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The transition from ICE to electric power trains in new vehicles along with the application of advanced active and passive noise reduction solutions has intensified the perception of noise sources not directly linked to the propulsion system. This includes road noise as amplified by the tire cavity resonance. This resonance mainly depends on tire geometry, air temperature inside the tire and vehicle speed and is increasingly audible for larger wheels and heavier vehicles, as they are typical for current electrical SUV designs. Active technologies can be applied to significantly reduce narrow band tire cavity noise with low costs and minimal weight increase. Like ANC systems for ICE powertrains, they make use of the audio system in the vehicle. In this paper, a novel low-cost system for road induced tire cavity noise control (RTNC) is presented that reduces the tire cavity resonance noise inside a car cabin.

Understanding left-turn vehicle-pedestrian accident mechanisms is critical for developing accident-prevention systems. This study aims to clarify the features of driver behavior focusing on drivers’ gaze, vehicle speed, and time to collision (TTC) during left turns at intersections on left-hand traffic roads. Herein, experiments with a sedan and light-duty truck (< 7.5 tons GVW) are conducted under four conditions: no pedestrian dummy (No-P), near-side pedestrian dummy (Near-P), far-side pedestrian dummy (Far-P) and near-and-far side pedestrian dummies (NF-P). For NF-P, sedans have a significantly shorter gaze time for left-side mirrors compared with light-duty trucks. The light-duty truck’s average speed at the initial line to the intersection (L1) and pedestrian crossing line (L0) is significantly lower than the sedan’s under No-P, Near-P, and NF-P conditions, without any significant difference between any two conditions.

Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems can be problematic, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to HVEDRs to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly captured and injected into the HVEDR to trigger an event record.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

In recent years, with the development of computing infrastructure and methods, the potential of numerical methods to reasonably predict aerodynamic noise in turbocharger compressors of heavy-duty diesel engines has increased. However, aerodynamic acoustic modeling of complex geometries and flow systems is currently immature, mainly due to the greater challenges in accurately characterizing turbulent viscous flows. Therefore, recent advances in aerodynamic noise calculations for automotive turbocharger compressors were reviewed and a quantitative study of the effects for turbulence models (Shear-Stress Transport (SST) and Detached Eddy Simulation (DES)) and time-steps (2° and 4°) in numerical simulations on the performance and acoustic prediction of a compressor under various conditions were investigated.

Considering the current trend towards the electrification of commercial vehicles, the development of Beam eAxle solutions has become necessary. The utilization of an electric drive unit in heavy-duty solid axle-based commercial vehicles presents unique and demanding challenges. These include the necessity for elevated peak and continuous torque while meeting packaging constraints, structural integrity requirements, and extended service life. One such solution was developed by BorgWarner to address these challenges. This paper offers a comprehensive overview of the design and development process undertaken for this Dual Motor Beam eAxle system. This includes the initial comparison of various eAxle solutions, the specifications of components selected for this design, and the initial results from dyno and vehicle development.

By installing an automated mechanical transmission (AMT) on heavy-duty vehicles and developing a reasonable shift strategy, it can reduce driver fatigue and eliminate technical differences among drivers, improving vehicle performance. However, after detaching from the experience of good drivers, the current shifting strategy is limited to the vehicle state at the current moment, and cannot make predictive judgment of the road environment ahead, and problems such as cyclic shifting will occur due to insufficient power when driving on the ramp. To improve the adaptability of heavy-duty truck shift strategy to dynamic driving environments, this paper first analyzes the shortcomings of existing traditional heavy-duty truck shift strategies on slopes, and develops a comprehensive performance shift strategy incorporating slope factors. Based on this, forward-looking information is introduced to propose a predictive intelligent shift strategy that balances power and economy.

Determining occupant kinematics in a vehicle crash is essential when understanding injury mechanisms and assessing restraint performance. Identifying contact marks is key to the process. This study was conducted to assess the ability to photodocument the various fluids on different vehicle interior component types and colors with and without the use of ultraviolet (UV) lights. Biological (blood, saliva, sweat and skin), consumable and chemical fluids were applied to vehicle interior components, such as seatbelt webbing, seat and airbag fabrics, roof liner and leather steering wheel. The samples were photodocumented with natural light and UV light (365 nm) exposure immediately after surface application and again 14 days later. The review of the photos indicated that fabric type and color were important factors. The fluids deposits were better visualized on non-porous than porous materials. For example, blood was better documented on curtain airbags than side or driver airbags.

