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The aeroacoustic development of vehicles is still mainly carried out in wind tunnels under steady flow conditions, although the real situation is different. However, as discussed in several earlier publications, a vehicle experiences unsteady, turbulent flow on road, which results for example from natural wind, wakes of other vehicles, or obstacles at the roadside in combination with side wind. The resulting temporal variations of the wind noise inside the cabin affect the passengers’ comfort and safety through fatigue. To be able to also consider the unsteady aeroacoustics in the vehicle development process, a comprehensive method has been developed that is presented in full for the first time in this paper. The on-road situation is simulated in a realistic and reproducible manner in the full-scale wind tunnel of the University of Stuttgart by means of an active turbulence generator, developed by FKFS. The turbulence generator allows to dynamically vary the flow angle with frequency components of up to 10 Hz. By comparing the unsteady wind noise for different flow situations and different vehicles, a flow scenario has been derived which leads to representative and meaningful aeroacoustic results. The measured temporal wind noise variations are investigated using modulation analysis, which has been adapted to show only relevant information in a comprehensible way. Finally, a psychoacoustic metric for rating the annoyance of unsteady, as well as steady, wind noise has been developed based on listening studies. The metric describes the subjective perception and allows evaluation and optimization of the aeroacoustic behavior specifically regarding the passengers’ comfort. With this new method all wind noise aspects, steady as well as unsteady, can be determined and taken into account for vehicle development. The evaluation and comparison of vehicles and variants is comprehensive and is focused on the passengers’ comfort.

As an important vibration damping element in automobile industries, the vibration transmitted from the engine to the frame can be reduced effectively because of rubber mount. The influence of preload on the static characteristics of rubber mount and the constitutive parameters identification of Mooney-Rivlin model under preload were studied. Firstly, a test rig for stiffness measurement of rubber mount under preload was designed and the influence of preload on the force versus displacement of mount was studied. Then, the model for estimating force versus displacement of rubber mount was established. The response surface model for parameters identification was established. And the identification method for estimating parameters of Mooney-Rivlin model of rubber mount was proposed with the crow search algorithm. Taking the rubber mount as the research object and taking the parameters of Mooney-Rivlin model as the variables. Then, using response polynomials corresponding to different force-displacement curves as the objective functions, a least squares method is used to ontain constitute parameters of Mooney-Rivlin model. The force versus displacement for the mount is estimated using the obtained parameters of Mooney-Rivlin model along with the developed model. The results show that the relative errors calculated with parameters identified under preload are keeping in 10% in all working conditions. The real force performance of the rubber mount is better reflected by the parameters identified under preload. The parameters identification method proposed in this paper can provide reference for the accurate identification of constitutive parameters.

The tooth surface error will affect the contact pattern and transmission error of the hypoid gear, which may result in an unfavorable dynamic response. The tooth surface error can be generated by machine tool errors, such as blade wear. The most common forms of blade wear are the positive cutter radius and the positive blade angle error. In addition, in the cutting process of face-hobbed hypoid gear, the continuous indexing motion will aggravate the blade wear due to the alternating cutting force. Most previous studies on the influence of hypoid gear tool errors only focus on the contact pattern and static transmission error. However, there are very few studies about the effect of tool errors on hypoid gear dynamic responses. In this paper, a hypoid gear tooth surface, mesh, and linear dynamic model with tool errors were established. The tooth surface deviation distribution of different tool errors was analyzed. The effective mesh parameters such as time-varying mesh points, line-of-action, mesh stiffness, and transmission error harmonics are calculated for different cutter radius errors and blade angle errors. The dynamic transmission error and mesh force are also analyzed to evaluate the hypoid gear vibratory responses. The simulation results show the negative cutter radius error and positive blade angle error will increase the dynamic responses of hypoid gear. The influence is more obvious under the high torque.

Automotive OEMs are required to meet applicable regulations for exterior noise for vehicles they produce. Acceleration noise (typically called pass-by noise) regulations impose an upper limit for noise emission. In addition, vehicles which can operate without a combustion engine must comply with regulations for minimum noise emitted during low speed driving. In order to make regulation-compliant measurements for global destinations, a test track meeting requirements of ISO 10844 may be necessary. However, strictly meeting this requirement doesn’t guarantee a usable facility for efficient measurements. This paper describes design goals, challenges and construction of a regulation compliant facility in Arizona. The intent was to build a facility with a long usable life before requiring repaving, sufficient isolation from nearby test roads, 24-hour usability and onsite amenities to accommodate technical staffs and vehicle retrofits. Consideration of needs of all stakeholders was necessary to make a facility not only regulation-compliant but also attractive to internal and external customers.

Fast NVH analysis from the start of the motor design is important to speed up product development by avoiding major NVH issues later. In this paper, different methods for electric motor noise and vibration predictions are proposed for different stages in the design process. The results are compared to previous experimental validation [1]. The first method combines electromagnetic finite element analysis to calculate the forces, with analytical models for the stator vibration and acoustic response [2]. This requires minimal structural design data, and can be carried out rapidly, allowing the exploration of many design possibilities in the early design stage, and is implemented in Ansys Motor-CAD. This is compared with the full structural finite element analysis (FEA) [1]. It is shown that the fast analytical NVH process provides a good prediction for the motor noise level early in the design process. The structural FEA approach provides additional information about the structural response at a later stage to refine the design. The use of frequency response functions (FRF) derived from the structural FEA are demonstrated, allowing the rapid calculation of noise results from the electromagnetic forces.

This paper presents a workflow that allows noise, vibration and harshness (NVH) engineers to objectively predict the passenger compartment noise levels due to structure-borne and radiated noise arising from the motor of an electric powertrain (ePowertrain). The optimized simulation workflow enables transmission, vehicle design engineers and NVH analyst to collaborate and address potential noise concerns well before production of the ePowertrain unit and vehicle. The NVH targets can be cascaded through a series of transfer functions, linking the electromagnetic (EM) excitation from the motor to passenger compartment noise level requirements. The workflow involves the use of Romax Spectrum and Actran software. The structural modelling of the ePowertrain including the vibration response of the ePowertrain is calculated using Romax Spectrum, whilst Actran computes the acoustic radiation around the complete vehicle, and Virtual SEA then covers the calculation to interior and exterior vehicle noise analysis. The former enables early NVH diagnostics and optimization of the ePowertrain system. The Actran Virtual SEA approach is used to extend the frequency range of an existing full-vehicle Finite Element (FE) model. It offers the advantage to evaluate on the same model, both the structure-borne and air-borne noise inside the cabin and assess their relative impact. This is an innovative approach compared to past methods used for evaluating the NVH performance of ICE vehicles where FE models were used for low-frequency structure-borne noise and Statistical Energy Analysis (SEA) models for high-frequency air-borne noise. This separation is not anymore possible and reliable high frequency methods for structure-borne noise are now mandatory.

