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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.

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.

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.

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.

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.

Whilst the primary function of the active grille shutters is to reduce the aerodynamic drag of the car, there are some secondary benefits like improving the warm up time of engine and also retaining engine heat when parked. In turbocharged IC engines the air is compressed (heated) in the turbo and then cooled by a low temperature cooling system before going into the engine. When the air intake temperature exceeds a threshold value, the engine efficiency falls - this drives the need for the cooling airflow across the radiator in normal operation. Airflow is also required to manage the convective heat transfer across various components in the engine bay for its lifetime thermal durability. Grill shutters can also influence the aerodynamic lift balance thus impacting the vehicle dynamics at high speed. The vehicle HVAC system also relies on the condenser in the front heat exchanger pack disposing the waste heat off in the most efficient way.

This study presents a one-dimensional model for the prediction of the pressure loss across a wall-flow gasoline particulate filter (GPF). The model is an extension of the earlier models of Bissett [1] and Konstandopoulos and Johnson [2] to the turbulent flow regime, which may occur at high flow rates and temperatures characteristic of gasoline engine exhaust. A strength of the proposed model is that only one parameter (wall permeability) needs to be calibrated. An experimental study of flow losses for cold and hot flow is presented, and a good agreement is demonstrated. Unlike zero-dimensional models, this model provides information about the flow along the channels and thus can be extended for studies of soot and ash accumulation, heat transfer and reaction kinetics.

The ability to assess noise transmitted through seals to cabin interiors early in the design process is very important for automotive manufacturers. When a seal design is inadequate, the noise transmitted can dominate the interior noise, making the wind noise performance of the vehicle unacceptable. This can cause launch delays, increasing costs and risking loss of sales. Designing seals using conventional experimental processes is challenging, since the location and strength of flow noise sources are not known when the seal design is planned. Making changes to the seal system after the tooling stage is expensive for manufacturers as tooling and redesign costs can be considerable. Deliberate overdesign by adding multiple layers of seals in a wide range of locations also can reduce profit by unnecessarily raising part and manufacturing costs.

The HyPACE (Hybrid Petrol Advanced Combustion Engine) project is a part UK government funded research project established to develop a high thermal efficiency petrol engine that is optimized for hybrid vehicle applications. The project combines the capabilities of a number of partners (Jaguar Land Rover, BorgWarner, MAHLE Powertrain, Johnson Matthey, Cambustion and Oxford University) with the target of achieving a 10% vehicle fuel consumption reduction, whilst still achieving a 90 to 100 kW/liter power rating through the novel application of a combination of new technologies. The baseline engine for the project was Jaguar Land Rover’s new Ingenium 4-cylinder petrol engine which includes an advanced continuously variable intake valve actuation mechanism. A concept study has been undertaken and detailed combustion Computational Fluid Dynamics (CFD) models have been developed to enable the optimization of the combustion system layout of the engine.

Under bonnet thermal encapsulation is a method for retaining the heat generated by a running powertrain after it is turned off. By retaining the heat in the engine bay, the powertrain will be closer to its operating temperatures the next time it is started, reducing the warm up time required. This reduces the period of inefficiency due to high friction losses before the engine reaches it operating temperature, and as a result reduces the vehicles fuel consumption and CO2 emissions. To develop an integrated and efficient encapsulation design, CAE methods can be applied to allow this work stream to start as early in a vehicles development cycle as possible. In this work, the existing test methods are discussed, and a new Thermal CFD method is presented that accurately simulates the fluid temperatures after a customer representative 9 hour park period.

Diesel engine designers often use swirl flaps to increase air motion in cylinder at low engine speeds, where lower piston velocities reduce natural in-cylinder swirl. Such in-cylinder motion reduces smoke and CO emissions by improved fuel-air mixing. However, swirl flaps, acting like a throttle on a gasoline engine, create an additional pressure drop in the inlet manifold and thereby increase pumping work and fuel consumption. In addition, by increasing the fuel-air mixing in cylinder the combustion duration is shortened and the combustion temperature is increased; this has the effect of increasing NOx emissions. Typically, EGR rates are correspondingly increased to mitigate this effect. Late inlet valve closure, which reduces an engine’s effective compression ratio, has been shown to provide an alternative method of reducing NOx emissions.

