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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.

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.

Abstract The adoption of battery electric trucks (BETs) as a replacement for diesel trucks has potential to significantly reduce greenhouse gas emissions from the freight transportation sector. However, BETs have shorter driving range and lower payload capacity, which need to be taken into account when dispatching them. This article addresses the energy-efficient dispatching of BET fleets, considering backhauls and time windows. To optimize vehicle utilization, customers are categorized into two groups: linehaul customers requiring deliveries, where the deliveries need to be made following the last-in-first-out principle, and backhaul customers requiring pickups. The objective is to determine a set of energy-efficient routes that integrate both linehaul and backhaul customers while considering factors such as limited driving range, payload capacity of BETs, and the possibility of en route recharging.

Abstract The drone logistics distribution method, with its small size, quick delivery, and zero-touch, has progressively entered the mainstream of development due to the global epidemic and the rapidly developing global emerging logistics business. In our investigation, a drone and a delivery man worked together to complete the delivery order to a customer’s home as quickly as possible. We realize the combined delivery network between drones and delivery men and focus on the connection and scheduling between drones and delivery men using existing facilities such as ground airports, unmanned stations, delivery men, and drones. Based on the dynamic-vehicle routing problem model, the establishment of a delivery man and drone with a hybrid model, in order to solve the tarmac unmanned aerial vehicle for take-out delivery scheduling difficulties, linking to the delivery man and an adaptive large neighborhood search algorithm solves the model.

Abstract Policy makers generally favor all-electric vehicles over hybrid-electric vehicles because of greater unit effectiveness in reducing carbon emissions. Since both systems use lithium-ion batteries, global demand for batteries is projected to grow 10-fold by 2030. If any step in the global battery supply-chain experiences bottlenecks, shortages can occur. We use a novel cost-effectiveness metric, carbon reduction per unit of battery capacity consumed, to rank emissions reductions accomplished by, alternatively, eight plug-in hybrid-electric vehicles, 75 hybrid-electric vehicles, and 230 mild hybrid-electric vehicles, which have the same total battery capacity as one all-electric vehicle. Our main finding, although counterintuitive, is that, with limited battery supplies, larger reductions in carbon emissions can be accomplished by hybrids than by all-electric vehicles.

Abstract Since the complexity of modern vehicles is increasing continuously, car manufacturers are forced to improve the efficiency of their development process to remain profitable. A frequently mentioned measure is the consequent integration of virtual methods. In this regard, objective evaluation criteria are essential for the virtual design of driving dynamics. Therefore, this article aims to identify robust objective evaluation criteria for the nonlinear combined longitudinal and lateral dynamics of a vehicle. The article focuses on the acceleration in a turn maneuver since available objective criteria do not consider all relevant characteristics of vehicle dynamics. For the identification of the objective criteria, a generic method is developed and applied. First, an open-loop test procedure and a set of potential robust objective criteria are defined.

Abstract Letter from the Special Issue Editors

Abstract In the last decades we have witnessed an increasing number of military operations in urban environments. Complex urban operations require high standards of training, equipment, and personnel. Emergency forces on the ground will need specialized vehicles to support them in all parts and levels of this extremely demanding environment including the subterranean and interior of infrastructure. The development of vehicles for this environment has lagged but offers a high payoff. This article describes the method for developing a concept for an urban operations vehicle by characterization of the urban environment, deduction of key issues, evaluation of related prototyping, science fiction story-typing of the requirements for such a vehicle, and comparison with field-proven and scalable solutions. Embedding these thoughts into a comprehensive research and development program provides lines of development, setting the stage for further research.

Abstract Wheel loaders are widely used in construction projects. In order to reduce pollution and energy consumption, major wheel loader manufacturers are developing electric powertrain technology. Our main research goal is to reduce the energy consumption of a pure electric loader. This study is intended to build a vehicle simulation model of a multiple drive motor electric loader. According to the common working conditions and empirical formulas of the loader, the simulation data of the electric loader are calculated. The torque distribution control strategy based on the optimal efficiency of the motor is designed for the multiple drive motor electric loader and is compared with the equal proportion distribution control and the axle load ratio distribution control through simulation analysis. The simulation results show that the proposed torque distribution control strategy based on motor optimal efficiency can reduce energy consumption by 7–12%.

Abstract As a special distributed drive configuration, H-type distributed drive electric vehicle (H-DDEV) has better off-road ability and trafficability than traditional distributed drive electric vehicle, which attracts the interest of researchers deeply. However, due to its unique structure, how to ensure the stability of H-DDEV is still a key control problem. In this article, a hierarchical stability control algorithm based on model predictive control (HC-MPC) is proposed for H-DDEV. First, the vehicle dynamics model is established, which can reflect the dynamic characteristics of H-DDEV. Then, the HC-MPC is designed. In the upper layer, the nonlinear two-degrees-of freedom (2-DOF) model is applied in MPC, which can improve the control accuracy without increasing complexity through local linearization.

Abstract Brake squeal reduces comfort for the vehicle occupants, damages the reputation of the respective manufacturer, and can lead to financial losses due to cost-intensive repair measures. Mode coupling is mainly held responsible for brake squeal today. Two adjacent eigenfrequencies converge and coalesce due to a changing bifurcation parameter. Several approaches have been developed to suppress brake squeal through structural changes. The main objective is to increase the distance of coupling eigenfrequencies. This work proposes a novel approach to structural modifications and sizing optimization aiming for a start at shifting a single component eigenfrequency. Locations suitable for structural changes are derived such that surrounding modes do not significantly change under the modifications. The positions of modifications are determined through a novel sensitivity calculation of the eigenmode to be shifted in frequency.