Vehicle weight reduction is important to improve the fuel mileage of Internal Combustion Engine (ICE) vehicles and to extend the range of Electric Vehicles (EVs). Glass Fiber Reinforced (GFR) Composite (Polyamide) brackets provide significant weight reductions at a competitive part price. Traditionally, metal brackets are designed to surpass a target natural frequency and static stiffness. Composite brackets are inherently less stiff and have lower natural frequencies. However, composite brackets also have higher material damping than metal brackets, and good isolation performance can be achieved. The key to integrating composite brackets into the vehicle design is to perform adequate analysis to ensure that the noise and vibration performance at the vehicle level meets expectations. In this paper, case studies are presented for two different vehicles – a Clevis bracket for an IC Engine vehicle, and an electric motor mount bracket. For each case, measurement data is used to develop simulator models for the vehicles with the traditional metal brackets as well as the composite brackets, and the models are used to make a comparative evaluation of passenger noise for different driving conditions.

The future is expected to bring Advanced Air Mobility (AAM) vehicles, including small unmanned aerial systems (sUAS), urban air mobility (UAM) vehicles and regional air mobility (RAM) vehicles. These manned and unmanned vehicles are propelled by rotors. Rotors tend to generate tonal sound as their blades interact periodically with airflow features. Since people are more sensitive to tonality, including tones, than broad band sound, AAM generated tonality is expected to be an important consideration for design. In this paper several tonality metrics are examined for their ability to explain perceived annoyance of AAM flyover noise as measured by NASA’s Rotorcraft Sound Quality Metric 1 (RoQM-1) test. The various investigated metrics use one-third octave band, narrow band, and autocorrelation analysis. It is observed that tonality influences but does not control perceived flyover noise annoyance due to other sound qualities like roughness, consistent with previous work. The metrics are also examined for their ability to explain perceived tonality of sounds generated by IT equipment. The metrics based on autocorrelation are observed to best explain perceived tonality while also being among the best for explaining flyover annoyance. The plausibility of the auditory nervous system as a physiological auto-correlator is discussed.

Ensuring the robustness of vehicle systems and features is a key aspect of customer satisfaction and therefore is of vital importance to Original Equipment Manufacturers (OEMs). Both initial quality and quality over the life of the vehicle are important aspects to customers. Therefore, some features and systems require an active approach at monitoring system health. The intent of Active Noise Cancellation (ANC) systems in vehicles is to increase the customer’s perception of vehicle refinement and brand quality. Due to the nature of ANC system operation, it is critical that the system does not go divergent resulting in unwanted noise being heard by the customer. So, a method to monitor the ANC system operation and performance is required to ensure robustness. Despite tuning ANC systems considering expected vehicle build tolerances, vehicle aging and customer specific use cases, an ANC system can occasionally still go divergent in the field. An ANC audit procedure is included in the system software to verify functionality and performance at End of Line (EOL), repair centers or verified though an Over the Air (OTA) procedure. This type of procedure can verify the vehicle’s acoustic cavity response and update ANC tuning parameters to account for any changes. This paper will show vehicle examples of why an ANC health monitoring system is needed and how we developed, implemented, and utilized an ANC audit procedure to improve ANC system robustness and customer satisfaction.

Foaming materials such as 2C-PUR or expandable baffles are increasingly used in the car body acoustic package of modern passenger vehicles. Over the last several decades the primary function of foaming materials was the moisture sealing and airborne noise absorption / insulation in various areas of the car body such as pillars, door sills or other cavities. Recent developments also show an increasing application of expandable foams, functioning as structural dampers and reducing structure-borne noise transmission through frames and pillars. This paper summarizes the results of various studies that deal with the impact of expandable baffle materials on structure-borne noise in car bodies with special focus on mid and high frequencies which become more relevant in the acoustic optimization efforts of EV’s. Structural vibrations are evaluated experimentally on foamed generic frames and double sheet metal systems under free-free boundary conditions. The most promising candidate among tested foaming materials is then introduced into a C-Segment car body. This body is investigated with laser scanning vibrometry and transfer functions are measured using dual shaker excitation. The presented results are showing potential damping performance of baffle foams compared to empty body and bodies damped with conventional liquid-applied-sound-dampers and bitumen foils.

HVAC systems are of critical importance in ensuring passengers’ thermal comfort inside the car cabin as well as safety requirements for defogging functions. These systems involve various components and subcomponents such as blowers, thermal exchangers or actuators, with a wide range of well-known technologies and also new ones on recently introduced innovative products.Currently, within established electrification trends worldwide, the HVAC system is becoming the most important embedded system that can induce major contribution of noise and vibration. These NVH issues can emerge through different transfer paths inside the car cabin possibly causing significant discomfort to passengers.During developments, the NVH issues are mastered and contained by both suppliers according to internal requirements and OEMs according to specifications. However, OEM specifications are mainly defined by overall noise levels and improvements over the years are generally consisting of reducing these specified levels. Furthermore, some HVAC NVH issues may not be well detected when using regular NVH metrics. This raises concerns about the limitations of the regularly used metrics in ensuring specification compliance and, above all, in depicting a subjective assessment at component level.Throughout a statistical analysis of HVAC systems population, this paper first focuses on the discrepancies between the data provided by traditional NVH metrics and subjective evaluations. Then, a deeper analysis involving psychoacoustic metrics provides a relevance overview of the applied metrics depending on the encountered issues and compared to a subjective assessment. Finally, from a responsibility perspective, these findings raise questions about the relevance of regular metrics used by OEMs and the right way to handle HVAC NVH topics nowadays.

In the mounting of vibration sources and receivers, it is typically desirable to have low stiffness for isolation. On the other hand, durability may demand a high stiffness to handle large inputs without excessive motion, which seems like a contradictory requirement. Both may be achieved using nonlinear stiffness mounts which make use of elastomer deformation to exhibit softening through geometric nonlinearity. This paper discusses a physical proof of concept for a quasi-zero stiffness mount design with a three-regime stiffness curve including a preload, isolation, and motion control regions. Building on a design concept proposed in prior literature, new experimental validation is obtained for the prior nonlinear static stiffness property, which is then fit into a more stable mount topology. Fabrication and material issues are also discussed. Finite element models are used with material coupon tests to characterize the elastic properties of chosen materials, as well as predicting the stiffness behavior of the mount. Computational models are also used along with sinusoidal vibration measurements to assess the effect of damping, which is quantified with structural, viscous, and fractional calculus based damping models. Experimental validation of the new configuration is provided for both static and dynamic excitations.

Community noise at vertiports is one of the most important questions related to upcoming urban air mobility (UAM) operations. While fixed-wing and/or fixed-rotor aircraft can mainly be treated by their changing operational parameters, such as rotor or propeller rpm, tilt-wing or tilt-engine configurations are more difficult to simulate because of their constantly changing noise emission and spatial radiation characteristics. The work presented in this paper is providing an overview of the noise situation at a virtual vertiport which is being approached and departed by a tilt-wing air-taxi in different ways. Several different departure procedures are simulated with the same generic air-taxi. For the noise emission semi-empiric methods were used. During the air-taxi’s descent and climb, different tilt configurations are included, mainly defined by the time dependent engine’s tilt-angle, but also related to different approach paths. Each approach or departure procedure is generating individually time dependent changing noise emission characteristics which are used for noise footprint mapping around the touch-down or lift-off point. Different noise metrics are computed, including some which are representative to indicate noise annoyance among the vertiport’s neighborhood. It is expected that each procedure will generate its own specific community noise situation in the vicinity of the vertiport, and thus may provide an early recommenddation for later operations. The results are compared towards each other. Additionally, a simulated certification flyover according to ICAO Chapter 13 - which is about the noise certification of tilt-rotor aircraft - is set into relation to the vertiport’s community noise.

