Development Of Vehicles Research Articles

An Analysis of Tesla's Disruptive Innovation Strategy

With the rapid development of new energy vehicles, governments and automobile manufacturers are generally aware that environmental protection is an important direction for the development of the future automotive industry, and Tesla, as a representative of the global new energy electric vehicles, has great research significance. In the fierce environment of the automobile market, Tesla has won the leadership position with many innovations. Therefore, the company is considered a disruptive innovation by the public. This paper focuses on whether Teslas innovation is disruptive innovation, and studies Teslas disruptive innovation strategy through literature reading and qualitative analysis. This paper discusses the definition of disruptive innovation, Teslas current innovation status and explores the difference between it and incremental innovation. This paper draws the conclusion that Tesla is a disruptive innovation by considering whether it creates a value network as a breakthrough point. Finally, the paper puts forward some suggestions, including extending the product line, adjusting its price to maintain its competitive advantage and innovation, adjusting the channel level based on local markets, provide more extreme and detailed experiential marketing and after-sales service, so as to promote Tesla's sustainable innovation.

Lightweight Design and Sustainability - Body Development of an Efficiency Vehicle

Lightweight Design and Sustainability - Body Development of an Efficiency Vehicle

Evolution of Algorithms and Applications for Unmanned Surface Vehicles in the Context of Small Craft: A Systematic Review

The development of autonomous vessels and unmanned surface vehicles (USVs) has generated great interest in the scientific community due to their potential and advantages for use in various environments and applications. Several literature review papers have been produced from different perspectives, contributing to a better understanding of the topic and to the analysis of advances, challenges, and trends. We hypothesize that the greatest attention has been focused on the development of high-impact applications in the maritime sector. Additionally, we depart from the need to investigate the potential and advances of USVs in fluvial environments, which involve particular operating conditions, where there are different socio-environmental conditions and restrictions in terms of access to conventional energy sources and communication systems. In this sense, the main objective of this work is to study USVs in the particular context of small craft. The search for records was conducted in Scopus and Web of Science databases, covering studies published from 2000 to 16 May 2024. The methodology employed was based on the PRISMA 2020 guidelines, which is a widely recognized protocol that ensures quality and rigor in systematic reviews and bibliometric analyses. To optimize the data collection and selection process, the semaphore technique was additionally implemented, allowing for an efficient categorization of the studies found. This combined methodological approach facilitated a systematic and transparent evaluation of the literature. This study was developed based on three research questions about the evolution of research topics, areas of application, and types of algorithms related to USVs. The study of the evolution of works on USVs was carried out based on the results of the meta-analysis generated with the Bibliometrix tool. The study of applications and developments was carried out based on information obtained from the papers for six study categories: application environment, level of autonomy, application area, algorithm typology, methods, and electronic devices used. For each of the 387 papers identified in the databases, labeling was performed for the 359 screened papers with six study categories according to the availability of information in the title and abstract. In the categories application sector, autonomy level, application area and algorithm type/task, it was identified that most studies are oriented toward the maritime sector, the developments to achieve full autonomy for USVs, the development of designs or algorithms at the modeling and simulation level, and the development and implementation of algorithms for the GNC subsystems. Nevertheless, this research has revealed a much wider range of environments and applications beyond maritime, military, and commercial sectors. In addition, from the mapping of the types of algorithms used in the GNC architecture, the study provides information that can be used to guide the design of the subsystems that enable USV autonomy for civilian use in restricted environments.

Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology

Abstract This study introduces a novel methodology for vehicle development under crashworthiness constraints. We propose coupling the solution space method (SSM) with active learning reliability (ALR) to map global requirements, i.e., safety requirements on the whole vehicle, to the design parameters associated with a component. To this purpose, we use a classifier to distinguish between the design that fulfills the requirements, the safe domain, and those that do not, the failure domain. This classifier is trained on finite element simulations, exploiting the learning strategies used by ALR to efficiently and precisely identify the border between the two domains and the information provided on these domains by the SSM. We then provide an exemplary application where the efficiency of the method is shown: the safe domain is identified with 270 samples and an average total error of 2.5%. The methodology we propose here is an efficient method to identify safe designs at a comparatively low computational budget. To the best of our knowledge, there is currently no methodology available that can identify regions in the design space that result in designs satisfying the local requirements set by the SSM due to the complexity and strong nonlinearity of crashworthiness simulations. The proposed coupling exploits the information of SSM and the capabilities of ALR to provide a fast mapping between the global requirements and the design parameters, which can, in turn, be made available to the designers to inexpensively evaluate the crashworthiness of new shapes and component features.

Research on Status Assessment and Operation and Maintenance of Electric Vehicle DC Charging Stations Based on XGboost

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards.

Electric Vehicle Thermal System Concept Development for Multiple Variants Using Digital Prototype and AI

The automotive industry is experiencing a surge in system complexity driven by the ever-growing number of interacting components, subsystems, and control systems. This complexity is further amplified by the expanding range of component options available to original equipment manufacturers (OEMs). OEMs work in parallel on more than one vehicle model, with multiple vehicle variants for each vehicle model. With the increasing number of vehicle variants needed to cater to diverse regional needs, development complexity escalates. To address this challenge, modern techniques like Model-Based Systems Engineering (MBSE), digitalization, and Artificial Intelligence (AI) are becoming essential tools. These advancements can streamline concept development, optimize thermal and HVAC system design across variants, and accelerate the time-to-market for next-generation EVs. The development of battery electric vehicles (BEVs) needs a strong focus on thermal management systems (TMSs) and heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in maintaining optimal battery temperature, maximizing range and efficiency, and ensuring passenger comfort. This article proposes a digital prototype (DP) and AI-based methodology to specify BEV thermal system and HVAC system components in the concept phase. This methodology uses system and variant thinking in combination with digital prototype (DP) and AI to verify BEV thermal system architecture component specifications for future variants without extensive simulation. A BEV cabin cooling requirement of 22 °C to be achieved within 1800s at a high ambient temperature (45 °C) is required, and its verification is used to prove this methodology.

Pedestrian safety behavior modeling method in human-vehicle-intelligence shared road space

In the context of the development of autonomous vehicles sharing road resources with traditional traffic participants in the future, modeling the safe behavior of pedestrians in the shared road space between people, vehicles and intelligence is crucial for the simulation development and reliability and safety testing of autonomous vehicles. To meet this demand, the safe movement behavior of pedestrians is first analyzed, and on this basis, the traditional pedestrian social force model is improved to construct a momentum model for the interactive movement of pedestrians with other pedestrians, traditional vehicles and autonomous vehicles in the shared road space; then, the safety parameters of the model are calibrated using a data set of human-vehicle interactive movement and a genetic algorithm, and a typical shared road space scenario is built to verify the effectiveness of the model. The simulation results show that the model can simulate the real safe movement behavior of pedestrians, and has better simulation effects when used in tasks such as human-vehicle-intelligence shared road space simulation.

Assessing Meaningful Visual and Tactile Feedback for Effective Automated Vehicle Takeover by Hearing and Non-hearing Drivers

With SAE Level 3 automated vehicles handling most driving tasks, there are still situations when the driver needs to take over. Multimodal displays have been introduced to inform drivers of the need to take over for critical scenarios (e.g., in construction zones) in instructional or informative formats. However, the effects of multimodal displays on takeover performance for drivers with hearing impairments are still unclear. Therefore, the goal of this study was to investigate how signal type (single tactile (T), single visual (V), and visual and tactile combined (VT)), information type (instructional, informative, and baseline), and hearing impairment (hearing-impaired and non-hearing-impaired drivers) affect drivers’ takeover performance. Findings show that signal type significantly influenced reaction and takeover times, with multimodal signals (VT) resulting in faster reactions compared to single modal signals. Additionally, the baseline condition yielded the faster reaction times compared to both instructional and informative formats. Hearing impairment, however, did not significantly affect reaction and takeover times. Findings may inform the development of future vehicle interfaces to assist drivers with hearing impairments.

