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In response to global climate change, there is a widespread push to reduce carbon emissions in the transportation sector. For the difficult to decarbonize heavy-duty (HD) vehicle sector, lower carbon intensity fuels can offer a low-cost, near-term solution for CO2 reduction. The use of natural gas can provide such an alternative for HD vehicles while the increasing availability of renewable natural gas affords the opportunity for much deeper reductions in net-CO2 emissions. With this in consideration, the US National Renewable Energy Laboratory launched the Natural Gas Vehicle Research and Development Project to stimulate advancements in technology and availability of natural gas vehicles. As part of this program, Southwest Research Institute developed a hybrid-electric medium-HD vehicle (class 6) to demonstrate a substantial CO2 reduction over the baseline diesel vehicle and ultra-low NOx emissions.

Design verification and quality control of automotive components require the analysis of the source location of ultra-short sound events, for instance the engaging event of an electromechanical clutch or the clicking noise of the aluminium frame of a passenger car seat under vibration. State-of-the-art acoustic cameras allow for a frame rate of about 100 acoustic images per second. Considering that most of the sound events introduced above can be far less than 10ms, an acoustic image generated at this rate resembles an hard-to-interpret overlay of multiple sources on the structure under test along with reflections from the surrounding test environment. This contribution introduces a novel method for visualizing impulse-like sound emissions from automotive components at 10x the frame rate of traditional acoustic cameras.

Gas bearings are an effective solution to high-speed rotor applications for its contamination free, reduced maintenance and higher reliability. However, low viscosity of gas leads to lower dynamic stiffness and damping characteristics resulting in low load carrying capacity and instability at higher speeds. Gas bearings can be enhanced by adding a foil structure commonly known as gas foil bearings (GFBs), whose dynamic stiffness can be tailored by modifying the geometry and the material properties resulting in better stability and higher load carrying capacity. A detailed study is required to assess the performance of high-speed rotor systems supported on GFBs, therefore in this study a bump type GFB is analyzed for its static and dynamic characteristics. The static characteristics are obtained by solving the non-linear Reynolds equation through an iterative procedure.

The development of an effective Acoustic Vehicle Alert System (AVAS) is not solely about adhering to safety regulations; it also involves crafting an auditory experience that aligns with the expectations of vulnerable road users. To achieve this, a deep understanding of the acoustic transfer function is essential, as it defines the relationship between the sound emitter (the speaker inside the vehicle) and the receiver (the vulnerable road user). Maintaining the constancy of this acoustic transfer function is paramount, as it ensures that the sound emitted by the vehicle aligns with the intended safety cues and brand identity that is defined by the car manufacturer. In this research paper, three distinct methodologies for calculating the acoustic transfer function are presented: the classical Boundary Element method, the H-Matrix BEM accelerated method, and the Ray tracing method.

Encapsulations of E-drive systems are gaining importance in electric mobility, since they are simple measure to improve the noise behavior of the drive. Current experimental evaluation methods however pose substantial challenges for the test personnel and are associated with considerable effort in both time and cost. Evaluating the encapsulation on an e-drive test bed, for example, requires a functional e-drive and test bed resources. Evaluations in the vehicle on the other hand make objective assessments difficult and are subject to increasingly limited availability of prototype vehicles fit for NVH testing. To overcome these challenges, AVL has developed a new experimental evaluation method for the NVH efficiency of e-drive encapsulations. In this method, the e-drive is freely suspended in a semi-anechoic chamber and its structure is excited using shakers while the radiated noise with and without encapsulation is measured.

Water management in PEMFC power generation systems is a key point to guarantee optimal performances and durability. It is known that a poor water management has a direct impact on PEMFC voltage, both in drying and flooding conditions: furthermore, water management entails phenomena from micro-scale, i.e., formation and water transport within membrane, to meso-scale, i.e., water capillary transport inside the GDL, up to the macro-scale, i.e., water droplet formation and removal from the GFC. Water transport mechanisms through the membrane are well known in literature, but typically a high computational burden is requested for their proper simulation. To deal with this issue, the authors have developed an analytical model for the water membrane content simulation as function of stack temperature and current density, for fast on-board monitoring and control purposes, with good fit with literature data.

Nowadays, electrification is largely acknowledged as a crucial strategy to mitigate climate change, especially for the transportation sector through the transition from conventional vehicles to electric vehicles (EVs). As the demand for EVs continues to rise, the development of a robust and widespread charging infrastructure has become a top priority for governments and decision-makers. In this context, innovative approaches to energy management and sustainability, such as Vehicle-to-Grid (V2G), are gradually being employed, leading to new challenges, like grid service integration, charge scheduling and public acceptance. For instance, the planned use scenario, the user’s behaviour, and the reachability of the geographical position influence the optimal energy management strategies both maintain user satisfaction and optimize grid impact.

A major issue of battery electric vehicles (BEV) is optimizing driving range and energy consumption. Under actual driving, transient thermal and electrical performance changes could deteriorate the battery cells and pack. These performances can be investigated and controlled efficiently with a thermal management system (TMS) via model-based development. A complete battery pack contains multiple cells, bricks, and modules with numerous coolant pipes and flow channels. However, such an early modeling stage requires detailed cell geometry and specifications to estimate the thermal and electrochemical energies of the cell, module, and pack. To capture the dynamic performance changes of the LIB pack under real driving cycles, the thermal energy flow between the pack and its TMS must be well predicted. This study presents a BTMS model development and validation method for a 75-kWh battery pack used in mass-production, mid-size battery SUV under WLTC.

