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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.

Air pollution from internal combustion engines poses a significant apprehension for both global warming and public health on a worldwide scale. The adoption of hybridization and electrification within the vehicular fleet can help to tackle these challenges. This study evaluates a waste heat recovery system for electric power generation, based on the Inverted Brayton Cycle (IBC) coupled with a high-performance gasoline engine. The Mercedes-Benz CLS 350 CGI engine platform was modelled in AVL Boost software and validated against the reference published experimental data. The engine model was then modified to incorporate the IBC to study the performance of the proposed hybrid propulsion system. The IBC power output was calculated at a wide range of engine speed and load, and results showed that up to 18 kW of extra power output can be generated by the IBC system.

Li-ion batteries (LIBs) optimum performance and lifetime depend on temperature, with the commonly suggested operating temperature being in the range of 25 to 40 °C. It's also crucial to keep the temperature difference between battery cells below 5°C. Operation at different temperature ranges can adversely affect or degrade the performance and lifetime of LIBs. A battery thermal management system (BTMS) is essential for keeping the battery temperature within the optimum range. This paper aims to develop and analyze the BTMS for an electric heavy-duty truck. To achieve this aim, battery cells and modules are modelled in ANSYS Fluent software. Validation with experimental results and mesh sensitivity studies are also performed to increase confidence in simulation data. The model is then analyzed for a specific cooling systems to investigate its effect on battery thermal performance during the operation.

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.

The importance of designing and sizing a thermal management system for electric vehicle powertrains cannot be overstated. Traditional approaches often rely on model-based system design using supplier reference component data, which can inadvertently lead to undisclosed errors arising from the interactions between the components and the environment. This paper introduces a novel test facility for battery electric vehicle thermal management technology, which has been designed for neural network virtual sensor and non-linear multi-in multi-out control development. The paper demonstrates how a digital twin of the test bench can used to support the development of such technology. Additionally, this paper presents preliminary results from the test bench revealing insights into the performance and interactions of key components. For instance, there is an observed 30% reduction in the maximum flow rate of the pump integrated into the test bench compared to the specified value.

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.

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.

Reducing exhaust emissions has been a major focus of research for a number of years since internal combustion engines (ICE) contribute to a large number of harmful particles entering the environment. As a way of reducing emissions and helping to tackle climate change, many countries are announcing that they will ban the sale of new ICE vehicles soon. Electrical vehicles (EVs) represent a popular alternative vehicle propulsion system. However, although they produce zero exhaust emissions, there is still concern regarding non-exhaust emission, such as brake dust, which can potentially cause harm to human health and the environment. Despite EVs primarily using regenerative braking, they still require friction brakes as a backup as and when required. Moreover, most EVs continue to use the traditional grey cast iron (GCI) brake rotor, which is heavy and prone to corrosion, potentially exacerbating brake wear emissions.

Electrification is one of the main solutions for the decarbonization of the transport system. It is employed widely by the automotive industry in light- and medium-duty vehicles and recently started to be considered in heavy-duty applications. However, powertrain electrification of heavy-duty vehicles, especially for Class 8 trucks, is very challenging. In this study, the battery-electric powertrain energy and technical performance of a DAF 44 tones truck are compared for two different electric drives. The case study truck is modeled in AVL CRUISE M software and the battery electric powertrain is evaluated for long haul driving cycle. The minimum number of battery packs is determined by defining the lowest energy consumption of the powertrain designed for the proposed drive cycle. Also, a transient analysis is accomplished to investigate the impact of various electric drives on energy consumption and performance of the proposed electric powertrain.

While the shift to vehicle electrification plays a pivotal role in governments’ targets towards carbon neutrality, there exists certain technical challenges that need to be addressed. The motorsport car industry is also affected by this policy with the electric cars being included in the formula SAE and formula E competitions as one of the main categories. Moreover, there is a gap in the literature in energy assessment of the electric powertrain used in Formula SAE (FSAE) and Formula Student (FS) cars. In this paper, a Formula Student electric car powertrain was designed as a case study for energy analysis. The proposed electric powertrain is equipped with a four-wheel drive system. The vehicle was modelled in AVL CRUISE M software using technical and measured lab data as input parameters. Simulations were run in a transient driving cycle for a real circuit layout used in previous SAE competitions.

Small diesel engines are a common primer for micro and mini-grid systems, which can supply affordable electricity to rural and remote areas, especially in developing countries. These diesel generators have no exhaust after-treatment system thus exhaust emissions are high. This paper investigates the potential of introducing simulated synthetic gas (syngas) to diesel in a small diesel engine to explore the opportunities of widening fuel choices and reducing emissions using a 5.7kW single cylinder direct injection diesel generator engine. Three different simulated syngas blends (with varying hydrogen content) were prepared to represent the typical syngas compositions produced from downdraft gasification and were injected into the air inlet. In-cylinder pressure, ignition delay, premixed combustion, combustion stability, specific energy consumption (SEC), and gaseous and particle emissions were measured at various power settings and mixing ratios.

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.

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.

In an era of rapidly increasing vehicle electrification, the gasoline engine remains a vital part of the passenger car powertrain portfolio. Lean-burn combustion is a formidable means for reducing the CO2 emissions of gasoline engines but demands the use of sophisticated emissions control. A 2.0 litre turbocharged direct-injection gasoline engine has been developed with a lean homogeneous combustion system matched to a robust lean and stoichiometric-capable exhaust aftertreatment. The aftertreatment system includes an SCR system and a GPF with filtration down to 10 nm particle size. The engine is equipped with a continuously variable valve-lift system, high-tumble ports and a high-energy ignition system; the boosting system comprises a variable geometry turbocharger and a 48 V electrical supercharger. The work reported formed part of the PaREGEn (Particle Reduced, Efficient Gasoline Engines) project under the Horizon 2020 framework programme.

Non-exhaust and exhaust particles from traffic were evaluated to account for nearly equal proportions in traffic-related emissions. Among non-exhaust emissions, tyre wear has been a crucial contributor to Particulate matter (PM), with its mass contribution as high as 30% to non-exhaust emissions from traffic. As exhaust emissions control regulation becomes stricter, which leads to a substantial reduction in exhaust emissions from road traffic, currently relative contributions of non-exhaust particles generated from tyre wear to PM is becoming more important. Accordingly, possible regulatory requirement and effectively control strategy of tyre wear particles needs to be developed. This review paper covers the physical properties, chemical composition, emission rates, and mathematic model development of tyre wear particles.