Bicycle computers record and store global position data that can be useful for forensic investigations. The goal of this study was to estimate the absolute error of the latitude and longitude positions recorded by a common bicycle computer over a wide range of riding conditions. We installed three Garmin Edge 530 computers on the handlebars of a bicycle and acquired 9 hours of static data and 96 hours (2214 km) of dynamic data using three different navigation modes (GPS, GPS+GLONASS, and GPS+Galileo satellite systems) and two geographic locations (Vancouver, BC, Canada and Orange County, CA, USA). We used the principle of error propagation to calculate the absolute error of this device from the relative errors between the three pairs of computers. During the static tests, we found 16 m to 108 m of drift during the first 4 min and 1.4 m to 5.0 m of drift during a subsequent 8 min period. During the dynamic tests, we found a 95th percentile absolute error for this device of ±8.04 m.

This paper investigates the tire-road interaction for tires equipped with two different solid rubber material definitions within a Finite Element Analysis virtual environment, ESI PAMCRASH. A Mixed Service Drive truck tire sized 315/80R22.5 is designed with two different solid rubber material definitions: a legacy hyperelastic solid Mooney-Rivlin material definition and an Ogden hyperelastic solid material definition. The popular Mooney-Rivlin is a material definition for solid rubber simulation that is not built with element elimination and is not easily applicable to thermal applications. The Ogden hyperelastic material definition for rubber simulations allows for element destruction. Therefore, it is of interest and more suited for designing a tire model with wear and thermal capabilities.

Reconstruction of inline crashes between vehicles with a low closing speed, so-called “low speed” crashes, continues to be a class of vehicle collisions that reconstructionists require specific methods to handle. In general, these collisions tend to be difficult to reconstruct due primarily to the lack of, or limited amount of, physical evidence available after the crash. Traditional reconstruction methods such as impulse-momentum (non-residual damage based) and CRASH3 (residual damage based) both are formulated without considering tire forces of the vehicles. These forces can be important in this class of collisions. Additionally, the CRASH3 method depends on the use of stiffness coefficients for the vehicles obtained from high-speed crash tests. The question of the applicability of these (high-speed) stiffness coefficients to collisions producing significantly less deformation than experimental crashes on which they are generated, raises questions of the applicability.

Customer expectations for cabin tractors from comfort perspective has grown multifold in the recent years. Cabin noise and vibration is one of the crucial parameters which drives comfort feel for the customer. This would enable customer to remain comfortable during long working hours. Moreover, Cabin Tractors with lesser noise levels found to have better acceptance by Customers all over the world. The Nebraska reports studied for US based tractor shows evolution of trend of cabin noise in 80-100HP tractors in last couple of decades. Undoubtedly, tractor manufacturers have put lot of efforts towards meeting legislation demand & stringent customer requirements for European and US market. This paper presents the work carried out for investigating major contributing factors affecting tractor cabin Noise & Vibration for a Noisy cabin tractor.

For commercial vehicles, reliability is key since the vehicle is typically linked to the daily earnings of the owner. To ensure continuous vehicle operation, early diagnostics of critical issues and proactive maintenance are important. However, an electric vehicle is a complex and dynamic system consisting of numerous components interacting with each other and with external environments such as road conditions, traffic, weather, and driving behavior. Thus, vehicle operation and performance are highly contextual and for identifying an abnormal operation (diagnostics) the solution must consider the conditions under which it is driven. To address this, the paper proposes an AI-based digital twin of an electric three-wheeler vehicle. TabNet a deep-learning based model is used to learn and generate near-ideal vehicle behavior. The focus of the paper is motor subsystem. The model is trained using appx 200 vehicles first 1500 km driven data.

India striving for carbon neutrality influences futures powertrain architecture of commercial vehicles. The use of CO2-free drives as battery electric have been demonstrated for various applications. The productivity still is a challenge due to missing high power charging infrastructure or limited range. This draws the attention to the use of sustainable fuels due to lower refueling times. The hydrogen engine got highest attention in the last couple of years. For markets as the EU the driver for hydrogen is the CO2 emission reduction, whereas for markets as India hydrogen offers the additional opportunity for more independence from fossil imports. Different OEMs all over the world have converted diesel engines to hydrogen operation with strong focus on performance and emission demonstration, so far with limited technology readiness of different key components.