Many research centers and companies in general aviation have been devoting efforts to the electrification of propulsive plants to reduce environmental impact and/or increase safety. Even if the final goal is the elimination of fossil fuels, the limitations of today's battery in terms of energy and power densities suggest the adoption of hybrid-electric solutions that combine the advantages of conventional and electric propulsive systems, namely reduced fuel consumption, high peak power, and increased safety deriving from redundancy. Today, lithium batteries are the best commercial option for the electrification of all means of transportation. However, lithium batteries are a family of technologies that presents a variety of specifications in terms of gravimetric and volumetric energy density, discharge and charge currents, safety, and cost.

Though modal analysis is a common tool to evaluate the dynamic properties of a structure, there are still many individual decisions to be made during the process which are often based on experience and make it difficult for occasional users to gain reliable and correct results. One of those experience-based choices is the correct number and placement of reference points. This decision is especially important, because it must be made right in the beginning of the process and a wrong choice is only noticeable in the very end of the process. Picking the wrong reference points could result in incomplete modal analysis outcomes, as it might make certain modes undetectable, compounded by the user's lack of awareness about these missing modes. In the paper an innovative approach will be presented to choose the minimal number of mandatory reference points and their placement.

The benefits introduced by the replacement of conventional centrifugal pumps with volumetric machines for Internal Combustion Engines (ICEs) cooling were experimentally and theoretically proven in literature. In particular, Sliding Rotary Vane Pumps (SVRPs) ensure to achieve an interesting reduction of ICEs fuel consumption and CO2 emissions. Despite volumetric pumps are a reference technology for ICE lubrication oil circuits, the application in ICE cooling systems still not represent a ready-to-market solution. Particularly challenging is the case of Heavy-Duty ICE due to the wide operating range the pump covers in terms of flow rate delivered. Generally, SVRPs are designed to operate at high speeds to reduce machine dimensions and, consequently, the weight. Nevertheless, speed increase could lead to a severe penalization of pump performance since the growth of the friction losses.

Vehicle powertrain electrification is considered one of the main measures adopted by vehicle manufacturers to achieve the CO2 emissions targets. Although the development of vehicles with hybrid and plug-in hybrid powertrains is based on existing platforms, the complexity of the system is significantly increased. As a result, the demands of testing during the development and calibration stages is getting significantly higher. To compensate that, high-fidelity simulation models are used as a cost-effective solution. This paper aims to present the methodology followed for the development of a rule-based energy management controller for a plug-in hybrid electric vehicle (PHEV), and to describe the experimental campaign carried out with this passenger car. The controller is implemented in a vehicle simulation model that is parametrized to replicate the operation of the vehicle.

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.

As environmental concerns have taken the spotlight, electrified powertrains are rapidly being integrated into vehicles across various brands, boosting their market share. With the increasing adoption of electric vehicles, market demands are growing, and competition is intensifying. This trend has led to stricter standards for noise and vibration as well. To meet these requirements, it is necessary to not only address the inherent noise and vibration sources in electric powertrains, primarily from motors and gearboxes, but also to analyze the impact of the spline power transmission structure on system vibration and noise. Especially crucial is the consideration of manufacturing discrepancies, such as pitch errors in splines, which various studies have highlighted as contributors to noise and vibration in electric powertrains. This paper focuses on comparing and analyzing the influence of spline pitch errors on two layout configurations of motor and gearbox spline coupling structures.

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.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

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.

To meet vehicle interior noise targets and expectations, components including those related to electric vehicles (EVs) can effectively be treated at the source with an encapsulation approach, preventing acoustic and vibration sources from propagating through multiple paths into the vehicle interior. Encapsulation can be especially useful when dealing with tonal noise sources in EVs which are common for electrical components. These treatments involve materials that block noise and vibration at its source but add weight and cost to vehicles – optimization and ensuring the material used is minimized but efficient in reducing noise everywhere where it is applied is critically important. Testing is important to confirm source levels and verify performance of some proposed configurations, but ideal encapsulation treatments are complex and cannot be efficiently achieved by trial-and-error testing.

For electric vehicles, road noise, together with wind noise, is the most important contributor for vehicle interior noise. Road noise is very dependent on the NVH behavior of axle system including wheels and tires. Axle system is part of vehicle platform which should be compatible with different body variants. Therefore, il is important to characterize the NVH performance of an axle system independently of car body structure, so that the design the axle can be optimized at the early stage according to the global requirements of all the related vehicles. The best way to characterize the NVH performance of an axle system is to measure the blocked forces on an appropriate test rig. However, the measurement of blocked forces from an axle system requires very stiff boundary conditions which is difficult to achieve in practice. For axles with rigid mountings, it is nearly impossible to measure the blocked forces on test rig.

During design development phases, automotive components undergo a strict validation process aiming to demonstrate requested levels of performance and durability. In some cases, specific developments encounter a major blocking point : decoupling systems responsible for optimal acoustic performances. On the one hand, damping rubbers need to be soft to comply with noise, vibration & harshness criteria. However, softness would provoke such high amplitudes during vibration endurance tests that components would suffer from failures. On the other hand, stiffer rubbers, designed for durability purposes, would fail to meet noise compliance. The rubber design development goes through a double-faced dilemma : design with acceptable trade-off between NVH and durability, and efficient ways to develop compliant designs. This paper illustrates two case studies where different methodologies are applied to validate decoupling systems from both acoustic and reliability perspectives.