Development History and Enlightenment of Electric Vehicles Based on Tesla's Financial Perspective

Facing the continually intensifying global energy crisis and environmental pollution challenges, electric vehicles, as an important representative of green transportation, are gradually becoming an important direction for the transformation and upgrading of the automotive industry. As a pioneering figure within the electric vehicle industry, Tesla's development history and financial performance are of great significance for understanding the current situation and future of the entire industry. This article first outlines the history of the development of electric vehicles and Tesla. Then, based on the current development status of Tesla at that time, Tesla's financial indicators were analyzed. Finally, four suggestions are proposed: firstly, electric vehicle companies should continue to increase investment in technology research and development, improve product performance and market competitiveness; Secondly, focus on cost control and operational efficiency improvement to achieve economies of scale; Thirdly, actively explore domestic and international markets, expand sales channels and market share; Fourth, pay attention to the development trends in cutting-edge fields such as energy storage and autonomous driving, and seize the market opportunities in the future; Finally, strengthen brand building and marketing to enhance brand influence and consumer recognition.

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.

Sustainable development of electric vehicles in China: A bibliometric analysis and systematic review, 2011 onward

To address the challenge of climate change, China declared that its carbon dioxide emissions would peak in 2030 and become carbon neutral by 2060. China is the largest energy supplier and electric vehicles market worldwide and will stop selling combustion vehicles by 2035 and completely phase them out by 2050. To provide a overview of the current research, a bibliometric analysis and systematic review were conducted. The bibliometric analysis utilized data from Web of Science and Scopus, selected based on the PRISMA guidelines. Focusing on studies conducted in China and related to social sciences, environmental sciences, business, or economics. A total of 188 articles met the inclusion criteria. The analysis was performed using VOS viewer software to map co-citations and keyword co-occurrences, identifying core research themes and prominent clusters in the field. However, most researchers have primarily focused on one or two aspects of sustainability, with the hot spots being subsidies, charging costs, charging infrastructure, charging stations, batteries, and emissions. Life cycle assessment is also the most applied theory. This study recommended future research to focus on the usage stage of EVs, incorporating all three sustainability pillars. Additionally, regional assessment should be conducted to consider regional characteristics such as temperature, how policy influences affect electric vehicle emissions, social impacts, and economic advantages. Before promoting electric vehicles, regional assessments should be conducted, so that stakeholders can make decisions.

Research and Analysis on the Development of New Energy Vehicles in the Context of Low-Carbon Environmental Protection

Driven by the global wave of low-carbon and environmental consciousness, the new energy vehicle industry is poised for unprecedented development opportunities. As environmental challenges become increasingly severe, governments worldwide have implemented a series of policies to encourage the research and promotion of new energy vehicles. These vehicles not only play a crucial role in alleviating environmental pressures but also foster technological innovation and the transformation of economic structures. This paper, from the perspective of low-carbon and environmental protection, examines the current development status and technological pathways of new energy vehicles, explores the impact of policy support and industry chain construction on their advancement, and provides a forecast of the market outlook. Through a thorough analysis, a better understanding of the role and significance of new energy vehicles in low-carbon environmental protection can be gained, offering scientific foundations for future policy-making and industrial development.

