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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.

Recent legislation banning the sale of new petrol and diesel vehicles in Europe from 2035 has shifted the focus of internal combustion engine research towards alternative fuels with net zero tailpipe emissions such as hydrogen. Research regarding hydrogen as a fuel is particularly pertinent to the so-called ‘hard-to-electrify’ propulsion applications, requiring a combination of large range, fast refuelling times or high-load duty cycles. The virtual design, development, and optimisation of hydrogen internal combustion engines has resulted in the necessity for accurate predictive modelling of the hydrogen combustion and autoignition processes. Typically, the models for these processes rely respectively on laminar flame speed datasets to calculate the rate of fuel burn as well as ignition delay time datasets to estimate autoignition timing. These datasets are generated using chemical kinetic mechanisms available in the literature.

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.

With ever stricter legislative requirements for CO2 and other exhaust emissions, significant efforts by OEMs have launched a number of different technological strategies to meet these challenges such as Battery Electric Vehicles (BEVs). However, a multiple technology approach is needed to deliver a broad portfolio of products as battery costs and supply constraints are considerable concerns hindering mass uptake of BEVs. Therefore, further investment in Internal Combustion (IC) engine technologies to meet these targets are being considered, such as lean burn gasoline technologies alongside other high efficiency concepts such as dedicated hybrid engines. Hence, it becomes of sound reason to further embrace diversity and develop complementary technologies to assist in the transition to the next generation hybrid powertrain. One such approach is to provide increased valvetrain flexibility to afford new degrees of freedom in engine operating strategies.

We present a new method to predict the power losses in electric vehicle (EV) transmission systems using a thermally coupled gearbox efficiency model. Friction losses in gear teeth contacts are predicted using an iterative procedure to account for the thermal coupling between the tooth temperature, oil viscosity, film thickness, friction, and oil rheology during a gear mesh cycle. Crucially, the prediction of the evolution of the coefficient of friction (COF) along the path of contact incorporates measured lubricant rheological parameters as well as measured boundary friction. This allows the model to differentiate between nominally similar lubricants in terms of their impact on EV transmission efficiency. Bearing and gear churning losses are predicted using existing empirical relationships. The effects of EV motor cooling and heat transfers in the heat exchanger on oil temperature are considered.

The paper presents results from a study into the potential of a complex cycle gas turbine engine, originally investigated by the Ford Motor Company for truck applications in the 1960s, and updated to gauge the possible improvements by raising the efficiencies of its constituent components from the values used in period to more modern levels. To perform this investigation, firstly a spreadsheet model was constructed and the data that Ford made available in the open literature were used to validate it. The methodology used in the model was to balance the power consumed by the compressors (and the auxiliaries where applicable) with that produced by their driving turbines, and to match the thermal power in the heat exchangers with the data provided. Using the quoted lower heating value of the diesel fuel originally used, this approach led to an accuracy in the match of brake specific fuel consumption (in terms of g/kWh) to three places of decimals.

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 present work investigates a means of controlling engine hydrocarbon startup and shutdown emissions in a Wankel engine which uses a novel rotor cooling method. Mechanically the engine employs a self-pressurizing air-cooled rotor system (SPARCS) configured to provide improved cooling versus a simple air-cooled rotor arrangement. The novelty of the SPARCS system is that it uses the fact that blowby past the sealing grid is inevitable in a Wankel engine as a means of increasing the density of the medium used for cooling the rotor. Unfortunately, the design also means that when the engine is shutdown, due to the overpressure within the engine core and the fact that fuel vapour and lubricating oil are to be found within it, unburned hydrocarbons can leak into the combustion chambers, and thence to the atmosphere via either or both of the intake and exhaust ports.

In a previous study it was shown that a production vehicle employing a Wankel rotary engine, the Mazda RX-8, was easily capable of meeting much more modern hydrocarbon emissions than it had been certified for. It was contended that this was mainly due to its provision of zero port overlap through its adoption of side intake and exhaust ports. In that earlier work a preliminary investigation was conducted to gauge the impact of adopting a zero overlap approach in a peripherally-ported Wankel engine, with a significant reduction in performance and fuel economy being found. The present work builds on those initial studies by taking the engine from the vehicle and testing it on an engine dynamometer. The results show that the best fuel consumption of the engine is entirely in line with that of several proposed dedicated range extender engines, supporting the contention that the Wankel engine is an excellent candidate for that role.

Eliminating toxic exhaust emissions, amongst them particulate matter (PM), is one of the driving factors behind the increasing use of electrified vehicles. However, it is frequently overseen that PM arise not only from combustion, but from non-exhaust traffic related causes as well; in particular from the vehicle brakes, tires and the road surface. Furthermore, as electrified vehicles weigh more and typically exhibit higher torques at low speeds, their non-exhaust emissions tend to be higher than for comparable conventional vehicles, especially those generated by tires. Fortunately, tire related emissions are directly related to tire wear, so that limiting tire wear can reduce these emissions as well. This can be accomplished by intelligently modulating the vehicle torque profile in real time, to limit the operation in conditions of higher tire wear.

