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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.

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.

Micro-mobility vehicles such as electric scooters and bikes are increasingly used for urban transportation; their designs usually trade off performance and range. Addressing thermal and cooling issues in such vehicles could enhance performance, reliability, life, and range. Limited packaging space within the wheels precludes the use of complex cooling systems that would also increase the cost and complexity of these mass-produced wheel motors. The present study begins by evaluating the external aerodynamics of the scooter to characterise the airflow conditions near the rotating wheel; then, a steady-state conjugate heat transfer model of a commercially available wheel hub motor (500W) is created using commercial computational fluid dynamics (CFD) software, StarCCM+. The CAD model of the motor used for this analysis has an external rotor permanent magnet (PM) brushless DC topology.

The emergence of additive manufacturing (AM) technology has enabled the internal cooling channel layout for high pressure aluminium die casting (HPADC) tools to be designed and modified without topological constraint. Optimisation studies of a full industrial HPADC mould for extending the tool service life has received limited attention due to the high geometrical complexity and the various physics with multi time- and length- scales in addition to the manufacturability limitations. In this work, a new computationally efficient algorithm that employs the adjoint optimisation method has been developed to optimise the coolant channels layout in a complete mould with various 3D printed inserts. The algorithms significantly reduced the computational time and resources by decoupling the fluid flow in the coolant channels from the tool and simulating them separately.

Additive manufacturing (AM) provides significant geometric design freedom for the cooling of high pressure die casting (HPDC) tools. Designing cooling channels that can achieve a uniform temperature throughout the tool-cast interface during the moulding process can limit part warping and sink marks, internal part stresses, and increase tool life. However, the design of the embedded cooling channels requires high computational resources to model the heat transfer process for the cast, mould, and coolant from the moment aluminium is injected into the cavity until the injection for the next cycle. To enable the examination of the effect of various parameters, a simplified 3-D CFD conjugate heat transfer model is introduced by considering the experimental observations. The model decouples the cast part from the mould.

Lithium-ion batteries (LiBs) have been widely used in electrified vehicles, and the battery thermal management (BTM) system is needed to maintain the temperature that is critical to battery performance, safety, and health. Conventionally, three-dimensional battery thermal models are developed at the early stage to guide the design of the BTM system, in which battery thermophysical parameters (radial thermal conductivity, axial thermal conductivity, and specific heat capacity) are required. However, in most literature, those parameters were estimated with greatly different values (up to one order of magnitude). In this paper, an investigation is carried out to evaluate the magnitude of the influence of those parameters on the battery simulation results. The study will determine if accurate measurements of battery thermophysical parameters are necessary.

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 porous medium approach is widely used to represent high-resistance devices, such as catalysts, filters or heat exchangers. Because of its computational efficiency, it is invaluable when flow losses need to be predicted on a system level. One drawback of using the porous medium approach is the loss of detailed information downstream of the device. Correct evaluation of the turbulence downstream affects the calculation of the related properties, e.g. heat and mass transfer. The novel approach proposed in the current study is based on a modified distribution of the resistance across the porous medium, which allows to account for the single jets developing in the small channels, showing an improved prediction of the turbulence at the exit of the device, while keeping the low computational demand of the porous medium approach. The benefits and limitations of the current approach are discussed and presented by comparing the results with different numerical approaches and experiments.

The fast-changing and asymmetrical nature of the cabin environment challenges climate control systems in maintaining occupant comfort. This article examines the relationship between the control that occupants have over the heating, ventilation, and air conditioning (HVAC) system and their perceived comfort within the surrounding thermal environment. Three test cases using automatic control (20°C, 22°C, 24°C) and one in manual mode were evaluated via driving trials under normal road conditions in the United Kingdom during winter. In these trials, car cabin occupants felt more comfortable when using manual control than automatic (Fisher’s test, p = 2.2 × 10−16). Occupants felt neutral thermal sensations at head and foot level when using manual control. At chest level, occupants felt thermally neutral for both automatic and manual controls.

