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The pace of innovations in battery development is revolutionizing the landscape and opportunities for energy storage applications leading to a stronger market segmentation enabling a better suitability to fulfill specific application requirements. For automotive applications, several approaches to increase energy densities, to improve fast charging performance, and to reduce cost on a pack level are considered. Among them, a promising example is the direct integration of battery cells into the battery pack (Cell-to-pack; CTP) or vehicle (Cell-to-chassis, CTC) to increase energy densities and to reduce costs, as already commercialized by Tesla, CATL and others. In the pack development, especially Asian players are one of the frontrunners, where e.g., hybrid cell battery systems with a mixture of cells with different cathode chemistries as introduced by NIO, are experiencing a high interest of the market.

This course is verified by Probitas as meeting the AS9104/3A requirements for Continuing Professional Development. Project Management and Advanced Product Quality Planning (APQP) are two critical techniques used in product development in the mobility industry today. This course will bring these techniques together in an easy to understand format that goes beyond the typical concept of constructing timelines and project planning, by exploring not only the Automotive APQP process, but also key aspects of PM processes.

Abstract This study demonstrates the defossilized operation of a heavy-duty port-fuel-injected dual-fuel engine and highlights its potential benefits with minimal retrofitting effort. The investigation focuses on the optical characterization of the in-cylinder processes, ranging from mixture formation, ignition, and combustion, on a fully optically accessible single-cylinder research engine. The article revisits selected operating conditions in a thermodynamic configuration combined with Fourier transform infrared spectroscopy. One approach is to quickly diminish fossil fuel use by retrofitting present engines with decarbonized or defossilized alternatives. As both fuels are oxygenated, a considerable change in the overall ignition limits, air–fuel equivalence ratio, burning rate, and resistance against undesired pre-ignition or knocking is expected, with dire need of characterization.

Discover the highly anticipated Second Edition to the Amazon #1 Best Seller, The Future of Public Transportation. Delve into 30 expertly crafted chapters brimming with insights from leading public transportation figures. From hydrogen-fueled buses to AI-driven advancements and cybersecurity, this book offers an unparalleled glimpse into the future of transit. Navigate the complexities of transit planning in a post-COVID world, where innovative solutions are essential to tackle infrastructure challenges and workforce shortages. Learn how AI is revolutionizing transit planning, enhancing outcomes for riders. Explore cutting-edge transit technology, including autonomous vehicles and zero-emission initiatives, with a focus on sustainability and customer experience. Whether you're a seasoned professional or new to the field, this book is your roadmap to success, empowering you to drive positive change in your organization.

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.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.

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.

Wrap around distance (WAD) is an important index to evaluate the contact position between pedestrian head and vehicle, and is also one of the key parameters of pedestrian accident reconstruction. The purpose of this paper is to explore whether the pedestrian headform testcan reflect the distribution of head injury in the real world. Firstly, in order to study the distribution of pedestrian head WAD in road accidents in China, a head WAD prediction model was established using logistic regression based on pedestrian height and vehicle collision speed. Secondly, in order to study the distribution of the risk of severe head injuries among pedestrians in accidents, the frequency of pedestrian head impact and the proportion of pedestrian head injury were counted respectively for sedans and SUVs. Subsequently, a risk curve for severe head injuries was constructed based on the head impact frequency and the proportion of severe injuries, utilizing a method that incorporates joint probability.

Compared to passenger cars, commercial vehicles have relatively high fuel and energy consumption, relatively high average annual driving mileage, and a wide range of use. Therefore, energy-saving management of commercial vehicles is crucial. For multi-axle distributed pure electric drive commercial vehicles, a dynamic allocation control strategy for driving torque based on energy consumption optimization is proposed. First, the basic idea of the driving torque distribution control strategy was analyzed and a relevant mathematical model was established. Then, the offline optimization of the distribution coefficients of the driving torque for each axle was carried out through a genetic algorithm, and the entire vehicle driving force distribution strategy using the distribution coefficients as an online lookup table was determined.

Abstract This study underscores the benefits of refining the intralogistics process for small- to medium-sized manufacturing businesses (SMEs) in the engineer-to-order (ETO) sector, which relies heavily on manual tasks. Based on industrial visits and primary data from six SMEs, a new intralogistics concept and process was formulated. This approach enhances the value-added time of manufacturing workers while also facilitating complete digital integration as well as improving transparency and traceability. A practical application of this method in a company lead to cutting its lead time by roughly 11.3%. Additionally, improved oversight pinpointed excess inventory, resulting in advantages such as reduced capital needs and storage requirements. Anticipated future enhancements include better efficiency from more experienced warehouse staff and streamlined picking methods. Further, digital advancements hold promise for cost reductions in administrative and supportive roles.

This white paper is an examination of the increasing rate of injuries and deaths worldwide of a class of road users, often referred to as “vulnerable road users” (VRUs), with a focus on pedestrians, cyclists, scooterists, highway road workers, safety, and emergency personnel. The potential to leverage pragmatic, evidence-based technology countermeasures to reduce these collisions, and the severity of those that do occur, is also examined.