Abstract Demand-Driven Material Requirements Planning (DDMRP) is regarded as a potential method of material management to provide planning and execution performance improvements in variable environments. However, Industry 4.0 refers to the fourth industrial revolution that allows creating a smart manufacturing system by using the new technologies of communication, automation, and digitalization. DDMRP and Industry 4.0 are crucial as new technologies are introduced to companies to improve their performance. Nevertheless, there is an absence of reviews showing the relationships between DDMRP and Industry 4.0. A literature review is used to identify the key constructs of DDMRP and Industry 4.0, and the relationships postulated between them are presented. The main objective of this study is to investigate the relationship between DDMRP and Industry 4.0. The result of this article was a model for integrating the DDMPRP and Industry 4.0 proposed upon a robust theoretical method.

Abstract Every year the demands on the electric grid increase, but the ability to deliver power where needed remains problematic because of transmission and distribution losses, vulnerabilities to natural disasters, and struggles to meet peak load requirements in an increasing number of regions. To meet these increasing demands, especially with emerging electric vehicles, it becomes ever more important to develop integrated demand and response systems. One such promising technology is the use of a micro Combined Heat and Power (mCHP)-based distributed energy system that addresses both electricity and thermal demands (i.e., electricity, hot water, and space heating demands) by using a single unit. However, one major problem with this technology at the residential level is the optimal sizing and maximizing the operational time of mCHP systems in meeting electrical and thermal demands.

Abstract Autonomous vehicles (AVs) are being rapidly introduced into our lives. However, public misunderstanding and mistrust have become prominent issues hindering the acceptance of these driverless technologies. The primary objective of this study is to evaluate the effectiveness of a driving simulator to help the public gain an understanding of AVs and build trust in them. To achieve this aim, we built an integrated simulation platform, designed various driving scenarios, and recruited 28 participants for the experiment. The study results indicate that a driving simulator effectively decreases the participants’ perceived risk of AVs and increases perceived usefulness. The proposed methodologies and findings of this study can be further explored by auto manufacturers and policymakers to provide user-friendly AV design.

Abstract The development of carsharing can reduce the number of private cars, which can save resources. Due to the limited supply of vehicles and diversified demands of users, it is necessary to plan the temporal and spatial distribution of cars. Predicting the pick-up time of carsharing users is of great significance to understand the travel preference of carsharing users, which can help operators formulate operational strategies such as relocation and pricing. To this end, this study adopts an improved decision tree (DT) to analyze and predict pick-up time for carsharing users. Firstly, the ordered clustering method is used to discretize time. Secondly, the random forest (RF) model is constructed to extract key features. Finally, the model of the C5.0 DT is constructed to predict the pick-up time of users. A case study is conducted to demonstrate the proposed model. The results indicate that the prediction accuracy of users’ pick-up time can reach 87%.

Abstract This article develops an analysis about the statistical nature of the Pondered Deviation from Reference (PDrs ) index for knock detection in internal combustion engines. The statistical analysis proved that the PDrs index is a statistic whose probability distribution function follows a log-normal distribution with a mean and variance of 1.015 and 0.0103, respectively. The distribution and the probability knock threshold of the PDrs index is independent of engine operating conditions, types of engines, and fuels used and through Monte Carlo’s simulations which stated that the probability knock threshold is equal to 1.64. Thereby, engines run with abnormal combustion if the PDrs is greater than 1.64, whereas if PDrs ≤ 1.64, engines run under normal combustion.

Abstract Transportation remains one of the main causes of world greenhouse gas emissions, which is why the automotive sector is investing heavily in electric vehicles. Fuel cell electric vehicles (FCEVs) are a promising and valid technology to achieve long-term emission-free mobility. The potential impact of FCEV introduction on several raw material markets, mainly platinum has not been investigated in detail using currently available data. A scenario-based analysis was conducted, based on both potential future technologies and FCEV design options, in order to determine the risks that would come up in the case of large-scale FCEV implementation in passenger vehicles. Four scenarios of widespread FCEV implementation are developed, in which vehicle design, fuel cell technology, and battery chemistry are taken into account, resulting in four future raw material demand scenarios for platinum (Pt), cobalt (Co), nickel (Ni), and lithium (Li).

Abstract The lack of traceability in today’s supply-chain system for auto components makes counterfeiting a significant problem leading to millions of dollars of lost revenue every year and putting the lives of customers at risk. Traditional solutions are usually built upon hardware such as radio-frequency identification (RFID) tags and barcodes, and these solutions cannot stop attacks from supply-chain (insider) parties themselves as they can simply duplicate products in their local database. This industry-academia collaborative work studies the benefits and challenges associated with the use of distributed ledger (or blockchain) technology toward preventing counterfeiting in the presence of malicious supply-chain parties. We illustrate that the provision of a distributed and append-only ledger jointly governed by supply-chain parties themselves makes permissioned blockchains such as Hyperledger Fabric a promising approach toward mitigating counterfeiting.

Abstract The Magic Formula (MF) Tire Model has received wide acclaim for vehicle dynamic simulations. However, it requires that the model parameters be determined for a particular tire, which in turn is a difficult task due to the large number of parameters involved. This work presents a study on the use of a Cascading Algorithm for determining the MF parameters, which is later packaged as a parameter estimation tool. Extending findings from previous research, the cascading algorithm aims to combine the strengths of both the Genetic Algorithm and Nelder-Mead Simplex (NMS) algorithm for the tire model fitting process. The algorithm performance is evaluated based on a total of 456 test datasets of six tires from four different manufacturers. The results show good fitting capabilities (86.5% of the fits have a coefficient of determination greater than 0.9) along with capturing the data trends accurately.