A typical cab used on agriculture machines is made up of a metal frame structure with large enclosing panels of glass, plastic, and metal. Acoustic treatments such as coatings, textiles and foams are used within the cab for aesthetics but also to mediate undesired noise. To develop effective designs for the cab to combat noise, accurate tools for measurement, and predictive methods for sound transmission loss are needed. This paper focuses on Sound Transmission Loss (STL) of the rear upper panel of a cab used in agriculture machines. Results from CAE based tools such as Statistical Energy Analysis (SEA), Finite Element Analysis (FEA) and Hybrid FE-SEA methods are compared to measurements. The panel studied included features such as curvature, deep drawn beads with a glass window and a damping coating. The simulation results are refined by incorporating methods for accurate modeling of ribs stiffness, curvature effect and radiation efficiency by synthetic modal approach. The STL predicted by this improved model correlate well with the data from test. This modeling methodology is further used to model various panels and build SEA model of the complete cab.

Current and future EV’s contain significant amounts of complex electrical hardware, including rechargeable energy modules, control units, cooling systems and wiring situated inside the cabin usually below the carpet, seats or trunk trim and below the cabin floor. These items, whilst likely to have a direct impact on transmission loss, are increasingly difficult to evaluate via typical methods of computer-based simulation. In particular, the packaging space allocated for control units, which may require an air gap between the body in white and the carpet for aspects of heat stabilization can be difficult to model using the transfer matrix method.In the case of battery installations their high bulk mass doesn’t necessarily provide significant increases in transmission loss due to adjacent acoustic weaknesses and the inherent sensitivity of the floor system. This paper examines a selection of novel techniques, using sound Phonons, developed to predict both baseline transmission loss and absorption and subsequently acoustic performance optimization. The user-friendly nature of the sound Phonon application enabled “full vehicle” models to be efficiently created for interior and exterior noise prediction. This is particularly useful for EV target compliance evaluation when a platform is undergoing conversion from ICE to EV or assisting a “clean sheet” EV design.

This paper investigates the problem of whine in the E-axle (Electric Drive Axle) system during acceleration of a light bus. The problem is identified as motor and reducer whine by the noise spectrum feature analysis method. Under the condition of ensuring motor performance and low cost, the motor whine is solved by optimizing the air tightness and sound insulation of the vehicle. Starting from gear microscopic shaping research, gear manufacturing and assembly process optimization was used to control gear whine. After testing and analysis, the means to effectively solve the E-axle system whine problem. The whine noise is optimized by about 8 dB (A). The results provide key technical support for the smooth production of the vehicle. It has certain guiding significance for the NVH (Noise, Vibration and Harshness) performance design and development of the E-axle system project.

Newly manufactured light-duty hybrid and electric passenger vehicles must comply with FMVSS 141 minimum sound requirements to reduce the risk of crashes with visually impaired and inattentive pedestrians. Commercial vehicles operate in a variety of noise-critical environments, from densely packed industrial yards to congested urban areas, making safe electric vehicle operation around pedestrians and bystanders vital. Though the market share of medium and heavy-duty hybrid and electric vehicles is projected to increase annually, there are currently no North American regulations specifically for minimum sound emissions of hybrid and electric vehicles heavier than 10,000 lb. GVWR. The primary intent of this paper is to investigate the efficacy and limitations of the current FMVSS 141 requirements when applied to heavy-duty electric trucks. Serving as a complementary test case, a Class 8 Freightliner eCascadia electric truck with onboard Acoustic Vehicle Alerting System (AVAS) was evaluated at Daimler Truck North America’s High Desert Proving Grounds in accordance with the FMVSS 141 procedure for light-duty passenger vehicles. Analysis of the measured data found the FMVSS 141 forward gear criteria to be reasonably effective when applied to the test vehicle, but the standard’s reverse gear criteria were deficient to other common industry-accepted backing alert methods. These results are used as a starting point for a discussion on various aspects of FMVSS 141, with an emphasis on how adding commercial vehicles to the scope of the standard could necessitate further changes to the defined acceptance criteria.

An Inline 4-cylinder engine is equipped with second-order balance shafts. When the engine is running under no-load acceleration conditions, the gear system of the balance shaft generated whine noise. In this paper, an analysis and experiment method for reducing the whine noise is presented. First, a flexible multi-body dynamic model of the engine is established, which includes shaft and casing deformation, micro-modification of the gears. Taking the measured cylinder pressure as input, the load on each gear of balance shaft gear system is calculated. In addition, the influence of tooth surface micro-modification on the meshed noise was analyzed. The results show that the dynamic meshing force between the crank gear and the shim gear is large under the original tooth surface micro-modification parameters, which is the main reason of the whine noise. The torsional vibration at the crankshaft nose and vibration acceleration at the cylinder block was measured during no-load conditions, and the measured results were compared with the calculated results, which validate the established model. Secondly, the mesh misalignment and load calculated in the model are used as inputs to optimize the tooth surface micro-modification parameters. Taking minimizing the peak-to-peak of loaded transmission error and the maximum contact stress on the tooth surface as optimization objectives, the micro-modification parameters as optimization variables, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm was used to conduct multi-objective optimization. A set of tooth surface micro-modification parameters was optimized, and the peak-to-peak loaded transmission error is reduced by 80%. According to the optimized parameters, the new scissor gears was fabricated and installed on the balance shaft. The experiment results showed that the whine evaluation index TNR (Tone-to-Noise Ratio) is reduced from 2.2dB to -1.4dB, which solved the whine noise.

Electric and Hybrid vehicles have standards for emitting enough noise to reduce danger and risk to pedestrians when operating at low speeds. Simulation can help to support development and deployment of these systems while avoiding a time-consuming, test-based approach to design these AVAS (Acoustic Vehicle Alerting System) warning systems. Traditionally, deterministic simulation methods such as Finite Element Method (FEM) and Boundary Element Method (BEM) are used at low frequencies and statistical, energy-based methods such as Statistical Energy Analysis (SEA) are used at high frequencies. The deterministic methods are accurate, but computationally inefficient, particularly when the frequency increases. SEA is computationally efficient but does not capture well the physics of exterior acoustic propagation. An alternative method commonly used in room acoustics, based on geometrical or ray acoustics, is “Ray Tracing” and can be used for sound field prediction. Ray Tracing generally estimates sound fields with an acceptable degree of accuracy through the full range of frequencies and can predict temporal and spatial distribution of sound fields with suitable accuracy for auralization. The image-source method is a ray acoustics method that guarantees finding all reflection paths up to a given order and can include diffraction effects.In this paper, a systematic approach is discussed to evaluate the potential for Ray Tracing to predict sound fields for AVAS design. Ray Tracing models with different combinations of parameters are compared to a BEM model as a reference. An accelerated BEM method known as H-matrix is also included in the comparison as a reference and to evaluate the potential for accelerated BEM to be used when more accuracy than Ray Tracing is needed but standard BEM is challenging to use because of model size and frequency range. Some conclusions about these methods for this application and others, such as Pass-By Noise prediction, are presented.

