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On the path to decarbonizing road transport, electric commercial vehicles will play a significant role. The first applications were directed to the smaller trucks for distribution traffic with relatively moderate driving and range requirements, but meanwhile, the first generation of a complete portfolio of truck sizes is developed and available on the market. In these early applications, many compromises were accepted to overcome component availability, but meanwhile, the supply chain can address the specific needs of electric trucks. With that, the optimization towards higher usability and lower costs can be moved to the next level. Especially for long-haul trucks, efficiency is a driving factor for the total costs of ownership. Besides the propulsion system, all other systems must be optimized for higher efficiency. This includes thermal management since the thermal management components consume energy and have a direct impact on the driving range.

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.

Nowadays, Bismuth (Bi) is being applied as an overlay material for engine bearings instead of Lead (Pb) which is an environmentally harmful material. Bi overlay has already been a solid performer in some automotive engine sectors due to its superior load carrying capacity and good robustness characteristic which are necessary to maintain its longevity during the lifetime of engines. The replacement is also seen on relatively larger size engines, such as Trucks and Off-highway heavy duty applications. Basically, these applications require higher power output than passenger cars, and the expected component lifecycle becomes longer. Though Bi has similar material characteristic to traditional Pb, it becomes challenging for the material alone to satisfy these requirements. Polymer overlay is known for its superior anti-wear performance and longer lifetime due to less adhesion against a steel counterpart than metallic materials (included Bi).

To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers.

Lithium-ion batteries serve as the main power source for contemporary electric vehicles. Safeguarding these batteries against damage is paramount, as it can trigger accelerated performance deterioration, potential fire hazards, environmental threats, and more. This study explores damage progression of a commercial vehicle lithium-ion battery module containing prismatic cells under indentation crush loading. We employed computational simulations of mechanical loading tests to investigate this behavior. Physical tests involved subjecting modules to low-speed (0.05 m/s) indentations using a V-shaped stainless-steel wedge, under six unique loading conditions. During the tests, force, and voltage change with wedge displacement were monitored. Utilizing experimental insights, we constructed a finite element model, which included key components of the battery module, such as the prismatic cells, steel frames, and various plastic parts.

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.

In the last decades, the locomotion of wheeled and tracked vehicles on soft soils has been widely investigated due to the large interest in planetary, agricultural, and military applications. The development of a tire-soft soil contact model which accurately represents the micro and macro-scale interactions plays a crucial role for the performance assessment in off-road conditions since vehicle traction and handling are strongly influenced by the soil characteristics. In this framework, the analysis of realistic operative conditions turns out to be a challenging research target. In this research work, a semi-empirical model describing the interaction between a tire and homogeneous and fine-grained soils is developed in Matlab/Simulink. The stress distribution and the resulting forces at the contact patch are based on well-known terramechanics theories, such as pressure-sinkage Bekker’s approach and Mohr-Coulomb’s failure criterion.

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 The two-branch exhaust of an asymmetric twin-scroll turbocharged engine are asymmetrically and periodically complicated, which has great impact on turbine matching. In this article, a matching effect of turbine speed parameter on asymmetric twin-scroll turbines based on the exhaust pulse energy weight distribution of a heavy-duty diesel engine was introduced. First, it was built as an asymmetric twin-scroll turbine matching based on exhaust pulse energy distribution. Then, by comparing the average matching point and energy matching points on the corresponding turbine performance map, it is revealed that the turbine speed parameter of energy matching points was a significant deviation from the turbine speed parameter under peak efficiency, which leads to the actual turbine operating efficiency lower than the optimal state.

Aluminium composites are remarkably used in automotive, aerospace, and agricultural sectors because of their lightweight with definable mechanical properties. The stir casting route was followed to fabricate cylindrical samples with base aluminium alloy LM4, LM4/SiC, LM4/Al2O3, and LM4/SiC/Al2O3. The tensile strength, compressive strength, hardness, and micro-structural analysis were performed on samples and Finite element analysis (FEA) was adopted to predict the failure modes of composites. The composites experimental results were found to be in line with the FEA results, however, the LM4/SiC/Al2O3 revealed better results on the mechanical properties when compared with other composite configurations. The mechanical properties improvement like hardness 5%-11%, tensile strength 10.26%-20.67%, compressive strength 15.19% - 32.58% and 71.52 - 82.1% reduction in dimension have been achieved in LM4/SiC/Al2O3 composite comparing to base metal.

