Search Results

The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

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.

Engine manufacturers are increasingly concerned about oil consumption due to its implications for operating costs, emissions, and durability in both diesel and natural gas-powered engines. As future engines aim for low or near-zero emissions while utilizing low/zero carbon fuels, lubricant oil consumption will play a critical role in achieving decarbonization and emissions targets. Hydrogen-fuelled engines, in particular, will be more vulnerable to oil droplet and oil ash-based pre-ignition. Traditionally, the influence of key design parameters on oil consumption has been determined during the validation phase of an engine development program, which entails extensive testbed hours and time-consuming hardware iterations. As a result, development programs may be unable to optimize oil consumption due to cost and time constraints.

For modern vehicle development, on-board vehicle Mass and road Gradient Estimation (MGE) can offer great benefit to many sub-systems on the vehicle, such as vehicle control system, transmission control system, and active safety system etc. However, there are still several challenges that need to be solved. Firstly, thanks to good accuracy, reliability, and robustness, regression analysis-based approaches: Recursive Least Squares (RLS) and Kalman Filter (KF) are very popular for MGE, but the trade-off between estimator’s accuracy and converge time is challenging. Furthermore, depending on vehicle and powertrain types, the implementation of MGE function could be very different. It is desired to have a structured approach for various vehicle applications’ MGE development. Lastly, good reliability of MGE does not always satisfy for complicated real-world driving maneuvers and road conditions.

The proposed Euro 7 emission standard foresees that the emission behaviour of Euro 7 vehicles is monitored via an on-board monitoring (OBM) system. In Euro 7 vehicles, OBM systems will monitor the emissions of nitrogen oxides (NOX), ammonia (NH3) and particulate matter (PM) for every trip through a combination of measured and modelled data. Sensors employed to support on-board diagnostics (OBD) in current vehicles may be used to support OBM. According to the Euro 7 OBM concept presented in this paper, OBM will serve a dual purpose: the first is to warn the user of a vehicle about the need to perform repairs on the engine or the pollution control systems when these are needed. If these repairs are not performed in a timely manner, the OBM system will be able to ultimately prevent engine restart, akin to the existing low-reagent driver warning system in some compression ignition vehicles. The second purpose of OBM is to monitor the compliance of vehicle types with the emission limits.

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.

In order to align with net-zero CO2 ambitions, automotive OEMs have been developing increasingly sophisticated strategies to minimise the impact that combustion engines have on the environment. Intelligent thermal management systems to actively control coolant flow around the engine have a positive impact on friction generated in the power cylinder by improving the warmup rate of cylinder liners and heads. This increase in temperature results in an improved frictional performance and cycle averaged fuel consumption, but also increases the piston to liner clearances due to rapid warm up of the upper part of the cylinder head. These increased clearances can introduce piston slap noise and substantially degrade the NVH quality to unacceptable levels, particularly during warmup after soak at low ambient temperatures. Using analytical techniques, it is possible to model the thermo-structural and NVH response of the power cylinder with different warm up 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 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.

Objective of this project was to establish very comprehensive, robust vehicle model of three wheeler electric vehicle. This was achieved by simulating vehicle operation and performance on standard as well as realistic driving cycles. Purpose of building a virtual electric three wheeler vehicle model was to validate the mathematical model with respect to real time road load testing. Electric 3 wheeler vehicle model was built in Ricardo-IGNITE. This model includes advanced vehicle which consists of detailed vehicle parameters like mass & CG locations, suspension, brakes and aerodynamic input parameters. Also other parameter includes road load resistances, rotational, transnational inertial effects and power-train efficiencies and detailed battery and motor model with its thermal & inertial properties respectively. Simulation results of performance and range are validated by actual vehicle testing on dynamo-meter and road for standard and realistic driving-cycles respectively.

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.

The flows in-cylinder have a profound effect on the mixture preparation and subsequent combustion in all engines. These flows are highly three-dimensional in nature and information from multiple planes is required to characterise the flow dynamics. The flow measurements reported here are from three orthogonal planes in an optical access engine that is based on the Jaguar Land Rover AJ200 Gasoline Direct Injection (GDI) engine. Particle Image Velocimetry (PIV) measurements have been taken every 5°CA from the start of induction to the end of compression. Data have been obtained from 300 cycles for separate experiments measuring flows in the tumble plane, the swirl plane and the cross-tumble plane. Vector comparison metrics are used to quantitatively compare ensemble averaged PIV flow fields to Computational Fluid Dynamics (CFD) simulations across each plane in terms of both the velocity magnitude and direction.

Many modern vehicles have blunt rear end geometries for design aesthetics and practicality; however, such vehicles are potentially high drag. The application of tapering; typically applied to an entire edge of the base of the geometry is widely reported as a means of reducing drag, but in many cases, this is not practical on real vehicles. In this study side tapers are applied to only part of the side edge of a simplified automotive geometry, to show the effects of practical implementations of tapers. The paper reports on a parametric study undertaken in Loughborough University’s Large Wind Tunnel with the ¼ scale Windsor model equipped with wheels. The aerodynamic effect of implementing partial side edge tapers is assessed from a full height taper to a 25% taper in both an upper and lower body configuration. These were investigated using force and moment coefficients, pressure measurements and planar particle image velocimetry (PIV).

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.

Predicting Exterior Water Management is important for developing vehicles that meet customer expectations in adverse weather. Fluid film methods, with Lagrangian tracking, can provide spray and surface water simulations for complex vehicle geometries in on-road conditions. To cope with this complexity and provide practical engineering simulations, such methods rely on empirical sub-models to predict phenomena such as the film stripping from the surface. Experimental data to develop and validate such models is difficult to obtain therefore here a high-fidelity Coupled Level-set Volume of Fluid (CLSVOF) simulation is carried out. CLSVOF resolves the interface of the liquid in three dimensions; allowing direct simulation of film behaviour and interaction with the surrounding air. This is used to simulate a simplified wing-mirror, with air flow, on which water is introduced.

The field of vision of the driver during wet road conditions is essential for safety at all times. Additionally, the safe use of the increasing number of sensors integrated in modern cars for autonomous driving and intelligent driver assistant systems has to be ensured even under challenging weather conditions. To fulfil these requirements during the development process of new cars, experimental and numerical investigations of vehicle soiling are performed. This paper presents the surface contamination of self- and foreign-soiling tested in the wind tunnel. For these type of tests, the fluorescence method is state-of-the-art and widely used for visualizing critical areas. In the last years, the importance of parameters like the contact angle have been identified when designing the experimental setup. In addition, new visualization techniques have been introduced.

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 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.