The auto industry is one of the major contributors for noise pollution in urban areas. Specifically, highly populated heavy commercial diesel vehicle such as buses, trucks are dominant because of its usage pattern, and capacity. This noise is contributed by various vehicle systems like engine, transmission, exhaust intake, tires etc. When the pass by noise levels exceeds regulatory limit, as per IS 3028, it is important for NVH automotive engineer to identify the sources & their ranking for contribution in pass by noise. The traditional methods of source identification such as windowing technique, sequential swapping of systems and subsystems which are time consuming.Also advanced method in which data acquisition with a synchronizing technology like telemetry or Wi-Fi for source ranking are effective for correctness.However they are time and resource consuming, which can adversely impact product development timeline.

The commercial vehicle sector (especially trucks) has major role in economic growth of a nation. With improving infrastructure, increasing number of commercial vehicles and growing amount of Vulnerable Road Users (VRUs) on roads, accidents are also increasing. As per RASSI (Road Accident Sampling System India) FY2016-21 database, commercial vehicles are involved in 43% of total accidents on Indian roads. One of the major causes of these accidents is Driver Drowsiness and Inattention (DDI) (approx. 10% contribution in total accidents). This paper describes novel driver-in-loop performance assessment methodology for comprehensive verification of Driver Monitoring System (DMS) for commercial vehicle application. Novelty lies in specification of test subjects, driving styles and variety of road traffic scenarios for verification of DMS system. Test setup is made modular to cater to different platform environments (Heavy, Intermediate, Light) with minor modifications.

For safety towards pedestrians and other road-vehicles, sound alert systems, like horns, have been in use since development of Automobiles. On the same line but with special purpose of preventing a Driver to sleep in a running vehicle, a Driver Monitoring System [DMS] is recently developed. For Electric Vehicles which are very quiet during starting and driving till 20 km/hr, Acoustic Vehicle Alert System (AVAS) is deployed in Europe and likely to be implemented in India soon. For all these Alert Devices, there are Standard Regulations across the world: ECE R-28 for Horns and ECE R-138 for AVAS. They, however, define only lower and upper dBA limits of the sound radiated by them. For DMS, no such regulation exists till today. With this, there has been a long time debate on what an optimal dBA level should be there, for all road-users as well as for vehicle- users inside the cabin.

Electric car markets experience exponential growth. As per IEA battery electric vehicles sales exceeded 10 million in 2022 [1] . There is projection from IEA that EV sales will touch 40 million mark by 2030, major contribution from China (12 m) and Europe (13.3 m) regions [2]. This growth projection attributed to many global factors, government policies, automakers commitment, climate change, etc. There is a massive push from global institutions and automobile community for transition to electric mobility. There is a 66% likelihood that the annual average near-surface global temperature between 2023 and 2027 will be more than 1.5°C above pre-industrial levels for at least one year. There is a 98% likelihood that at least one of the next five years, and the five-year period, will be the warmest on record [3]. Hence transition is imperative to reduce greenhouse gases and adhere to climate change commitments. Today EVs are not popular as ICE.

Road infrastructure in India is being upgraded at a rapid pace. Quality of life of people has also improved significantly in the last decade. Such trends have significantly impacted design of commercial vehicles and vehicular systems in the country. This paper deals with the design and development of a modern futuristic instrument panel for trucks. Methodology to arrive at product features and solutions which retain their novelty and appeal for a longer term has also been illustrated. Regulatory scenario, modularity, HMI, Perceived Quality, Driver Comforts, evolving technologies, trends and materials are some of the considerations which have discussed in detail. International benchmarks and customer requirement have been analyzed for setting Performance targets. A digital approach for evaluating these considerations evolved during the design and development process has been elaborated in detail.

Automotive crash data analysis and reconstruction is vital for ensuring automotive safety. The objective of vehicle crash reconstruction is to determine the vehicle's motion before, during, and after the crash, as well as the impact on occupants in terms of injuries. Simulation approaches, such as PC CrashTM, have been developed to understand pre-crash and post-crash vehicle motion, rather than the crash phase behavior. Over the past few decades, crash phase simulations have utilized vehicle finite element models. While multibody simulation tools are suitable for crash simulations, they often require detailed crash test data to accurately capture vehicle behavior, which is not always readily available. This paper proposes a solution to this limitation by incorporating crash test data from databases, such as NHTSA, Global NCAP, consumer rating reports, and videos, along with a multibody-based approach, to conduct crash phase simulations.