Vehicle Manufacturers Research Articles

Logistics challenges in used car maritime traffic from Europe to west Africa

This paper addresses the main logistics challenges in used car maritime traffic from Europe to West Africa. Thus, the methodology (quantitative and qualitative) analyses data from the International Organization of Motor Vehicle Manufacturers (OICA), from 2015 to 2023 of government and port authorities to show the importance of used car market for mobility and socioeconomic activities. This is supplemented by surveys based on direct observation in the field, questionnaires and interviews involving in Europe 55 stakeholders and 127 in Africa. The results demonstrate that cars used and their parts, but not wrecks, are essential for motorization in West Africa. A pre-export process needs to be set up to ensure that exported vehicles are parked in better condition to meet the required common environmental standards for sustainable mobility.

Succinate exacerbates mastitis in mice via extracellular vesicles derived from the gut microbiota: a potential new mechanism for mastitis.

A high grain diet causes an ecological imbalance in the gut microbiota and serves as an important endogenous trigger of mastitis in dairy cows, but the underlying mechanisms are unclear. Our previous study revealed that subacute rumen acidosis (SARA)-associated mastitis has distinct metabolic profiles in the rumen, especially a significant increase in succinate, but the role of succinate in the pathogenesis of mastitis remains unclear. Succinate treatment exacerbates low-grade endotoxemia-induced mastitis in mice. Specifically, succinate increased the production of gut microbiota-extracellular vehicles (mEVs) containing lipopolysaccharides, which can diffuse across the damaged intestinal barrier into the mammary glands. Administration of mEVs promotes mammary inflammation via activation of the TLR4/NF-κB pathway. Our findings suggest that succinate promotes mastitis through the proliferation of enteric pathogens and mEVs production, suggesting a potential strategy for mastitis intervention on the basis of intestinal metabolic regulation and pathogen inhibition. The role of mEVs in interspecific communication has also been elucidated.

Cooperation and Production Strategy of Power Battery for New Energy Vehicles Under Carbon Cap-and-Trade Policy

Considering the supply chain composed of a power battery supplier and a new energy vehicle manufacturer, under the carbon cap-and-trade policy, this paper studies the different cooperation modes between the manufacturer and the supplier as well as their strategies for green technology and power battery production. Three game models are constructed and solved, respectively, under the collaboration mode of wholesale purchasing, patent-licensed manufacturing, and own R&D + Wholesale purchasing. The equilibrium analysis is carried out. Finally, the influence of relevant parameters is explored through numerical simulation. It is found that (1) the manufacturer’s choice of optimal battery production strategy is influenced by the input cost of green technology, the production cost of power battery, the carbon trading price, and the free carbon quota allocated by the government; (2) the cost coefficient of technological innovation affects negatively the optimal decision-making of the supply chain members, the market demand, and the optimal profit, and it has no impact when the cost coefficient reaches a certain value; (3) carbon cap-and-trade policy can, to a certain extent, incentivize suppliers and manufacturers to carry out technological innovation to reduce carbon emissions in the production process, but we cannot ignore the negative impacts of excessively high carbon trading price on the level of emission reduction and the market demand; and (4) the government should reasonably control the carbon price and carbon quota. The above conclusion will provide reference suggestions for new energy vehicle manufacturers and related suppliers.

Electric Vehicles for a Flexible Energy System: Challenges and Opportunities

As the adoption of Electric Vehicles (EVs) accelerates, driven by increasing urbanization and the push for sustainable infrastructure, the need for innovative solutions to support this growth has become more pressing. Vehicle-to-Grid (V2G) technology presents a promising solution by enabling EVs to engage in bidirectional interactions with the electrical grid. Through V2G, EVs can supply energy back to the grid during peak demand periods and draw power during off-peak times, offering a valuable tool for enhancing grid stability, improving energy management, and supporting environmental sustainability. Despite its potential, the large-scale implementation of V2G faces significant challenges, particularly from a technological and regulatory standpoint. The success of V2G requires coordinated efforts among various stakeholders, including vehicle manufacturers, infrastructure providers, grid operators, and policymakers. In addition to the technical barriers, such as battery degradation due to frequent charging cycles and the need for advanced bidirectional charging systems, regulatory frameworks must evolve to accommodate this new energy paradigm. This review aims to provide a comprehensive analysis of V2G technology, focusing on different perspectives—such as those of users, vehicles, infrastructures, and the electricity grid. This study will also explore ex ante, ex post, and ongoing assessment studies, alongside the experiences of pioneer cities in implementing V2G.