Abstract The sinking and trimming of the hull in the channel would directly affect the handling and navigation safety of the ship. In view of the ship sinking, a series of empirical formulas to estimate the subsidence have been put forward for vessel in spacious shallow water areas. However, most of the equations are based on seagoing vessels. They are not suitable for inland ships with small scales, shallow drafts, and narrow navigation width. Till now, research on ship squat in intermediate channel has not yielded more practical results. Here, a generalized physical model is used to study the sinking of 500t class ships in restricted intermediate channel under different channel widths, water depths, and speeds. The main factors affecting the squat are analyzed, the empirical relation is compared with the measured squat. The Barrass equation is modified, and the calculation relation of the settlement suitable for inland river ships is proposed.

Machine learning is used for the research and development of ITS services and the rider assistance for on-road motorcycle racing. Meanwhile, rider assistance systems for off-road motorcycles have yet to be developed, partly due to the complexity of the measurement conditions, as described in the previous paper. This research aims to create a reliable AI which is capable of classifying typical jump behaviors in off-road riding by machine learning to create a rider assistance system for off-road motorcycles. Motorcycle manufacturers and certain research institutes use motion sensors to collect data, but the data is obtained from a limited number of vehicles and riders. The creation of a rider assistance system requires a large amount of validation data. Furthermore, it is desirable to achieve the target with data that can be measured in mass-produced vehicles, which will make it possible to collect data even from general users.

The current political push for e-mobility marked a major decline in the R&D interest to internal combustion engine (ICE). Following this global trend, Ford is committed to going 100% electric by 2030 for passenger cars and 2035 for light commercial vehicles. At the same time, many researchers admit that, due to many objective factors, vehicles powered by ICE will remain in operation for decades to come. Development of alternative carbon-neutral fuels can bring a renaissance in the ICE development as practical limitations of electric-only approach get exposed. Since a significant part of energy losses in the ICE comes from friction, engine tribology has been an important research topic over the past two decades and a significant progress in improving the engine efficiency was achieved.

Abstract The ability to predict the durability of a structure depends on the knowledge of operating loads experienced by the structure. Typically, multi-body dynamics (MBD) models are used to cascade measured wheel loads to hard points. However, in this approach, there are many sources by which errors creep into cascaded forces. Any attempt to reduce sources of such errors is time consuming and costly. In typical program development timelines, it is very difficult to accommodate such model calibration efforts. Commercial load cells exist in the industry to give engineers insight into understanding the complex real-world loading of their structures. A significant limitation to the use of load cells is that the structure needs to be modified to accept the load cell, and not all desired loading degrees of freedom (DOFs) can be measured. One of the innovative solutions to calculate operating loads is to convert the structure itself into its own load transducer.

Abstract Articulated vehicles form an important part of our society for the transport of goods. Compared to rigid trucks, tractor-trailer combinations can transport huge quantities of load without increasing the axle load. The fifth wheel (FW) acts as a bridge between the tractor and trailer, which can be moved within the range to achieve rated front and rear axle loads. When the FW is moved front, it adversely affects the cab dynamics and cab suspension forces. Compared to the cab pitch and roll, yaw motion increases drastically. The current study tries to address this issue by providing reaction rod links in the rear cab suspension. In this study, a 4×2 tractor with a three-axle semitrailer is considered by keeping the FW at its frontmost position, which is the worst-case scenario for a cab. Three different cases of reaction rod arrangement and its influence on cab dynamics are studied in comparison with a model without reaction rods.

This SAE Recommended Practice describes a test method for determination of heavy truck (Class VI, VII, and VIII) tire force and moment properties under cornering conditions. The properties are acquired as functions of normal force and slip angle using a sequence specified in this practice. At each normal force increment, the slip angle is continually ramped or stepped. The data are suitable for use in vehicle dynamics modeling, comparative evaluations for research and development purposes, and manufacturing quality control. This document is intended to be a general guideline for testing on an ideal machine. Users of this SAE Recommended Practice may modify the recommended protocols to satify the needs of specific use-cases, e.g., reducing the recommended number of test loads and/or pressures for benchmarking purposes. However, due care is necessary when modifying the protocols to maintain data integrity.

Abstract With the rapid development of the Internet and intelligent control technology, intelligent transportation has become a research hotspot in building a smart city. Under the background of intelligent transportation, it is particularly important to effectively evaluate the rail transit as the framework of urban public transport in this study, and fuzzy mechanism is introduced to optimize the support vector machine (SVM), and on this basis, analytic hierarchy process (AHP) and SVM are combined to improve the classification accuracy and improve the rail transit operation safety evaluation index system. The experimental results show that the classification accuracy of the fuzzy SVM combined with AHP is above 85% on all the datasets, and it can effectively eliminate the less-relevant indicators. In the actual evaluation of Shanghai Rail Transit safety, the prediction accuracy exceeded 80% and the highest reached 94.51%.

Abstract This research examined tractor operators’ daily vibration exposure A(8) with different input riding parameters, i.e., average speed (m/s) (2.78, 3.89, 5.0), body mass (BM) (kg/m2) (35.3, 32.6, 25.4), and different terrain types (brick, farm, and tar roads). To arrange the systematic sequence of experiments, Taguchi’s L9 orthogonal array has been selected for this study. The signal-to-noise ratio (SNR) is calculated to analyze the overall influence of input parameters over the output parameters. In this study, it is found that A(8) responses exceeded the recommended action value among all the tractor operators according to ISO 2631-1 (1997). The average speeds and various terrain conditions were shown to be the most influential significant variables (p ≤ 0.05), with percentage contributions of 53.71% and 11.53%, respectively.