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With the electrification of the automotive industry, electric motors have emerged as pivotal components. A profound understanding of their vibrational behaviour stands as a cornerstone for guaranteeing not only the optimal performance and reliability of vehicles in terms of noise, vibration, and harshness (NVH), but also the overall driving experience. The use of conventional finite element analysis (FEA) techniques for identification of the natural frequencies characteristics of electric motors often imposes significant computational loads, particularly when accurate material and geometrical properties and wider frequency ranges are considered. On the other hand, traditional reduced order vibroacoustic methodologies utilising simplified 2D representations, introduce several assumptions regarding boundary conditions and properties, leading to sacrifices in the accuracy of the results.

Electric vehicles offer cleaner transportation with lower emissions, thus their increased popularity. Although, electric powertrains contribute to quieter vehicles, the shift from internal combustion engines to electric powertrains presents new Noise, Vibration, and Harshness challenges. Unlike traditional engines, electric powertrains produce distinctive tonal noise, notably from motor whistles and gear whine. These tonal components have frequency content, sometimes above 10 kHz. Furthermore, the housing of the powertrain is the interface between the excitation from the driveline via the bearings and the radiated noise (NVH). Acoustic features of the radiated noise can be predicted by utilising the transmitted forces from the bearings. Due to tonal components at higher frequencies and dense modal content, full flexible multibody dynamics simulations are computationally expensive.

It is recognized that the heavier vehicles, the more emissions, thus the more imperative to electrify. In this study, long haul heavy-duty trucks are referred as HDTs, which are recognized as one of the hard-to-electrify vehicle segments, though the automotive industry has gained trending advantages of electrifying both light-duty cars and SUVs. Since big rigs such as Class 8 HDTs have significant road-block challenges for electrification due to the demanding long-hour work cycles in all weathers, this study focuses on quantifying those electrification challenges by taking advantage of the public data of Class 8 tractors & trailers. Tesla Semi is the research target though its vehicle spec data is sorted out with fragmentary information in the public domain. The key task is to analyze the battery capacity requirements due to environmental temperature and inherent aging over the lifespan.

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 primary function of automotive windscreen wipers is to remove excess water and debris to secure a clear view for the driver. Their successful operation is imperative to vehicle occupants’ safety. To avoid reliance on experimental testing there is a need to develop physics-based models that can quantify the effects of design-based decisions on automotive wipers. This work presents a suite of evaluative tools that can provide quantitative data on the effects of design decisions. We analyse the complex non-linear contact interaction between the wiper blade and the automotive screen using finite element analysis, assessing the impact of blade geometry on the contact distribution. The influence of the evolution of normal applied load by the wiper arm is also investigated as to how it impacts the contact distribution evolution. The dynamics of the blade are subsequently analysed using a multiple connected mass spring damper system.

As lithium-ion electric vehicle (EV) batteries are sensitive to the conditions they are exposed to during charging and discharging, operational control has been an important research area. While an understanding of the effects current load and operation temperature has on the ageing stability of a battery has been established, associated control strategies are yet to be fully optimized. Most battery charging studies utilize controlled ambient temperatures and basic defined cycles, which may only apply to a small subset of real-world EV consumers. This leads to control strategies that do not consider electrical grid price fluctuation, user driving habits or local weather conditions. This paper looks to propose improved smart charging strategies of EVs to reduce consumer costs while also increasing the battery longevity. To accomplish the primary objective, A model has been generated that simulates the standard charge cycle of a battery.

Enhanced protection against high speed crashes requires more aggressive passive safety countermeasures as compared to what are provided in vehicle structures today. Apart from such collision-related scenarios, high energy explosions, accidentally caused or otherwise, require superior energy-absorbing capability of vehicle body subsystems. A case in point is a passenger vehicle subjected to an underbody blast emanating shock wave energy of military standards. In the current study, assessment of the behavior of a “hollow” countermeasure in the form of a depressed steel false floor panel attached with spot-welds along flanges to a typical predominantly flat floor panel of a car is initially carried out with an explicit LS-DYNA solver. This is followed up with the evaluation of PU (polyurethane) foam-filled and liquid-filled false floor countermeasures. In all cases, a charge is detonated under the false floor subjecting it to a high-energy shock pressure loading.