The thermal and mechanical loads of the engine rise dramatically with the increase in engine power density, which places higher demands on the design of the piston. In this paper, the design development of a steel piston for a marine diesel engine belonging to 190 series heavy-duty diesel engines was studied. The design methods including material selection and structural design were used to finished the preliminary design. In the meanwhile, the design philosophies of the aluminum alloy piston and composite piston for the 190 series diesel engines were used for reference in the design process. The designed steel piston was tested in the engine durability bench test and simulated for reliability. The results showed that the failure of the steel piston occurred at the same position in both the test and the simulation. The cause of cracking in the steel piston was analyzed, and the insufficient strength of the local structure led to high-cycle fatigue failure. Based on the failure mechanism, the modified design was carried out. The improved steel piston not only satisfied the fatigue strength assessment but passed the engine durability bench test successfully. Finally, the design development of the steel piston was finished. This study has significance for the design of steel pistons for large-bore diesel engines.

The driver monitoring system (DMS) plays an essential role in reducing traffic accidents caused by human errors due to driver distraction and fatigue. The vision-based DMS has been the most widely used because of its advantages of non-contact and high recognition accuracy. However, the traditional RGB camera-based DMS has poor recognition accuracy under complex lighting conditions, while the IR-based DMS has a high cost. In order to improve the recognition accuracy of conventional RGB camera-based DMS under complicated illumination conditions, this paper proposes a lightweight low-illumination image enhancement network inspired by the Retinex theory. The lightweight aspect of the network structure is realized by introducing a pixel-wise adjustment function. In addition, the optimization bottleneck problem is solved by introducing the shortcut mechanism. Model performance comparison test results demonstrate that the Structure Similarity Index Measure index of the proposed model is 7.04% and 31.03% higher than that of Multi-Scale Retinex with Color Restoration and Contrast Limiting Adaptive Histogram Equalization, respectively. In the use case, the proposed model is capable to process videos with a resolution of 400×600 at a speed of 20fps on average, which meets the requirements of DMS video stream processing speed. Furthermore, a MobileNet based distraction state recognition network pre-trained on the SFD3 dataset is adopted as the back-end to verify its application in the DMS system. The results show that the accuracy in the driver's distracted behavior recognition with a low-light environment is improved by 75.39% compared to before use.

The vehicle instrument panel (IP) system has several interactions with the surrounding components such as the Dash, Cowl, Cross Car Beam (CCB), Floor, Body Side etc. With such interactions comes different loadings, usage scenarios, interfaces and design challenges to overcome. For the specific case of the IP to Cowl & Dash interfaces, the position and performance in different load cases, such as, but not limited to, vibration and heat expansion loading as well as the assembly process. A design solution is required to enhance the performance in all these scenarios while maintaining the cost, weight & complexity as low as possible. This paper describes the development process of an optimized solution with a multi-disciplinary approach using advanced computer aided engineering (CAE) optimization tools, which involved performance in multiple virtual evaluations and mass. The achieved enhanced solution provides of multiple alternatives from early design stages to allow flexibility in the manufacture stage, it also proved to be effective to reduce complexity by using few parts in the assembly. The enhanced method reduces time required to assemble and the weight while meeting the performance required.

Metal fatigue has been a topic interesting to engineers for long, because it has had a profound impact on making the design of virtually all products more reliable. As the research in this area evolved, the understanding of fatigue has been gained so tremendously that it has been become possible to conduct more and more complicated fatigue analysis or simulation without having to undergo lengthy fatigue tests for a product under design. In this work is a numerical model to predict the fatigue life of a power train component, whose metal material essentially behaves as being inhomogeneous due to a thin and hard surface layer on the top of the base material. As a typical case in the power train of automobiles, the clutch component is subject to the inertial load arising from an angular velocity as well as the torque loads, varying cyclically with time in a form other than what is called constant-amplitude. The strain or stress responses to the loads show that they depend not only on the loading history, but also on the status in the clutch engagement. As the loads have caused the component to exceed the yield somewhere in the metal structure, strain-life approaches are employed to compute the number of cycles to failure. A commonly-used linear damage cumulative summation, known as Miner’s Rule, is applied to assess the fatigue damage fraction. Through the results from the fatigue simulation, it will be demonstrated that the case-hardened surface has much less fatigue damage than the inner core. In other words, it is the core at the interface between the material zones that initiates a crack. Also, in the end of this paper, the effect of the depth of case-hardening on the fatigue life will be discussed.

In response to the steadily worsening impact of global warming, greater efforts are being made to achieve carbon neutrality. Toyota Motor Corporation developed an in-vehicle solar charging system that utilizes generated solar energy to drive the vehicle. While the ignition is off, energy generated from a solar panel is used to charge the main battery. Then, while the ignition is on, this energy is supplied to the 12 V system to reduce consumption of the main battery energy, thereby helping to improve the electric driving range. This 1st-generation solar charging system adopted in the Prius PHV in 2017 was the first mass-produced in-vehicle solar charging system in the world. In 2022, the 2nd-generation solar charging system was developed and adopted in the bZ4X, including performance improvements such as a newly designed solar roof and lightweight charging system. However, further enhancements are necessary to facilitate the adoption of in-vehicle solar charging systems, as well as to help achieve carbon neutrality. Therefore, the 2nd-generation solar charging system has been adopted on the new Prius Prime and launched in the USA for the first time in 2023. Eight representative cities in the USA were selected under the Köppen climate classification, and the annual and monthly energy generated from its solar panels was calculated from solar radiation data. As a result, the performance of the 2nd-generation solar charging system was calculated in the USA and found to show high product value. This paper introduces the development of the 2nd-generation solar charging system and the performance generated from its solar panels in the USA.

In this paper, an equivalent conversion method is proposed to apply the six-dimensional force road spectrum of the four-axle vehicle on the same platform to the three-axle through the axle load comparison. Further, the feasibility of the devolved equivalent conversion method is verified, and the fatigue performance improvement of the wishbone support structure of a commercial vehicle is finally achieved. Specifically, firstly, the load spectrum at each attachment point of the suspension for the three-axle vehicle is obtained through the iteration of the multi-body dynamic model. Furthermore, the finite element model of the suspension for the three-axle vehicle is established; the analysis of fatigue life for the suspension structure is performed by extracting stress amplitude through the multi-axis cyclic counting method and calculating equivalent force amplitude through McDiarmid’s criterion, combined with the SN curve of the material. Finally, the prediction and optimization design of the fatigue weak position for the wishbone support are realized. The results of the three-axle vehicle after trial production show that the fatigue damage position of the control arm bracket is consistent with the simulation prediction, and the fatigue performance of the optimized suspension is significantly improved. Therefore, the developed method can be applied to the fatigue analysis of replacement vehicle components, which can shorten the design cycle and improve the efficiency under the premise of ensuring the accuracy and reliability of fatigue life prediction.

