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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.

The work examined the practicality of converting a modern production 6 cylinder 7.7 litre heavy-duty diesel engine for flex dual-fuel operation with ammonia as the main fuel. A small amount of diesel fuel (pilot) was used as an ignition source. Ammonia was injected into the intake ports during the intake stroke, while the original direct fuel injection equipment was retained and used for pilot diesel injection. A bespoke engine control unit was used to control the injection of both fuels and all other engine parameters. The aim was to provide a cost-effective retrofitting technology for existing heavy-duty engines, to enable eco-friendly operation with minimal carbon emissions. The tests were carried out at a baseline speed of 600 rpm for the load range of the engine (10-90%), with minimum pilot diesel quantity and as high as 90% substitution ratio of ammonia for diesel fuel.

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.

Ammonia (NH3) is emerging as a potential fuel for longer range decarbonised heavy transport, predominantly due to favourable characteristics as an effective hydrogen carrier. This is despite generally unfavourable combustion and toxicity attributes, restricting end use to applications where robust health and safety protocols can always be upheld. In the currently reported work, a spark ignited thermodynamic single cylinder research engine was upgraded to include gaseous ammonia and hydrogen port injection fueling, with the aim of understanding maximum viable ammonia substitution ratios across the speed-load operating map. The work was conducted under stoichiometric conditions with the spark timing re-optimised for maximum brake torque at all stable logged sites. The experiments included industry standard measurements of combustion, performance and engine-out emissions.

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 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.

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.

One of the main challenges in internal combustion engine design is the simultaneous reduction of all engine pollutants like carbon monoxide (CO), total unburned hydrocarbons (THC), nitrogen oxides (NOx), and soot. Low-temperature combustion (LTC) concepts for compression ignition (CI) engines, e.g., premixed charged compression ignition (PCCI), make use of pre-injections to create a partially homogenous mixture and achieve an emission reduction. However, they present challenges in the combustion control, with the usage of in-cylinder pressure sensors as feedback signal is insufficient to control heat release and pollutant emissions simultaneously. Thus, an additional sensor, such as an ion-current sensor, could provide further information on the combustion process and effectively enable clean and efficient PCCI operation.

Porous wall permeability is one of the most critical factors for the estimation of backpressure, a key performance indicator in automotive particulate filters. Current experimental and analytical filter models could be calibrated to predict the permeability of a specific filter. However, they fail to provide a reliable estimation for the dependence of the permeability on key parameters such as wall porosity and pore size. This study presents a novel methodology for experimentally determining the permeability of filter walls. The results from four substrates with different porosities and pore sizes are compared with several popular permeability estimation methods (experimental and analytical), and their validity for this application is assessed. It is shown that none of the assessed methods predict all permeability trends for all substrates, for cold or hot flow, indicating that other wall properties besides porosity and pore size are important.

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.

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.

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 present work represents the continuation of the introductory study presented in part I [11] where the experimental plan, the measurement system and the tools developed for the testing of a modern Wankel engine were illustrated. In this paper the motored data coming from the subsequent stage of the testing are presented. The AIE 225CS Wankel rotary engine produced by Advanced Innovative Engineering UK, installed in the test cell of the University of Bath and equipped with pressure transducers selected for the particular application, has been preliminarily tested under motored conditions in order to validate the data acquisition software on the real application and the correct determination of the Top Dead Centre (TDC) location which is of foremost importance in the computation of parameters such as the indicated work and the combustion heat release when the engine is tested later under fired conditions.

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.

Permanent magnet (PM) electrical machine has far-reaching impacts in aviation electrification due to the continuous development in high power density and high efficiency electrical drives. The primary barrier to acceptance of permanent magnet machines for safety-critical starter-generator systems is its low fault-tolerance capability and low reliability (for the conventional designs). This article investigates a modular triple three-phase PM starter-generator comprehensively, including the tradeoff of fault-tolerant topology, optimization design process, analysis of electromagnetic (highlight the post-fault analysis) and thermal behavior, respectively. The triple three-phase segmented topology proposed meet the fault-tolerant requirement along with complete electrical, magnetic, and thermal isolation. There would be cost penalty on the proposed topology, but it gets offset by the ease of manufacturing of coils and their insertion.

Electric machines in aerospace applications are subjected to extremely high operating temperatures. This increases coercivity or decreases saturation flux density of the electrical steel resulting in increased core loss. The need for high power density and increased operating speed favours the use of thin gauge Silicon Steel (Si-Fe) and Cobalt Iron (Co-Fe) laminations for aerospace applications. Therefore, the variation in iron loss is studied for three grades of Si-Fe laminations by subjecting them to controlled ageing in laboratory. The analysis is also provided over a range of flux density and frequency to generalize the phenomenon over the operating domain. The results of ageing the laminations are in turn used to predict the degradation in performance of a 1.15 MW, 16-pole 48-slot propulsion machine for aerospace application. The degradation is estimated in terms of variation in iron loss.

Evolving of aircraft design towards further electrification requires safe and fault-free operation of all the components. More electric aircraft are increasingly utilizing electro-mechanical actuators (EMA). EMAs are prone to jamming and subsequent failure due to large forces on the shaft. Large forces are generated due to the high reflected inertia of the electric machine rotor. To limit the force acting on the shaft, a torque limiting device is connected to the power train which can separate the rotating mass of the electric machine from the power train. In this paper, a concept of integration of torque limiter and the electric machine rotor is presented to reduce overall volume and mass. It is connected closely with the rotor, within the motor envelope. A commercially available torque limiter and an electric machine designed for actuator application are used to demonstrate the concept. While essential for safety, the torque limiter adds to the mass and size of the overall EMA.