High-precision reconstruction of a heat flux boundary via a programmable scanning electron beam controlled by the voltage waveform design method

A scanning electron beam can be used to construct heat flux boundaries (HFBs). However, the long current response time in the deflection coil using a current control method (CCM) cause the deviation of the current waveform, potentially reducing the accuracy of the reconstructed HFB relative to the target HFB. The impact of the current response time on the reconstruction accuracy increases as the field frequency increases, and the accuracy can be reduced from 0.89 to 0.69. To shorten the current response time and improve the reconstruction accuracy, a voltage waveform design method (VWDM) is introduced as a replacement for the CCM. The result indicates that the accuracy of the HFB reconstructed by a scanning electron beam controlled by the VWDM can reach 0.91. Additionally, increasing the maximum output voltage of the power amplifier used to generate the voltage can further improve the reconstruction accuracy of the HFB with the VWDM. This study provides a new approach for the accurate construction of HFBs for rapid thermal processing, additive manufacturing and thermal assessment of hypersonic vehicles.

Developing electric vehicles in China and the United States: Revisiting debates on industrial strategy

AbstractMotivationStrategies to develop manufacturing industry are the subject of lively debate over whether to follow or defy national comparative advantage. Electric vehicles (EVs), a major change for automobile manufacturers, are no exception.Despite the US government's promotion of global integration to activate the semi‐invisible hand of market forces, neither traditional Detroit automakers nor new firms, even with government grants or loans, were able to launch EVs on the world market promptly, with the notable exception of Tesla. In China, the government helped dozens of domestic electric vehicle manufacturers enter the market through formidable government subsidies. However, none of China's manufacturers, other than Wuling, were able to surpass Tesla in sales in China.The rapid growth of Tesla and Wuling without relying heavily on government protection and subsidies raises the question of whether their success can be attributed to other forms of state intervention.PurposeWhat policies and strategies enable manufacturers to develop and market a new technology, such as EVs? What can we learn by comparing the experiences of US and Chinese vehicle manufacturers?We consider these questions and what the implications are for debates about industrial development strategy.Methods and approachWe compare four carmaking firms—GM, Tesla, BYD, and Wuling—their business strategies and their interactions with the US and Chinese states. We draw on company reports, government documents, and press reports.FindingsBoth China and the US employed semi‐developmentalist, semi‐neoliberal policies to promote the manufacture of EVs. The US subsidized research and development in its automobile industry and provided incentives to consumers. It has subsequently moved to lending to the most promising firms for EV production. China, in contrast, has selectively subsidized EV firms all the way from development to marketing.Subsidies from the US government have been steady: they are expected to stay this way in the near term. However, the fastest‐growing carmakers have used outward foreign direct investment to gain competitive advantage. China's subsidies, on the other hand, have been unstable: linked to the performance of the target firms. The higher the firm's profit, the lower the subsidies and vice versa. China has been careful with subsidies to prevent rent seeking and moral hazard.Neither following nor defying comparative advantage explains the strategies followed by the four firms: comparative advantage considerations have been tempered by the possibilities of foreign investment.Policy implicationsThe results imply that traditional developmental strategies have resorted to inward foreign direct investment in high‐income countries as a means to learn technologies and outward foreign direct investment in low‐ and medium‐income countries as a way to reduce production costs, but have missed the comparative advantage of outward and inward foreign direct investment, and strategic mixes of these, when developing and diffusing radical innovation like EVs.

Social risk assessment of the transition from internal combustion engine vehicles to electric vehicles: Material supply chains in China, Japan, and South Korea

In order to reduce carbon emissions, the production of internal combustion engine vehicles (ICEVs) is being transitioned to the production of electric vehicles (EVs). However, the social unsustainability of this process needs further testing. This study explored the social risk characteristics of the transition from ICEVs to EVs based on the Social Hotspots Database (SHDB), combining with the Social Life Cycle Assessment (SLCA) and the Marginal Analysis Method (MAM). Here, we revealed the differences in social risks in the material supply chains between China, Japan, and South Korea, which may create incentives for 100 % supply concentration in a hypothetical game. In terms of social risk, common substitution trends in the iron‑aluminum sector and synergy patterns in the copper-rubber sector were observed in the three countries. As a key factor influencing social risks, cost factor may change, and cost changes due to transition were mainly influenced by production adjustments and sourcing differences, reflecting differences in production capacity trends. This study makes it possible to consider ICEVs and EVs as two endpoints for transition activity in a SLCA exercise and provides valuable insights focusing on transition, aiding in material selection for future product design and underscoring the importance of proactive consideration of social sustainability indices.