Fuel cell electric vehicle is one of the powertrain paths to reach zero emission suggested by many different government and research institutes. This paper shows a convenient tool to model fuel cell electric vehicle performance with simple input. By using an in-house developed hybrid/battery/fuel cell vehicle modeling tool (HBFCMT), the fuel cell powertrain combination can be analyzed with the assumption of certain control strategy. One example of light duty passenger vehicle is analyzed using this tool. Corresponding vehicle and fuel cell performance data are presented. Compared with test data, the model results are within the expectation in trend wise. Overall, the difference between the model and test data is 6.1% regarding the key performance data: hydrogen consumption in the WLTC cycle. Such conclusion is reasonable to justify the application of the modelling tool in new energy fuel cell electric vehicle design and developments.

This paper presents the design of Interior Permanent Magnet Synchronous motor (IPMSM) using Soft Magnetic Composite (SMC) material for Electric Vehicle (EV) applications. In EV, the electric motor is characterized by high power density, high torque, low weight and cost efficient. The high torque, high power to weight ratio and high mechanical strength Synchronous Motor along with the SMC structures will make it suitable candidate for such an EV application. With this focus the new SMC material Somaloy 3P is proposed as a motor core material. The SMC material is made up of iron powder particle coated with an electrically insulated layer which gives a unique 3D flux property. This material provides low eddy current losses because of high material resistance. The benefits of Somaloy 3P material for design of electric motor are compact, high performance, weight reduction and cost efficient. The impacts of Soft magnetic composite in IPMSM have been investigated through Electromagnetic Finite Element Analysis (FEA) static and dynamic characterization with purpose of reducing the iron loss and weight and cost of the motor. Electromagnetic field study is carried out to characterize and compare key advantages of Somaloy 3P material with the conventional silicon steel material. This research may provide guidance for the future prototype.

Climate change has become a real problem in our world. Society is trying to contain it as much as possible, promoting more sustainable behaviors and limiting pollution. For the automotive industry, this leads to progressive electrification and reduction of tailpipe emissions and fuel consumption for conventional vehicles. In this framework, this paper presents the design of a vehicle to compete in the Urban Concept category of Shell Eco Marathon, a competition among universities that has the goal to release a vehicle with the lowest possible fuel consumption. This work describes the monocoque design phases of the vehicle JUNO. The complete design approach is described, through the analysis of the decisional workflow adopted to integrate every technical solution from the aerodynamic constraints to the structural ones passing from the vehicle dynamic requirements. The methodology adopted to size the monocoque is explained in depth through the analysis of the multi-step optimization performed by means of finite element models. Therefore, the load configuration and the design constraints are illustrated with the aim of mass optimization. Specifically, the rollover and the torsional stiffness test have been chosen as the most significative load cases in terms of structural integrity and performance respectively. Furthermore, the definition of the final monocoque geometry and stacking sequence is described together with the virtual results obtained. Finally, a brief explanation of the production process is presented, to complete all the project phases.

It is estimated that the share of autonomous vehicles in the market will reach an important point between 2050s and 2060s. Some major benefits of autonomy in ground vehicles can be regarded as reducing traffic, saving fuel and reducing emissions. Accordingly, it is anticipated that autonomous vehicles (AVs) will prevent driver error from happening, which is the primary cause of 90% of traffic accidents. However, it is a prerequisite that the AVs are accepted by the public, and be used regularly in daily life. AVs obliges everyone to be a passenger, thereby occupants will lose authority on the vehicle and have to deal with non-driving tasks during an automated ride. This will increase the lack of situational awareness, leading occupants to be more sensitive to motion sickness, where the major reasons of motion sickness are conflict between vestibular and visual senses, lack of control, unable to predict the direction of movement. Increase in motion sickness reduces comfort and can adversely affect people's attitude towards AVs. In this study, a Simscape Multibody dynamic model is developed based on a 6 DOF biodynamics model including the vertical and fore-aft motion of the whole body. The multibody model is then integrated with vestibular system dynamics to evaluate the ISO2631-1 standard motion sickness dose value (MSDV). This study aims to develop a realistic multibody simulation model to be used in comfort analyses of AVs in terms of motion sickness. This paper also demonstrates the impact of using biodynamics models in evaluating motion sickness for simulation environment. Results are quantified by using Simulink Vehicle Dynamics Blockset by comparing MSDV with and without the multibody model for several case scenarios (i.e. double lane change test, constant radius, increasing steer).

This paper deals with the energy efficiency of cooperative cruise control technologies when considering vehicle strings in a realistic driving environment. In particular, we design a cooperative longitudinal controller using a state-of-the-art model predictive control (MPC) implementation. Rather than testing our controller on a limited set of short maneuvers, we thoroughly assess its performance on a number of regulatory drive cycles and on a set of driving missions of similar length that were constructed based on real driving data. This allows us to focus our assessment on the energetic aspects in addition to testing the controller’s robustness. The analyzed controller, based on linear MPC, uses vehicle sensor data and information transmitted by the vehicle driving the string to adjust the longitudinal trajectory of the host vehicle to maintain a reduced inter-vehicular distance while simultaneously optimizing energy efficiency. To keep our controller as close as possible to a real-life deployable technology, we also consider passenger comfort in our MPC design, which is a relevant aspect that is often a conflicting objective with respect to energy efficiency. Our simulation scenario is characterized by a homogeneous string of three battery electric vehicles and was modelled in a MATLAB/Simulink environment. An extensive set of simulation experiments forms the basis for our discussion on the energy-saving potential of cooperative driving automation systems.

CAN bus network proved to be efficient and dynamic for small compact cars as well as heavy-duty vehicles (HDV). However, HDVs are more susceptible to malicious attacks due to lack of security in their intra-vehicle communication protocols. SAE proposed a new standard named J1939-91C for CAN-FD networks which provides methods for establishing trust and securing mutual messages with optional encryption. J1939-91C ensures message authenticity, integrity, and confidentiality by implementing complex cryptographic operations including hash functions and random key generation. In this paper, the three main phases of J1939-91C, i.e., Network Formation, Rekeying, and Message Exchange, are simulated and tested on Electronic Control Units (ECUs) supporting CAN-FD network. Numerous test vectors were generated and validated to support SAE J1939-91C. The mentioned vectors were produced by simulating different encryption and hashing algorithms with variable message and key lengths. Moreover, the output vectors of each phase were passed to the subsequent phase to build up a complete scenario. The obtained simulation results will be used in the future for assessing the benefits of the standard as well as identifying potential strengths or possible shortcomings of the new protocol and to help suggest recommended enhancements and modifications.

Considering the change of vehicle future power demand in the process of energy distribution can improve the fuel saving effect of hybrid system. However, current studies are mostly based on historical information to predict the future power demand, where it is difficult to guarantee the accuracy of prediction. To tackle this problem, this paper combines hybrid energy management with predictive cruise control, proposing a hierarchical control strategy of predictive energy management (PEM) that includes two layers of algorithms for speed planning and energy distribution. In the interest of decreasing the energy consumed by power components and ensuring transportation timeliness, the upper-level introduces a predictive cruise control algorithm while considering vehicle weight and road slope, planning the future vehicle speed during long-distance driving. The lower-level calculates the future power demand based on the results of speed planning, and a dynamic programming method is utilized to determine the global optimal power distribution rules for the current road and driving condition with the goal of optimal engine fuel consumption. The comparison of simulation and vehicle test results indicates that under the various high-speed cruising conditions with little change in speed range and road slope, the predictive energy management strategy has a significant improvement in fuel saving compared with the rule-based energy management strategy.