In aftertreatment system design, flow uniformity is of paramount importance as it affects aftertreatment device conversion efficiency and durability. The major trend of downsizing engines using turbochargers means the effect of the turbine residual swirl on the flow needs to be considered. In this paper, this effect has been investigated experimentally and numerically. A swirling flow rig with a moving-block swirl generator was used to generate swirling flow in a sudden expansion diffuser with a wash-coated diesel oxidation catalyst (DOC) downstream. Hot-wire anemometry (HWA) was used to measure the axial and tangential velocities of the swirling flow upstream of the diffuser expansion and the axial velocity downstream the monolith. With no swirl, the flow in the catalyst monolith is highly non-uniform with maximum velocities near the diffuser axis. At high swirl levels, the flow is also highly nonuniform with the highest velocities near the diffuser wall.

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.

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.

The advent of 3D displays offers Human-Machine Interface (HMI) designers and engineers new opportunities to shape the user's experience of information within the vehicle. However, the application of 3D displays to the in-vehicle environment introduces a number of new parameters that must be carefully considered in order to optimise the user experience. In addition, there is potential for 3D displays to increase driver inattention, either through diverting the driver's attention away from the road or by increasing the time taken to assimilate information. Manufacturers must therefore take great care in establishing the ‘do’s and ‘don’t's of 3D interface design for the automotive context, providing a sound basis upon which HMI designers can innovate. This paper describes the approach and findings of a three-part investigation into the use of 3D displays in the instrument cluster of a road car, the overall aim of which was to define the boundaries of the 3D HMI design space.

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved.

A potentially important, but inadequately studied, source of passengers' exposure to pollutants when a road vehicle is stationary, with an idling engine, results from the ingestion of a vehicle's own exhaust into the passenger compartment through the HVAC intake. We developed and applied a method to determine the fraction of a vehicle's exhaust entering the cabin by this route. Further the influence of three parameters: ambient tail-wind speed, vehicle ground clearance and tail pipe angle, is assessed. The study applies Computational Fluid Dynamic (CFD) simulation to the distribution of exhaust gasses around a vehicle motorized with a 2.2 liter Diesel engine. The simulation employs efficient meshing techniques and realistic loading conditions to develop a general knowledge of the distribution of the gasses in order to inform engineering design.

A vehicle on the road encounters an unsteady flow due to turbulence in the natural wind, unsteady wakes of other vehicles and as a result of traversing through the stationary wakes of roadside obstacles. Unsteady effects occurring in the sideglass region of a vehicle are particularly relevant to wind noise. This is a region close to the driver and dominated by separated flow structures from the A-pillar and door mirrors, which are sensitive to unsteadiness in the onset flow. Since the sideglass region is of particular aeroacoustic importance, the paper seeks to determine what impact these unsteady effects have on the sources of aeroacoustic noise as measured inside the passenger compartment, in addition to the flow structures in this region. Data presented were obtained during on-road measurement campaigns using two instrumented vehicles, as well as from aeroacoustic wind tunnel tests.

Statistical Energy Analysis (SEA) is the standard analytical tool for predicting vehicle acoustic and vibration responses at high frequencies. SEA is commonly used to obtain the interior Sound Pressure Level (SPL) due to each individual noise or vibration source and to determine the contribution to the interior noise through each dominant transfer path. This supports cascading vehicle noise and vibration targets and early evaluation of the vehicle design to effectively meet NVH targets with optimized cost and weight. A common misconception is that SEA is only capable of predicting a general average interior SPL for the entire vehicle cabin and that the differences between different locations such as driver's ear, rear passenger's ear, lower interior points, etc., in the vehicle cannot be analytically determined by an SEA model.

At higher speeds aerodynamic noise tends to dominate the overall noise inside the passenger compartment. Large-scale turbulent conditions experienced on the road can generate different noise characteristics from those under steady-state conditions experienced in an acoustic wind tunnel. The objective of this research is to assess the relationship between on-road flow conditions and the sound pressure level in the cabin. This research, covering links between the unsteady airflow around the vehicle and aeroacoustic effects, is a natural progression from previous aerodynamic studies. On-road testing was undertaken using a current production vehicle equipped with a mobile data logging system. Testing was carried out on major roads at typical highway speeds, where wind noise is very significant. Of particular interest are high-yaw conditions, which can lead to a blustering phenomenon.