An empirical analysis of Saudi Arabian women’s intentions to adopt hydrogen fuel cell cars

To successfully execute demand-side tactics for alternative energy, such as hydrogen, it is essential to thoroughly comprehend the preferences and circumstances that stimulate client demand. Thus far, studies have been scarce on female customers’ opinions, intentions, and behaviour towards various products in the Kingdom of Saudi Arabia (KSA). However, no studies have been conducted thus far, specifically on hydrogen fuel cell vehicles (HFCVs). Our study examines the key elements influencing women’s inclination to embrace HFCVs in the KSA. This study utilized an online questionnaire to gather data from 227 prospective female clients, thus conducting empirical research. To ascertain women’s intentions towards hydrogen fuel cell automobiles, we employed a structural equation modelling technique utilizing Smart-PLS 4.1. The findings of our study demonstrate that the perceived advantages, perceived barriers, public opinion, and government initiatives significantly influence the desire of women in Saudi Arabia to use HFCVs. The study suggests that the government should gather all pertinent stakeholders, including vehicle manufacturers, hydrogen producers, research institutes, and organizations, to address the challenges and accelerate the advancement of the hydrogen transportation system.

Perbaikan Nilai Overall Equipment Effectiveness dengan Metode Total Productive Maintenance pada PT. Electric Vehicle Trimotorindo

This research was conducted to increase the Overall Equipment Effectiveness (OEE) value at PT Electric Vehicle Trimotorindo, which is an electric vehicle manufacturing company. The main problem faced by the company is the decline in OEE value every month due to lack of machine maintenance and deterioration in production quality. The method used in this study is Total Productive Maintenance (TPM), which involves identifying the factors that cause production losses through the analysis of Six Big Losses. The results show that the OEE value has increased from 78.61% to 80.36% after improvements through TPM, especially in increasing the quality rate from 88.48% to 92.23%. The conclusion of this study is that the application of TPM can improve the effectiveness of machine use and production quality, which ultimately contributes to the improvement of the company's productivity.

Container-Based Electronic Control Unit Virtualisation: A Paradigm Shift Towards a Centralised Automotive E/E Architecture

The past 40 years have seen automotive Electronic Control Units (ECUs) move from being purely mechanical controlled to being primarily digital controlled. While the safety of passengers and efficiency of vehicles has seen significant improvements, rising ECU numbers have resulted in increased vehicle weight, greater demands placed on power, more complex hardware and software, ad hoc methods for updating software, and subsequent increases in costs for both vehicle manufacturers and consumers. To address these issues, the research presented in this paper proposes that virtualisation technologies be applied within automotive electrical/electronic (E/E) architecture. The proposed approach is evaluated by comprehensively studying the CPU and memory resource requirements to support container-based ECU automotive functions. This comprehensive performance evaluation reveals that lightweight container virtualisation has the potential to welcome a paradigm shift in E/E architecture, promoting consolidation and enhancing the architecture by facilitating power, weight, and cost savings. Container-based virtualisation will also enable efficient and robust online dynamic software updates throughout a vehicle’s lifetime.

Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.

Balancing Sustainability and Cost: Green Electricity Adoption in Chinese Vehicle Manufacturing

Abstract: Against the backdrop of escalating environmental concerns and the worldwide drive towards achieving carbon neutrality, this paper tackles the urgent task of incorporating environmentally-friendly electricity into an industry known for its substantial energy usage. The central research focuses on the justification of investing in green electricity for vehicle manufacturers in China, taking into account the abundance of other carbon reduction choices that may provide more economically efficient solutions. The study utilizes literature analysis and review to examine the economic and environmental consequences of adopting green electricity. It situates the topic within the larger framework of China's goals for sustainable economic growth and carbon neutrality. Its objective is to provide clarity on the compromises between sustainability and affordability, providing valuable perspectives on how automobile manufacturers may effectively negotiate these obstacles while actively supporting the worldwide initiative to combat climate change.

Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles

With the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort. However, the diversity and weaknesses of the Android operating system pose substantial safety risks to AVs, as these factors can expose them to threats, namely Android malware. The advanced behaviour of multi-data source fusion in autonomous driving models has mitigated recognition accuracy and effectualness for Android malware. To efficiently counter new malware variants, novel techniques distinct from conventional methods must be utilized. Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. This manuscript proposes a Deep Learning-Based Improved Transformer Model on Android Malware Detection (DLBITM-AMD) technique for Internet vehicles (IoVs). The main aim of the presented DLBITM-AMD approach is to detect Android malware effectually and accurately. The DLBITM-AMD method performs a Z-score normalization process to convert the raw data into a standard form. Then, the DLBITM-AMD approach utilizes the binary grey wolf optimization (BGWO) model to select optimum feature subsets. An improved transformer is integrated with the RNN model and softmax to enhance classification for Android malware recognition. Finally, the snake optimizer algorithm (SOA) method is employed to select the optimum parameter for the classification method. An extensive experiment of the DLBITM-AMD method is accomplished on a benchmark dataset. The performance validation of the DLBITM-AMD technique portrayed a superior accuracy value of 99.26% over existing Android malware recognition models.

The window of opportunity: a case study of the battery electric vehicle in Thailand

Purpose Thailand, although a late entrant, has emerged as a significant center for the production and export of internal combustion engine (ICE) vehicles in Asia. With the arrival of a disruptive battery-electric vehicle (BEV), this study aims to investigate whether Thailand can exploit this window of opportunity to leapfrog. Design/methodology/approach This paper uses data sourced from Thailand’s Department of Intellectual Property to analyze electric vehicle (EV) patent applications. This paper collected qualitative data and conducted interviews with several EV manufacturers. Findings Although Japanese automakers dominate essential EV technology patents, they trail behind Chinese automakers, which benefited from the Free Trade Agreement (FTA) in EV sales. The incumbents, Japanese and Western, choose to expand their production of hybrid electric vehicles (HEVs) and plug-in hybrid vehicles. ICE technology does not constrain new entrants, such as Chinese automakers, Japanese newcomers and Thai newcomers. Compared to Japanese carmakers stuck with ICE and HEV technologies, they have been transitioning to BEVs at a faster pace. BEV has opened the door for large Thai indigenous energy corporations to enter the automotive industry by focusing on niche areas (nonfour-wheel vehicles) and supporting businesses like charging stations. In summary, Thailand has successfully attracted foreign direct investment (FDI) into the EV sector, but it has not yet succeeded in developing indigenous technologies related to EVs. The Thai automobile sector fails to leapfrog because it is too tied up with Japanese automotive production networks and has an unfavorable FTA with China. Originality/value This study sheds light on the limitations of an industrial development strategy relying on openness to trade and FDI without adequately strengthening indigenous technologies and firms, as the country adopting the strategy fails to leapfrog when the window of opportunities created by disruptive technologies arrives.

A Review on Secure Outsourcing and Privacy-Preserving Traffic Monitoring in Fog-Enabled VANETs

New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing, but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity, the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs, such as data confidentiality and message access control are still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs, better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs, including the protection of data storage, user authentication, access control, system availability across multiple network connections, and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.

Estimation of the world market and application of nanomaterials in the aerospace industry

The object of the study is the global aerospace market status, segment analysis, dynamics, competition, and prospects. The methods of searching and analyzing literature data, summarizing, systematizing, and visualizing data with diagrams are used. The aerospace industry is at the forefront of technological innovation and is constantly searching for new advanced materials to improve productivity, efficiency, and safety. The aerospace market encompassing the design, manufacture, and maintenance of aircrafts, space vehicles, and related systems. It includes the commercial and military sectors, as well as the space exploration field. The world market of the aerospace industry was studied. Factors affecting the market positively and negatively were identified. According to the global aerospace market segmentation by vehicle types, the leading position in 2023 was occupied by the commercial aircrafts segment with a share of 63.4 %; by the vehicle size – by the narrow-body aircraft segment (72.4 %); by the end consumer – by the private sector segment (65.4 %); by operation – by manually operated aircrafts segment (79.4 %); and by geographic regions – by the North American segment (47.3 %). The main strategic trends and directions of the further aerospace market development are presented. The world market of aerospace materials was studied. Factors affecting market dynamics are identified, and market challenges are highlighted. According to the world aerospace market segmentation by the materials types, the leading place in 2022 belonged to the composite materials segment with a share of 69 %; by the aircraft type – to the commercial aircrafts segment (51 %); and by geographical regions – to the European segment (35.0 %). The trends of the sustainable aerospace industry development are summarized: modern aircraft design, use of sustainable aviation fuel, urban air mobility, modern traffic technology, and air transportation management optimization. For each trend, possible actions leading to changes in the aerospace industry are considered. The question of the nanomaterials use in the space industry is considered. Some characteristics and possibilities of application of nanocomposite materials, nanocoatings, nanofluids, nanosensors, and carbon nanotubes, as well as examples of the nanomaterials application in aircraft components are given. The industry problems are identified, and their possible solutions are given.