Modern diesel cars, fitted with state-of-the-art aftertreatment systems, have the capability to emit extremely low levels of pollutant species at the tailpipe. However, diesel aftertreatment systems can represent a significant cost, packaging and maintenance requirement. Reducing engine-out emissions in order to reduce the scale of the aftertreatment system is therefore a high priority research topic. Engine-out emissions from diesel engines are, to a significant degree, dependent on the detail of fuel/air interactions that occur in-cylinder, both during the injection and combustion events and also due to the induced air motion in and around the bowl prior to injection. In this paper the effect of two different piston bowl shapes are investigated.

In order to specify a brake system that will have robust performance over the entire range of expected vehicle drive cycles it is vital that it has sufficient thermal inertia and dissipation to ensure that component temperatures are kept within acceptable limits. This paper presents a high fidelity CAE (computer aided engineering) technique for predicting the temperature of the front brake and the surrounding suspension components whilst installed on vehicle. To define the boundary conditions the process utilizes a coupled unsteady CFD (computational fluid dynamics) and thermal solver to accurately predict the convective heat transfer coefficients across a range of vehicle speeds. A 1-D model is used to predict the brake energy inputs as well as the vehicle speed-time curves during the drive cycle based on key vehicle parameters including wide-open-throttle performance, drive train losses, rolling resistance, aerodynamic drag etc.

The causes of engine knock are well understood but it is important to be able to relate these causes to the effects of controllable engine parameters. This study attempts to quantify the effects of a portion of the available engine parameters on the knock behavior of a 60% downsized, DISI engine running at approximately 23 bar BMEP. The engines response to three levels of coolant flow rate, coolant temperature and exhaust back pressure were investigated independently. Within the tested ranges, very little change in the knock limited spark advance (KLSA) was observed. The effects of valve timing on scavenge flow and blow through (the flow of fresh air straight into the exhaust system during the valve overlap period) were investigated at two conditions; at fixed inlet/exhaust manifold pressures, and at fixed engine torque. For both conditions, a matrix of 8 intake/exhaust cam combinations was tested, resulting in a wide range of valve overlap conditions (from 37 to -53°CA).

Simulations are presented which fully couple both the aerodynamics and cooling flow for a model of a fully engineered production saloon car (Jaguar XJ) with a two-tier cooling pack. This allows for the investigation of the overall aerodynamic impact of the under-hood cooling flow, which is difficult to predict experimentally. The simulations use a 100 million-element mesh, surface wrapped and solved to convergence using a commercially available RANS solver (STARCCM+). The methodology employs representative boundary conditions, such as rotating wheels and a moving ground plane. A review is provided of the effect of cooling flows on the vehicle aerodynamics, compared to published data, which suggest cooling flow accounts for 26 drag counts (0.026 Cd). Further, a sensitivity analysis of the pressure drop curves used in the porous media model of the heat exchangers is made, allowing for an initial understanding of the effect on the overall aerodynamics.

The power demand of air conditioning in PHEVs is known to have a significant impact on the vehicle’s fuel economy and performance. Besides the cooling power associated to the passenger cabin, in many PHEVs, the air conditioning system provides power to cool the high voltage battery. Calculating the cooling power demands of the cabin and battery and their impact on the vehicle performance can help with developing optimum system design and energy management strategies. In this paper, a representative vehicle model is used to calculate these cooling requirements over a 24-hour duty cycle. A number of pre-cooling and after-run cooling strategies are studied and effect of each strategy on the performance of the vehicle including, energy efficiency, battery degradation and passenger thermal comfort are calculated. Results show that after-run cooling of the battery should be considered as it can lead to significant reductions in battery degradation.