Lightweight materials are in great demand in the automotive sector to enhance system performance. The automotive sector uses composite materials to strengthen the physical and mechanical qualities of light weight materials and to improve their functionality. Automotive elements such as the body shell, braking system, steering, engine, battery, seat, dashboard, bumper, wheel, door panelling, and gearbox are made of lightweight materials. Lightweight automotive metals are gradually replacing low-carbon steel and cast iron in automobile manufacture. Aluminium alloys, Magnesium alloys, Titanium alloys, advanced high-strength steel, Ultra-high strength steel, carbon fiber-reinforced polymers, and polymer composites are examples of materials used for light weighing or automobile decreased weight. The ever-present demand for fuel-efficient and ecologically friendly transport vehicles has heightened awareness of lowering weight and performance development.

Aluminum alloys are employed in agricultural equipment, aerospace sectors, medical instruments, machinery, automobiles, etc. due to their physical and mechanical characteristics. The geometrical shape and size of the parts are modified in turning operation by using a single-point cutting tool. A356 aluminum alloy is widely used in various engineering sectors, hence there is a necessity to produce A-356 components with quality. The inappropriate cutting parameters used in turning operation entail high production costs and reduce tool life. Box–Behnken design (BBD) based on response surface methodology (RSM) was used to design the experiments such that the experiment trials were conducted by varying cutting parameters like N-spindle speed (rpm), f-feed rate (mm/rev), and d-depth of cut (mm). The multi-objective responses, such as surface roughness (SR) and metal removal rate (MRR) were analyzed with the desirability method.

With the rise of worldwide trends towards light weighting and the move towards electric vehicles, it is now more important than ever for the automotive industry to develop and implement lightweight materials that will result in significant weight reduction and product improvements. A great deal of research has been done on how to best combine and configure honeycomb cores with the right face sheets for Truck-Mounted Container Applications. Honeycomb structures possess the ability to bring about superior structural rigidity when the core parameters are selected and optimized based on the automotive application requirements.

Tippers used for transporting blue metal, construction and mining material is designed with different types of load body to suit the material being carried, capacity and its application. These load bodies are constructed with high strength material to withstand forces under various operating conditions. Structural strength verification of load body using FEM is conducted, by modelling forces due to payload as a pressure function on the panels of the load body. The spatial variation of pressure is typically assumed. In discrete element method (DEM) granular payload material such as gravel, wet or dry sand, coal etc., can be modelled by accounting its flow and interaction with structure of load body for prediction of force/pressure distribution. In this paper, coupled FE-DEM is used for determining pressure distribution on loading surfaces of a tipper body structure of a heavy commercial vehicle during loading, unloading and transportation.

Commercial transportation is the key pillar of any growing economy. Light and Small commercial vehicles are increasing every day to cater the logistics demand, but there is always a gap between customer’s actual and desired operational efficiency. This is because of lack of organized fleet and efficient fleet operation. The major requirement of fleet owners is timely delivery, high productivity, downtime reduction, real time tracking, etc., Automakers are now providing fleet management application in modern LCV & SCV to satisfy the fleet operator requirement. However, any feature malfunction, consignment mismatch, wrong notification, missed alerts, etc., can incur huge loss to fleet operator and disrupt the entire supply chain. Hence it is very critical to extensively validate the telematics features in fleet management application. This paper explains the approach for exhaustive validation strategy of fleet management applications (B2B) from end user perspective.

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.

Abstract Being an engineer-to-order (ETO) operating industry, the control cabinet industry faces difficulties in process and workplace optimizations due to changing requirements and lot size one combined with volatile orders. To optimize workplaces for employees, current literature is focusing on ergonomic designs, providing frameworks to analyze workplaces, leaving out the optimal design for productivity. This work thus utilizes a Kano analysis, collecting empirical data to identify essential design requirements for assembly workplaces, incorporating input from switchgear manufacturing employees. The results emphasize the need for a balance between ergonomics and efficiency in workplace design. Surprisingly, few participants agree on the correlation between improved processes and workspaces having a positive impact on their well-being and product quality.