Advanced driver assistance systems rely on external sensors that encompass the vehicle. The reliability of such systems can be compromised by adverse weather, with performance hindered by both direct impingement on sensors and spray suspended between the vehicle and potential obstacles. The transportation of road spray is known to be an unsteady phenomenon, driven by the turbulent structures that characterise automotive flow fields. Further understanding of this unsteadiness is a key aspect in the development of robust sensor implementations. This paper outlines an experimental method used to analyse the spray ejected by an automotive body, presented through a study of a simplified vehicle model with interchangeable rear-end geometries. Particles are illuminated by laser light sheets as they pass through measurement planes downstream of the vehicle, facilitating imaging of the instantaneous structure of the spray.

Over the past decade there has been significant development in Automated Driving (AD) with continuous evolution towards higher levels of automation. Higher levels of autonomy increase the vehicle Dynamic Driving Task (DDT) responsibility under certain predefined Operational Design Domains (in SAE level 3, 4) to unlimited ODD (in SAE level 5). The AD system should not only be sophisticated enough to be operable at any given condition but also be reliable and safe. Hence, there is a need for Automated Vehicles (AV) to undergo extensive open road testing to traverse a wide variety of roadway features and challenging real-world scenarios. There is a serious need for accurate Ground Truth (GT) to locate the various roadway features which helps in evaluating the perception performance of the AV at any given condition. The results from open road testing provide a feedback loop to achieve a mature AD system.

Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form.

GTDI engines are becoming more efficient, whether individually or part of a HEV (Hybrid Electric Vehicle) powertrain. For the latter, this efficiency manifests itself as increase in zero emissions vehicle mileage. An ideal device for energy recovery is a turbogenerator (TG), and, when placed downstream the conventional turbine, it has minimal impact on catalyst light-off and can be used as a bolt-on aftermarket device. A Ricardo WAVE model of a representative GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated using steady state mapping data. This was integrated into a co-simulation environment with a SISO (Single-Input, Single-Output) dynamic controller developed in SIMULINK for the actuator control (with BMEP, manifold air pressure and TG pressure ratio as the controlled variables).

As the number of electric vehicles (EVs) within society rapidly increase, the concept of maximizing its efficiency within the electric smart grid becomes crucial. This research presents the impacts of integrating EV charging infrastructures within a smart grid through a vehicle to grid (V2G) program. It also observes the circulation of electric charge within the system so that the electric grid does not become exhausted during peak hours. This paper will cover several different case studies and will analyze the best and worst scenarios for the power losses and voltage profiles in the power distribution system. Specifically, we seek to find the optimal location as well as the ideal number of EVs in the distribution system while minimizing its power losses and optimizing its voltage profile. Verification of the results are primarily conducted using GUIs created on MATLAB.

Wet Clutches are used in automotive powertrains to enable compact designs and efficient gear shifting. During the slip phase of engagement, significant flash temperatures arise at the friction disc to separator interface because of dissipative frictional losses. An important aspect of the design process is to ensure the interface temperature does not exceed the material temperature threshold at which accelerated wear behavior and/or thermal degradation occurs. During the early stages of a design process, it is advantageous to evaluate numerous system and component design iterations exposed to plethora of possible drive cycles. A simulation tool is needed which can determine the critical operational conditions the system must survive for performance and durability to be assured. This paper describes a time-efficient multiphysics model developed to predict clutch disc temperatures with a runtime in the order of minutes.

Considerations of surface contamination and airborne spray are becoming increasingly significant throughout the automotive design process. Advanced driver assistance systems, such as autonomous cruise control, are growing in popularity. These systems rely on external sensors, the performance of which may be impaired by both direct obstruction and spray. Existing experimental methods of assessing front-end surface contamination and wiper performance have typically utilised fixed spray-grids positioned upstream of the vehicle. The resulting spray is largely steady in nature, in contrast to the unsteady flow-field and tyre spray that would be produced by preceding vehicles. This paper presents the numerical analysis of the spray ejected downstream of a simplified automotive body. The continuous phase (air) is solved using a DDES-based approach coupled with a Lagrangian representation of the dispersed phase (water).