This paper presents an analysis on the position of driver eye height as a function of their standing height, weight, biological sex, seat back angle and seat bottom angle. Typically, eye heights are estimated based on standing height, or measured from a rigid seated position with a vertical seat back. While reasonably close, these estimated eye heights are generally not correct for individuals seated in deformable vehicle seats with non-vertical seat back angles. Thus, these measurements tend to overestimate the participants eye height in more ecologically probable scenarios, such as driver eye height while operating a vehicle. In this study, eye measurements were taken from a standing position and while seated on a rigid surface and then compared to the same participant’s eye height measured while seated on six different representative vehicle seats with seat back angles of 20, 25, and 30 degrees respectively. Furthermore, this study also measured for-aft positioning of the participant’s eyes relative to the anterior edge of the head restraint at each seat position. This study utilized three pickup truck seats, two light passenger vehicle seats, and one sport utility vehicle seat. This data outlines a methodology that can be used to estimate eye positions of other adult humans in deformable vehicle seats when only their standing height, weight, and biological sex are known, or even more precisely when general seat geometry is also known. This allows for more accurate recreations of first-person point-of-view lines of sight or visual obstruction analysis.

This paper describes a hierarchical motion planning and control framework for overtaking maneuvers under racing circumstances. Unlike urban or highway autonomous driving conditions, race track driving requires longer prediction and planning horizons in order to respond to upcoming corners at high speed. In addition, the subject vehicle should determine the optimal action among possible driving modes when opponent vehicles are present. In order to meet these requirements and secure real time performance, a hierarchical architecture for decision making, motion planning, and control for an autonomous racing vehicle is proposed. The supervisor determines whether the subject vehicle should stay behind the preceding vehicle or overtake, and its direction when overtaking. Next, a high level trajectory planner generates the desired path and velocity profile in a receding horizon fashion. In order to reduce the computational burden despite maintaining a sufficiently long planning horizon, a low fidelity kinematic vehicle model is utilized for the planner. Finally, a model predictive control (MPC) based low level trajectory tracker generates the lateral and longitudinal control inputs. A high fidelity dynamic vehicle model is introduced to provide a high quality control input for a relatively short control horizon compared with the motion planner. Simulation results demonstrate that the proposed algorithm successfully controls the subject vehicle to deal with preceding opponent vehicles under racing track environments.

In this paper, tensile experiments were performed on the dorsal and ventral skin of rats, and the mechanical properties of the skin in these two sites were compared and analyzed.A three-factor experimental protocol of site (dorsal and ventral), strain rate (0.71s-1, 7.1×10-3s-1), and sampling orientation (0°, 45° and 90° relative to the spine) was established for tensile test using the L6(31×22) orthogonal table modified from the standard orthogonal table L4 (23). Uniaxial tensile experiments were performed on rat skin samples to calculate the stress-strain curve. The failure strain energy was selected as the index, and the sum of squared deviations of the factors to the index was calculated by analysis of variance (ANOVA), and the contributions of the factors to the failure strain energy were evaluated.The results showed that the site factor has the largest effect on the tensile strain energy with a contribution of 88.9% and a confidence level of 95%. The strain rate has a small effect on tensile strain energy with a contribution of 10.4% and a confidence level of 90%. Orientation makes almost no contribution to the tensile strain energy. The mean index value of the dorsal skin was 6.492, which was 3.5 times higher than that of the ventral skin. The mean index value of the skin at high strain rates was 4.970, which was 1.5 times higher than that at low strain rates. There was no significant difference in the mean index value among the three orientations.

This paper addresses a three dimensional (3D) mission domain coverage control problem combined with camera pose control to align towards specific objects of interest. We consider an unmanned ground vehicle (UGV) based on a unicycle kinematics model with an onboard camera sensor based on a visual perspective sensor model. The coverage control problem has been researched in large part for planar domains, which is however not sufficient for real world applications for UGV navigation. Furthermore, in contrast to coverage control of points in the environment, when dealing with objects of interest, it is more amicable to consider that there exist certain orientations to which the camera must align itself to properly cover the object and make ‘sense’ of it. Hence, we seek to derive both UGV coverage control law for 3D mission domains and onboard camera pose control considering target orientation. The goal is to allow the UGV to survey each point in the environment to a preset level while also controlling the camera to align and look towards objects of interest, thereby increasing situational awareness. Analytic control laws for the UGV are formulated based on a Lyapunov error function defined in a time-varying 3D domain. L1 and L2 rotation averaging techniques are applied and analyzed for multiple target points and are coupled with a convergence function to give analytic control laws on the manifold for the camera pose. Finally, the control laws are verified and simulated in CoppeliaSim using a Python API and the results are analyzed and discussed.

This investigation focuses on conventional powertrain technologies that provide operational synergy based on customer utilization to reduce fuel consumption for a heavy-duty, nonroad (off-road) material handler. The vehicle of interest is a Pettibone Cary-Lift 204i, with a base weight of 50,000 lbs. and a lift capacity of 20,000 lbs. The conventional powertrain consists of a US Tier 4 Final diesel engine, a non-lockup torque converter, a four-speed powershift automatic transmission, and all-wheel drive. The paper will present a base vehicle energy/fuel consumption breakdown of propulsion, hydraulic and idle distribution based on a representative end-user drive cycle. The baseline vehicle test data was then used to develop a correlated lumped parameter model of the vehicle-powertrain-hydraulic system that can be used to explore technology integration that can reduce fuel consumption. Two conventional powertrain modifications are explored that provide potential pathways that significantly alter the base powertrain and include 1.) a torque converter disconnect clutch and 2.) a low voltage stop-start system that have the potential to reduce fuel consumption on the end user representative drive cycle by 10.3% and 9.8%, respectively. Details of how the powertrain modifications would be executed, physical hardware, and application to other heavy-duty nonroad vehicle applications are included in the discussion.