Crisis PR in the New Media Era: the Impact on the Reputation of Chinese New Energy Businesses

The self-transformation of new media presents unprecedented challenges for crisis PR. In recent years, the rapid development of new energy vehicles in China has often led to widespread criticism when facing crisis Public Relations (PR) issues. This study takes Tesla, NIO, and Li Auto, three companies developing new energy vehicles in China. It compares their crisis PR strategies in the new media era, exploring their impact on corporate reputation. By collecting the quantity of cognitive and emotional assessments of positive, neutral, and negative nature, and calculating the conversion between evaluations of different natures, this research finds that incorrect response strategies lead to a decrease in both emotional and cognitive reputations, while successful response strategies primarily enhance cognitive reputation. Successful strategies may involve subjective proactive measures, characterized by swift or preemptive PR, positive emotional attitudes, and sustained responsiveness. This study provides empirical insights into understanding and addressing the challenges of crisis PR in the new media era, offering references for companies to formulate crisis management strategies.

LRSDet: lightweight remote sensing target detection network with local-global information awareness and rapid sample assignment

Abstract With the rapid development of aerospace and unmanned aerial vehicles, using neural networks for object detection in optical remote sensing images (O-RSI) has encountered heightened challenges. The optical remote sensing images have the characteristics of complex geometric scenes, dense groups of objects, and significant multi-scale variations of objects; researchers need to use more complex models to achieve higher accuracy. However, this complexity also brings challenges to the application of lightweight scenes. Therefore, to cope with the trade-off challenge between model complexity and detection accuracy, we propose a lightweight network model LRSDet in this study. The model integrates local and global information processing mechanisms and introduces a fast positive sample assignment strategy to adapt to resource-constrained embedded and mobile platforms. By constructing a lightweight feature extraction network and a lightweight path aggregation network and incorporating the ESM-Attention module, the feature extraction capability of the model in complex remote sensing scenarios is significantly improved. In addition, the application of a dynamic soft threshold strategy further optimizes the positive sample selection process and improves the detection efficiency of the model. Experimental on the O-RSI datasets DIOR, NWPU VHR-10, and RSOD, while analyzing model real-time performance on aerial video and embedded devices, outperforming other state-of-the-art methods.

A privacy protection scheme to resist ring selection attack in peer-to-peer double auction V2V electricity transaction for charging stations

With the development of Electric Vehicles (EVs), the number of public charging piles cannot meet the immediate charging needs of EV users sometimes. The charging stations provide flexible electricity transaction schemes for users by managing charging piles and Vehicle-to-Vehicle (V2V). Users can freely choose transaction partners and submit transaction plans to multiple charging stations. The mobility of EVs results in users randomly selecting rings multiple times, which may cause them to appear in different rings. Attackers can link multiple transaction plans and infer user privacy information. The leakage of transaction information may threaten the fairness of transactions. Aiming at the above situations, a privacy protection scheme to resist ring selection attack in Peer-to-Peer double auction V2V electricity transaction for charging stations is proposed. Firstly, the ring signcryption algorithm is improved to resist ring selection attack. Secondly, a real identity verification algorithm is designed to track malicious users. Theoretical analysis proves that the security of the proposed privacy protection scheme. The elliptic curve point multiplication is used in the scheme to improve computational efficiency. Experiments show that this scheme has low computational and communication costs. Verification cost increases slowly with the increase of the number of ring members.

Cable risk and resilience in the age of uncrewed undersea vehicles (UUVs)

The risks associated with near total global economic reliance on the undersea telecommunication cable network is increasingly acknowledged by scholars and government officials worldwide. Recent disruptive incidents affecting seabed infrastructure—including cables—have served only to highlight the network’s vulnerability. This article examines how fast-paced technological developments in uncrewed undersea vehicles (UUVs) will affect the security of this critical infrastructure. To that end, this research article asks what advantages this frontier technology offers to those seeking to harm the cable network, and, from the opposite perspective, for those concerned about protecting it. The conclusions offered by this article will arm policymakers and other interested parties with greater knowledge about the emerging impact of UUVs on the security of seabed infrastructure and the undersea telecommunication cable network more specifically.