Research on Electric Vehicle Powertrain Systems Based on Digital Twin Technology

As a critical component of electric vehicles, the powertrain has a significant impact on the overall performance of vehicles. In addressing the challenge of lengthy testing cycles, this study develops a para model of the powertrain, utilizing digital twin (DT) technology, thereby establishing a framework for simulation testing of multi-controller intermodulation. We establish functional definition coverage testing by designing specific functional requirement use cases, and we validate the failure mechanism via fault injection use cases. The results indicate that the DT testing platform can effectively simulate the operational interactions among various controllers within the powertrain system. In comparison to traditional field testing, the digital twin-based testing methodology offers enhanced operational efficiency and allows for the examination of testing conditions that are impractical to implement in real vehicles, particularly in the context of fault injection testing, thus facilitating the early detection of potential safety risks within the system. The advancement of this technical solution holds significant practical implications for the future mass production and development of electric vehicles.

El auge del coche eléctrico en China y su impacto en la Unión Europea

Rationale China’s electric vehicle sector has recently aroused considerable interest thanks to its rapid growth and current dominance of the global market. Given the importance of the automotive sector in Spain and the European Union (EU), and the recent introduction of tariffs, it is worth exploring the factors behind this rise. Takeaways •Battery electric vehicle (BEV) market penetration in the EU has grown notably in recent years: in 2023 BEVs accounted for 15% of all vehicles sold (5.4% in Spain), a figure well above the 1.9% recorded in 2019 (0.7% in Spain). •China has secured its status as the world’s leading electric vehicle manufacturer and exporter, its output proving particularly popular in the European market. As a consequence, the market share of BEVs made in China rose from 0.4% of new BEV registrations in 2019 to 20% in 2023. •The success of Chinese BEV exports rests on lower manufacturing costs, thanks largely to China’s control over the supply chain, government support policies at various stages of production and the country’s early embrace of electric vehicles.

Electric vehicle ramp recognition based on fusion of vehicle mass estimation

Road slope is the necessary environmental information for intelligent electric vehicles. The accuracy of slope recognition directly determines the control quality of vehicles on the hill. Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to To satisfy the application requirements of mass production vehicles, this paper proposes an electric vehicle slope recognition method based on vehicle mass estimation. Firstly, the vehicle longitudinal dynamics model is established, and the signal characteristics of the acceleration sensor under the actual vehicle conditions are analyzed. The least square vehicle mass estimation strategy with forgetting factor is constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms are designed for static parking and dynamic driving scenarios. In the static scenario, the filter latch strategy is used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario, the Kalman filter algorithm based on measurement noise adaptation is designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method is verified by Simulink -CarSim joint simulation. Finally, the real vehicle test is completed on Chery new energy's mass production electric vehicle platform and domain controller. road test results show that the mass estimation error is less than ±10 kg; the static estimation error of the slope is less than 0.001 rad, and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved, which ensures the environmental adaptability of the intelligent electric vehicles.

Effects of head restraint (HR) interference on child restraint system (CRS) performance in frontal and far-side impacts.

Forward-facing child restraint systems (FF CRS) and high-back boosters often contact the vehicle seat head restraint (HR) when installed, creating a gap between the back surface of the CRS and the vehicle seat. The effects of HR interference on dynamic CRS performance are not well documented. The objective of this study is to quantify the effects of HR interference for FF CRS and high-back boosters in frontal and far-side impacts. Production vehicle seats with prominent, removeable HRs were attached to a sled buck. One FF CRS and two booster models were tested with the HR in place (causing interference) and with the HR removed (no interference). A variety of installation methods were examined for the FF CRS. A total of twenty-four tests were run. In frontal impacts, HR interference produced small but consistent increases in frontal head excursion and HIC36. Head excursions were more directly related to the more forward initial position rather than kinematic differences caused by HR interference. In far-side impacts, HR interference did not have consistent effects on injury metrics. Overall, these results suggest only slight benefits of removing the HR in frontal impacts specifically. Caregivers should use caution if removing a vehicle HR to ensure that the current child occupant and all future vehicle occupants have adequate head support available in case of a rear impact.