As compared with other batteries, lithium-ion batteries are featured by high power density, long service life, high energy density, environmental friendliness and thus have found wide application in the area of consumer electronics. However, lithium-ion batteries for electric and hybrid electric vehicles (EVs and HEVs) have high capacity and large serial-parallel numbers, which, coupled with such problems as safety, durability, cost and uniformity, imposes limitations on the wide application of lithium-ion batteries in the EVs and HEVs. The narrow area in which lithium-ion batteries operate with safety and reliability necessitates the effective control and through the use of management of battery management system. Battery state of health (SOH) monitoring has become a crucial challenge in EVs and HEVs research, as SOH significantly affects the overall vehicle performance and life cycle. This paper presents both cycling and calendar aging at high and low temperatures. In the proposed model the calendar aging is represented as a function of time, storage temperature and state-of-charge (SOC). On the other hand, cycle aging is represented as a function of energy throughput, cell temperature and C-rate. The cycling and calendar aging models are later combined to formulate an empirical cell-level aging model. The performance of the cell-level empirical aging model is validated by comparing it with the aging test data for different driving scenarios. The aging prediction from the empirical aging model shows very good agreement with the test data with a maximum normalized root mean squared error (RMSE) of 0.85%

With the popularity of automated vehicles, the future mixed traffic flow contains automated vehicles with different degrees of intelligence developed by other manufacturers. Therefore, simulating the interaction behavior of automated vehicles with varying levels of intelligence is crucial for testing and evaluating autonomous driving systems. Since the algorithm of traffic vehicles with various intelligence levels is difficult to obtain, it leads to hardships in quantitatively characterizing their interaction behaviors. Therefore, this paper designs a new automated vehicle test platform to solve the problem. The intelligent vehicle testbed with multiple personalized in-vehicle control units in the loop consists of three parts: 1. Multiple controllers in the loop to simulate the behavior of traffic vehicles；2. The central console applies digital twin technology to share the same traffic scenario between the tested vehicle and the traffic vehicle, creating a mixed traffic flow. 3. The tested vehicle can be either a virtual or an actual vehicle, i.e., an actual vehicle in the loop used to represent the effects of vehicle dynamics, actuators, and other characteristics.

Prospective combustion engine applications require the highest possible energy conversion efficiencies for environmental and economic sustainability. For conventional Spark-Ignition (SI) engines, the quasi-hemispherical flame propagation combustion method can only be significantly optimized in combination with high excess air dilution or increased combustion speed. However, with increasing excess air dilution, this is difficult due to decreasing flame speeds and flammability limits. Pre-Chamber (PC) initiated jet ignition combustion systems significantly shift the flammability and flame stability limits towards higher dilution areas due to high levels of introduced turbulence and a significantly increased flame area in early combustion stages, leading to considerably increased combustion speeds and high efficiencies. By now, vehicle implementations of PC-initiated combustion systems remain niche applications, especially in combination with lean mixtures. This is also due to challenges regarding cold-start, combustion stability at low loads, and emissions. Nevertheless, PC ignition systems allow overall engine efficiencies >45%. Therefore, a market launch of an engine using globally lean mixtures ignited by a PC system is desirable. This requires a fast-running and predictive physical model to conduct robust design studies and complement existing testing methodologies (3D-CFD, experimental). This paper addresses the development of a quasi-dimensional burn rate model for PC ignition combustion systems. The presented modeling approach combines the well-established two-zone entrainment model (main-chamber) with a semi-empirical PC model that aims to detect the PC influence on the main-chamber combustion. Dedicated models predict the impact of the jet-induced turbulence and the increased flame area. The models are integrated into the so-called cylinder module developed at IFS (Institute of Automotive Engineering Stuttgart). For the model validation, measurement data of a single-cylinder research engine using different fuels (E1001, RON95E102), loads (IMEP = 6 − 15 bar), excess air dilutions (λ = 1 − 2) and compression ratios (16.41, 12.62) are used, showing a satisfactory prediction of the burn rate and pressure curve.

The catalyzed diesel particulate filter with Pt and Pd noble metals as the main loaded active components are widely used in the field of automobile engines, but the high cost makes it face huge challenges. Rare earth element doping can improve the soot oxidation performance of the catalyzed diesel particulate filter and provide a new way to reduce its cost. In this paper, thermogravimetric tests and chemical reaction kinetic calculations were used to explore the effect of Pt-Pd catalysts doped Ce, and La rare earth elements on the oxidation properties of soot. The results shown that, among Pt-Pd-5%Ce, Pt-Pd-5%La, and Pt-Pd-5%Ce-5%La catalysts, Pt-Pd-5%La catalyst has the highest soot conversion, the highest low-temperature oxidation speed, and the activation energy is the smallest. Compared with soot, this catalyst reduced T10 and T20 by 82% and 26%, respectively, meaning the catalytic activity of Pt-Pd-5%La catalyst was the best. With the decrease of catalyst/soot ratios, the soot conversion and oxidation speed of Pt-Pd and Pt-Pd-5%La catalysts decreased, and characteristic temperature increased. In both catalyst formulations, samples with catalyst/soot ratio of 5 showed the best catalytic activity, and the other samples with smaller catalyst/soot ratios showed less difference. The study revealed the influence of doping elements and catalyst/soot ratios on the oxidation characteristics and reaction kinetics of soot, which has a guiding significance for optimizing the doping scheme of rare earth elements and realizing the reduction of noble metals.

Accelerating product development cycles and incentives to reduce costs in product development are strong motivators to move to virtual development and validation processes. Challenges to moving to a virtual paradigm include a wealth of historical data and context for hardware tests, uncertainty over dependencies, and a lack of a clear path of transition to virtual methods. In this paper we will discuss approaches to understanding the value created by hardware tests and aligning that value to virtual processes. We will also discuss the need for a virtual context to be added to SAE J1739 [1] (DFMEA detection criteria), and how to create paths to maximize the value of virtual assessments. Finally, we will also discuss the cultural and organizational changes required to support.

E-mobility is creating more challenges and great opportunities for automotive textile industries to bring out new textiles for light weight, more aesthetic, better feel, sustainable and biomaterial to meet the customer perception. Textiles allows a more design freedom to in terms of construction, weaving and wrapping solutions. A hard rough plastic surface could be transferred into a more pleasant soft touch surface by a simple wrapping with textiles. The introduction of electric vehicle will convert the car as more silent as it replaces the engine by motor and battery mechanism. The more silent is the car, the more silent is the BSR behavior of the material. This work discloses of a polyester textile developed to meet automotive lightweight to strength requirements with its new nonwoven construction for seat insert and bolster application which demands for high breaking strength, abrasion resistance, stretch and set and soiling resistance. This textile also studied for thermal resistance and sound dampening and results were analyzed using DSC, TGA and DMA analysis techniques. This paper also demands the requirements of advanced textiles for future mobility.

Machine Learning (ML) components are widely adopted in autonomous vehicles to perform tasks such as perception and planning. Despite the multiple uses of machine learning components and their benefits, incorrect outputs from machine learning components can compromise the safety of the system. The limitations of the machine learning algorithms and their acceptable level of performance that results in a reasonable level of residual risk are considered as a part of ISO 21448, the safety of the intended functionality (SOTIF) standard. Currently, to measure the performance of machine learning models, statistical metrics such as accuracy, recall, precision, and F1-measure are often used depending on the nature of the data and task. While these metrics help in understanding which machine learning model is better and can be chosen as a part of the vehicle’s architecture, they do not provide much information regarding safety, in particular, SOTIF. There is a need for new metrics to better assess safety corresponding to these machine learning models. The new metrics need to focus more if an incorrect output from the model results in crashes and near crashes and aid in proposing design changes that help to reduce the residual risk of the vehicle. To achieve this goal, in this paper we discuss the limitation of current metrics with an example architecture that uses machine learning models and propose new scenario-based metrics that help in better analysis of machine learning models for SOTIF.

