers and straight slots have been assessed for 0° yaw. This includes force and pressure measurements for most configurations.

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.