Adhesive bonding provides a versatile strategy for joining metallic as well as non-metallic substrates, and also offers the functionality for joining dissimilar materials. In the design of unibody vehicles for NVH (Noise, Vibration and Harshness) performance, adhesive bonding of sheet metal parts along flanges can provide enhanced stiffening of body-in-white (BIW) leading to superior vibration resistance at low frequencies and improved acoustics due to sealing of openings between flanges. However, due to the brittle nature of adhesives, they remain susceptible to failure under impact loading conditions. The viability of structural adhesives as a sole or predominant mode of joining stamped sheet metal panels into closed hollow sections such as hat-sections thus remains suspect and requires further investigation.

Today’s advanced vehicles have high degree of interaction due to numerous sensors, actuators and also with complex communication within the control units. In order to hack a vehicle, it has to be within a certain range of communication. Here, we discuss the On-Board Diagnostic (OBD) regulations for next generation BEV/HEV, its vulnerabilities and cybersecurity threats that come with hacking. We propose three cybersecurity attack detection and defense methods: Cyber-Attack detection algorithm, Time-Based CAN Intrusion Detection Method and, Feistel Cipher Block Method. These control methods autonomously diagnose a cybersecurity problem in a vehicle’s onboard system using an OBD interface, such as OBD-II when a fault caused by a cyberattack is detected, All of this is achieved in an internal communication network structure. The results discussed here focus on the first detection method that is Cyber-Attack detection algorithm.

This paper presents experimental investigations of diagnosing and analyzing the low-frequency, low- SNR (Signal to Noise Ratio) noise sources of three motorcycles using a hybrid technology that consists of a passive SODAR (Sonic Detection And Ranging) and modified HELS (Helmholtz Equation Least Squares) methods. The former enables one to determine the precise locations of multiple sound sources in 3D space simultaneously over the entire frequency range that is consistent with a measurement microphone in non-ideal environment, where there are random background noise and unknown interfering signals. The latter enables one to reconstruct all acoustic quantities such as the acoustic pressure, acoustic intensity, time-averaged acoustic power, radiation patterns, and sound transmission paths through arbitrarily shaped vibrating structures.

The replacement of the ICE engine with an electric motor has led to a significant reduction in vibration and noise. The characteristics of the electric motor as part of the powertrain still need consideration from an NVH perspective, as there are still two highly tonal components generating noise to the cabin, albeit at higher frequencies. The radial electromagnetic force causes a structural vibration on the casing which changes with motor speed and can be used to indicate vehicle speed. The current excitation causes a primarily tangential force on the poles of the motor at a specific frequency, but both are narrow band and can cause annoyance. The traditional approach to predicting the sound radiation of electric motors is usually based on finite element analysis (FEA). While this method has the capability to estimate the time response, it is computationally too demanding and does not allow for early investigations at systems level.

The adoption of lithium-ion batteries in vehicle electrification is fast growing due to high power and energy demand on hybrid and electric vehicles. However, the battery overall performance changes with time through the vehicle life. This paper investigates the electric vehicle battery cell aging under different usages. Battery cell experimental data including open circuit voltage and internal resistance is utilized to build a typical electric vehicle model in the AVL-Cruise platform. Four driving cycles (WLTP, UDDS, HWFET, and US06) with different ambient temperatures are simulated to acquire the battery cell terminal currents. These battery cell terminal current data are inputs to the MATLAB/Simulink battery aging model. Simulation results show that battery degrades quickly in high ambient temperatures. After 15,000 hours usage in 50 degrees Celsius ambient temperature, the usable cell capacity is reduced up to 25%.

Designing a vehicle for superior crash safety performance in consumer rating tests such as US-NCAP is a compelling target in the design of passenger vehicles. In today’s context, there is also a high emphasis on making a vehicle as lightweight as possible which calls for an efficient design. In modern vehicle design, these objectives can only be achieved through Computer-Aided Engineering (CAE) for which a detailed CAD (Computer-Aided Design) model of a vehicle is a pre-requisite. In the absence of the latter (i.e. a matured CAD model) at the initial and perhaps the most crucial phase of vehicle body design, a rational approach to design would be to resort to a knowledge-based methodology which can enable crash safety assessment of an assumed design using artificial intelligence techniques such as neural networks.