With the growing prosperity of the long-distance freight and urban logistics industry, the demand for cargo trucks is gradually increasing. The connecting bracket is the critical connecting part of the truck chassis, which bears the load transmitted by the road excitation and reduces the damage to the frame caused by the load. However, the occurrence of rough road conditions is inevitable in heavy-duty transportation. In this paper, road durability tests and fatigue life analysis are carried out on the original structure to ensure the safety of the vehicle. Based on the known boundary and load constraints, a lightweight and high-performance structure is obtained through size optimization, as the original structure cannot meet the performance requirements. Firstly, the road test was conducted on the truck where the original bracket structure is located. The stress response spectrums under fatigue simulation were obtained by combining the road test load spectrums at the excitation points. Moreover, according to the von Mises yield criterion or Tresca yield criterion, the stress-strain in the multiaxial state is equated to the stress-strain in the uniaxial state and made as the fatigue damage parameter. Then the cumulative damage value was carried out by adopting the fatigue damage accumulation theory based on Miner's rule. Secondly, a new connecting bracket layout is designed and optimized by size optimization with multiple performance constraints. Finally, fatigue life prediction and road durability tests were conducted based on the optimized design. The fatigue analysis and road durability test results show that the optimized bracket has better stiffness, strength, and fatigue performance than the original bracket.

Electric vehicle sharing (EVS) can alleviate traffic congestion and reduce emissions. However, the poor user experience and lack of word-of-mouth effect lead to the low utilization rate of EVS in China. Based on the demand and pain points of EVS, this paper concentrates on travel mode choice behavior of consumers under social networks and establishes an agent-based model for EVS diffusion. The results show that: (1) Social networks can promote the diffusion of EVS and the number of opinion leaders and the number of fans of opinion leaders play an important role. (2) Consumers are more sensitive to travel costs than non-travel time now, but with the improvement of demand for travel experience, consumers are more concerned with non-travel time. (3) The non-travel time of EVS needs to be reduced to 9, 8 and 7 minutes respectively to retain users when the travel cost increases to 0.7, 0.8 and 0.9 Yuan/minute.

The Daimler Detroit AssuranceⓇ 4.0 collision mitigation system is able to assist a driver in various aspects of safely operating their vehicle. One capability is the Active Brake Assist (ABA), which uses the Video Radar Decision Unit (VRDU) to communicate with the front bumper-mounted radar to provide information about potential hazards to the driver. The VRDU may warn the driver of potential hazards and apply partial or full braking, depending on the data being gathered and analyzed. The VRDU also records event data when an ABA event occurs. This data may be extracted from the VRDU using Detroit DiagnosticLink software. This paper presents an overview of the VRDU functionality and examines aspects of VRDU data such as the range and resolution of data elements, the synchronicity or timing of the recorded data, and application of the data for use in the analysis of crashes. Various tests were performed using a truck equipped with Detroit AssuranceⓇ 4.0 in a manner designed to trigger VRDU records. VRDU data was compared to data collected from the J1939 CAN Bus and from a Racelogic VBOX 3i 100 Hz GPS Data logger.

Published data relevant to snowmobile crash reconstruction is comparatively limited, especially pertaining to mountain snowmobiling and riding in deep snow. Snowmobiling is a unique motorsport activity as it requires substantial rider input and physical interaction to properly control the vehicle. The added complexities of varying slope angle and snow depth in mountain terrain make application of test data from testing done on flat surfaces less useful when applied to sloped terrain analysis.New data from testing performed in deep snow conditions on various slopes is presented in this paper. Acceleration tests were performed using two late model mountain snowmobiles from a stop on various slope angles. Additional related factors such as snow density, trenching, and snow mass momentum exchange are also discussed. Comparison of these test results to previously published snowmobile testing data advances the understanding of snowmobile acceleration parameters into mountain terrain.Significant changes in acceleration performance can result from small changes in slope. Even for the most advanced mountain snowmobiles in loose snow, acceleration becomes more limited as slope increases. Acceleration results are comparable to earlier data, irrespective of snowmobile horsepower and snow conditions. In earlier published data, flat terrain accelerations on packed snow in the range of 0.60g were recorded. In this testing, 0.65 was at the low end of the data, with average full-throttle accelerations of between 0.65g and 0.75g being achieved by both snowmobiles on flat undisturbed powder snow. Keeping in mind that these are different machines, the findings show that traction and horsepower are as significant as snow characteristics when analyzing snowmobile performance.

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. To distinguish this model from others, it has inputs for the local weather and electrical grid pricing forecasts. By providing this information, the charging controller can determine when the best time to charge the battery will be to ensure the lowest pricing, while efficiently maintaining an appropriate battery temperature during the charge cycle. Timing of the charge can be crucial as the controller needs to establish whether letting the battery cool after driving, then heating to charge during cheaper hours is more cost effective than charging during peak hours directly after a drive cycle while the battery is still warm.The methods used to accomplish the primary objective also help for the secondary objective by ensuring the correct battery operating temperature window during charging. During the storage phase of the vehicle, the controller can monitor the battery temperature and forecast to ensure an appropriate temperature. By controlling the temperature and charging cycles this way, battery ageing should be reduced due to the direct correlation between temperature and SEI growth.

This paper provides an overview of possible engine design optimizations by utilizing highly knock-resistant potential greenhouse gas (GHG) neutral synthetic fuels. Historically the internal combustion engine was tailored to and highly optimized for fossil fuels. For future engine generations one of the main objectives is to achieve GHG neutrality. This means that either carbon-free fuels such as hydrogen or potential greenhouse gas neutral fuels are utilized. The properties of hydrogen make its use challenging for mobile application as it is very diffusive, not liquid under standard temperature/pressure and has a low volumetric energy density. C1-based oxygenated fuels such as methanol (MeOH), dimethyl carbonate (DMC) and methyl formate (MeFo) have properties like conventional gasoline but offer various advantages. Firstly, these fuels can be produced with renewable energy and carbon capture technologies to be GHG neutral. Secondly, the C1-based fuels burn with significantly less pollutant emissions. A third advantage is the high knock resistance of those fuels. This inherits a drastic efficiency potential for spark ignition engines as the compression ratio and therefore the potential thermal efficiency can be directly increased. In the single cylinder engine, a compression ratio (CR) of ~20:1 is investigated proving the high knock resistance as well as the efficiency potential of MeOH and a mixture containing 65 vol% DMC and 35 vol% MeFo (C65F35). Special attention is paid to the direct injection strategy, which utilizes up to quadruple injections and 35MPa fuel pressure. Later on, a more moderate CR increase to 15:1 with a CFD optimized piston design is investigated at a state of the art four-cylinder engine (4CE) utilizing C65F35. The whole engine map is presented proving the real-world usability and efficiency potential of this fuel type in combination with the optimized piston. WLTC and RDE tests were performed, underling both the practicality and the efficiency potential in dynamic conditions. The 4CE tests are rounded off by showcasing the potential of lean operation with two different high-energy ignition systems (Corona and passive pre-chamber ignition). The performance investigation on both engines is accompanied by emission measurements utilizing standard exhaust analyzers, an FTIR-device and particle number counting systems.