The road freight transport sector is one of the main responsible for the air pollution (as the case of particulate matter) and greenhouse gases emissions worldwide. Different types of fuel technologies have been developed in order to improve efficiency, reduce air pollution impacts, such as the case of liquefied natural gas (LNG) for heavy-duty vehicles. Many studies show the relationship between the effects of short and long-term exposure to particulate matter (PM) and, according to the World Health Organization (WHO), premature deaths worldwide as well as cardiorespiratory diseases in elderly population are related to this pollutant. In this context, this paper aims at evaluating the atmospheric dispersion of PM in a stretch of a highway (Anhanguera-Bandeirantes) in the São Paulo State in Brazil due to the road freight transport considering the use of diesel and LNG in heavy-duty vehicles and the impacts on human health. The software AERMOD designed by U.S.

Nowadays, proton exchange membrane (PEM) fuel cell is widely seen as a promising energy conversion device especially for transportation application scenario because of its high efficiency, low operation temperature and nearly-zero road emission. Extensive modeling work have been done based on different dimensions during the past decades, including one-dimensional (1D), two-dimensional (2D), three-dimensional (3D) and intermediate combinations in between (e.g. “1+1D”). 1D model benefits from a rationally-chosen set of assumptions to obtain excellent calculation efficiency, yet at the cost of accuracy to some extent. In contrast, 3D model has great advantage over 1D model on acquiring more comprehensive information inside the fuel cell. For macro-scale modeling work, one compromise aiming to realize both acceptable computation speed and reasonable reflection of cell operation state is to simplify the membrane electrode assembly (MEA).

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.

The use of Wankel rotary engines as a range extender has been recognised as an appealing method to enhance the performance of Hybrid Electric Vehicles (HEV). They are effective alternatives to conventional reciprocating piston engines due to their considerable merits such as lightness, compactness, and higher power-to-weight ratio. However, further improvements on Wankel engines in terms of fuel economy and emissions are still needed. The objective of this work is to investigate the engine modelling methodology that is particularly suitable for the theoretical studies on Wankel engine dynamics and new control development. In this paper, control-oriented models are developed for a 225CS Wankel rotary engine produced by Advanced Innovative Engineering (AIE) UK Ltd. Through a synthesis approach that involves State Space (SS) principles and the artificial Neural Networks (NN), the Wankel engine models are derived by leveraging both first-principle knowledge and engine test data.

Following market demands for a niche balance between engine performance and legislation requirement, a new-concept compressor scroll has been designed for small to medium size passenger cars. The design adopts a slight deviation from the conventional method, thus resulting in broader surge margin and better efficiency at off-design region. This paper presents the performance evaluation of the new compressor scroll on the cold-flow gas-stand followed by the on-engine testing. The testing program focused on back-to-back comparison with the standard compressor scroll, as well as identifying on-engine operational regime with better brake specific fuel consumption (BSFC) and transient performance. A specially instrumented 1.6L gasoline engine was used for this study. The engine control unit configuration is kept constant in both the compressor testing.

In use cars often drive through the wakes of other vehicles. It has long been appreciated that this imposes a fluctuating onset flow which can excite a structural response in vehicle panels, particularly the bonnet. This structure must be designed to be robust to such excitation to guarantee structural integrity and maintain customer expectations of quality. As we move towards autonomous vehicles and exploit platoons for drag reduction, this onset flow condition merits further attention. The work reported here comprises both measurements and simulation capturing the unsteady pressure distribution over the bonnet of an SUV following a similar vehicle at high speed and in relatively close proximity. Measurements were taken during track testing and include 48 static measurement locations distributed over the bonnet where the unsteady static pressures were recorded.

Future CO2 emission legislation requires the internal combustion engine to become more efficient than ever. Of great importance is the boosting system enabling down-sizing and down-speeding. However, the thermodynamic coupling of a reciprocating internal combustion engine and a turbocharger poses a great challenge to the turbine as pulsating admission conditions are imposed onto the turbocharger turbine. This paper presents a novel approach to a turbocharger turbine development process and outlines this process using the example of an asymmetric twin scroll turbocharger applied to a heavy duty truck engine application. In a first step, relevant operating points are defined taking into account fuel consumption on reference routes for the target application. These operation points are transferred into transient boundary conditions imposed on the turbine.

The Wankel rotary engine historically found limited success in automotive applications due in part to poor combustion efficiency and challenges around emissions. This is despite its significant advantages in terms of power density, compactness, vibrationless operation, and reduced parts count in relation to the 4-stroke reciprocating engine, which is now-dominant in the automotive market. A large part of the reason for the poor fuel economy and high hydrocarbon emissions of the Wankel engine is that there is a very significant amount of overlap when the ports are opened and/or closed by the rotor apices (so-called peripheral ports). This paper investigates the benefits of zero overlap from a production engine with this characteristic and the effect of configuring a peripherally-ported Wankel engine in such a manner.