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With the development of internet technology and autonomous vehicles (AVs), the multimodal transportation and distribution model based on AVs will be a typical application paradigm in the smart city scenario. Before AVs carry out logistics distribution, it is necessary to plan a reasonable distribution path based on each customer point, and this is also known as Vehicle Routing Problem (VRP). Unlike traditional VRP, the urban logistics distribution process based on multimodal transportation mode will use a set of different types of AVs, mainly including autonomous ground vehicles and unmanned aerial vehicles (UAVs). It is worth pointing out that there is currently no research on combining the planning of AVs distribution paths with the trajectory planning of UAVs. To address this issue, this article establishes a bilevel programming model. The upper-level model aims to plan the optimal delivery plan for AVs, while the lower-level model aims to plan a driving trajectory for UAVs.

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.

The design of passive suspension systems is being improved since the early days of the automotive industry in order to obtain the best tradeoff between ride comfort and handling. In this context, passenger cars tend to prioritise ride comfort whilst racing cars tend to focus on handling. On the other hand, Formula SAE is a series of undergraduate competitions in which the students design, build and compete with small, formula-style, mono-seated vehicles. As part of the competition events, the vehicle experiences tight corners and short-length slaloms. The minimum turning diameter and the shortest length of slalom period conducted by Formula SAE prototypes are 9 m and 7.6 m, respectively. Therefore, high controllability of vehicle dynamic behaviour is required in order to enhance the cornering speed, this is achievable by working on the dampers to optimise the rates of load transfer in cornering.

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.

An increase in data measurement and recording within vehicles has allowed Anti-lock Braking Systems (ABS) to monitor a vehicle’s dynamic behavior in far more detail. This increased monitoring helps to improve vehicle response in scenarios such as braking whilst cornering and braking on uneven surfaces. The Durability and Robustness (D&R) CAE department within Jaguar Land Rover discovered that the lack of a complex ABS system in virtual vehicle models was contributing to poor lateral and longitudinal loads correlation throughout the suspension and mounting systems. D&R CAE started a project to incorporate Continental’s ABS system, provided by ‘©Continental AG’ for physical JLR vehicles, into SIMPACK virtual vehicles by means of a co-simulation (2017 n.d.). The work involved collaboration between 3 departments in Jaguar Land Rover and ultimately led to implementation of the ABS into the JLR standard automotive virtual database.

Most ground vehicles related accidents occur when the friction demand to perform a maneuver with a certain vehicle and tires exceeds the coefficient of friction of the pavement surface. As generally known, the forces and moments acting on the vehicle body are mainly generated at the tire-road surface interface. The common characteristics of tire forces on any surface include a linear region where the forces vary linearly with respect to the relative slip values; and a nonlinear region where the forces saturate and may even start decreasing. The experience of most of the daily drivers on the roads is limited within this linear region where the dynamic behavior of the vehicle remains proportional to the driver’s inputs. Therefore, an unexpected change in tire or surface characteristics (due to a change in surface friction, large driver inputs, etc.) may easily cause the driver to panic and/or to lose his/her ability to maintain a stable vehicle.

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.

A novel semi-analytical solution has been developed for the calculation of the static and dynamic response of an off road tire interacting with a deformable terrain, which utilizes soil parameters independent of the size of the contact patch (size-independent). The models involved in the solution presented, can be categorized in rigid and/or pneumatic tires, with or without tread pattern. After a concise literature review of related methods, a detailed presentation of the semi-analytical solution is presented, along with assumptions and limitations. A flowchart is provided, showing the main steps of the numerical implementation, and various test cases have been examined, characterized in terms of vertical load, tire dimensions, soil properties, deformability of the tire, and tread pattern. It has been found that the proposed model can qualitatively capture the response of a rolling wheel on deformable terrain.

Brake disc materials are being utilised that have low noise/dust properties, but are sensitive to contamination by surface water. This drives large dust shields, making brake cooling increasingly difficult. However, brake cooling must be delivered without compromising aerodynamic drag and hence CO2 emissions targets. Given that front brake discs sit in a region of geometric, packaging and flow complexity optimization of their performance requires the analysis of thermal, aerodynamic and multi-phase flows. Some of the difficulties inherent in this task would be alleviated if the complete analysis could be performed in the same CAE environment: utilizing common models and the same solver technology. Hence the project described in this paper has sought to develop a CFD method that predicts the amount of contamination (water) that reaches the front brake discs, using a standard commercial code already exploited for both brake disc thermal and aerodynamics analysis.

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.