Engine Mass Flow Estimation through Neural Network Modeling in Semi-Transient Conditions: A New Calibration Approach

Nowadays, engine experimental research represents a very expensive field within the automotive industry, but it remains fundamental for engine and vehicle development. The present work aims to investigate a novel approach for engine control system calibration, by adopting machine learning techniques to model physical parameters of the engine starting from experimental data measured at the test bench. The main goal is to create a methodology which accelerates the calibration process without losing accuracy. A model that estimates air mass flow is created by adopting either a tree ensemble model or an artificial neural network trained on a small dataset, which was previously acquired at the test bench using a random calibration of the volumetric efficiency map. The model’s performance is first validated on a larger, random dataset. Then, the volumetric efficiency calculated from the air mass flow model estimation is used to calibrate the transfer function of the Engine Control Unit. Finally, the sensitivity of the model error correlated with the number of data points acquired is used in order to determine the best practice for a Design Of Experiment, which minimizes data acquisition. The methodology proposed can lead to reduced time and costs of the whole calibration process of the engine, without losing accuracy. The analysis was conducted on the entire vehicle, which is crucial for drivability, especially in motorcycles since they are highly sensitive to air-to-fuel ratio adjustments. This work demonstrates that machine learning models can be adopted for the fine-tuning of the calibration process, which is normally performed manually.

Propulsion and energy extraction performance of wave-powered mechanism considering wave and current

Wave-powered mechanism can extract wave energy as propulsion, enabling long-term ocean observation. The kinematic properties of wave propulsion mechanisms are susceptible to ocean current loads in addition to wave effects, presenting complex dynamic behaviors. In this paper, to understand the effects of ocean currents on the kinematic performances of wave propulsion mechanism, the dynamic performance of a wave propulsion mechanism in the combined condition of wave and current is analyzed based on numerical simulation method for fluid-rigid body coupling and experimental methods. The performance of the propulsion mechanism against the current under counter-current conditions and the contribution of flow to the propulsion performance under co-current conditions are discussed. The intrinsic relationship between wave height, current velocity and current direction is investigated, and the energy extraction performance of wave propulsion mechanism is analyzed. According to the results, the ability of propulsion mechanism to resist counter-current is obtained, and it is found that as the counter-current velocity increases, the hydrofoil will stall and the propulsion force and efficiency decreases significantly. Whilst, the co-current has a promoting effect on the forward of the mechanism, but within a certain range of co-current speed, the mechanism will be negatively affected by the shedding vortex. And the discrimination mechanisms for wave-dominated propulsion and current-dominated propulsion are obtained. This work reveals the combined effect of waves and currents on the motion performance of wave propulsion mechanism, providing a reference for the development of surface vehicles that utilize multiple ocean energies for synergistic propulsion.

A Mountain Gazelle Optimization (MGO) for Enhancing the Deep Learning Performance in Various Operating Systems

This study introduces a novel optimization framework that assesses and enhances deep learning algorithm performance across autonomous car operating systems. The framework generates synthetic performance measures, including inference time, memory use, CPU/GPU utilization, and accuracy, to evaluate algorithm performance. By employing the Mountain Gazelle Optimizer (MGO), the study identifies the best deep learning algorithm and operating system setup for accuracy and resource efficiency. The proposed methodology normalizes performance indicators, defines a fitness function to assist optimization, and then iterates through numerous configurations to discover the optimum option. Extensive trials and scenario comparisons validate the effectiveness of our approach. Computational efficiency and accuracy improved significantly, revealing the ideal autonomous car system performance combinations. This study not only enhances deep learning optimization but also provides practical instructions for building robust autonomous car systems in varied operating contexts, thereby informing the development of future autonomous vehicles and offering actionable insights for developers and researchers in the field.

A comprehensive review on safe reinforcement learning for autonomous vehicle control in dynamic environments

To operate safely in a dynamic environment, autonomous vehicles must possess the same level of predictive driving abilities as human drivers and must be capable of anticipating the future actions of other dynamic objects in the environment, especially those of neighboring vehicles. The development of safe autonomous vehicles (AVs) poses a challenging task as it requires algorithms that can make real-time decisions in unpredictable circumstances. Reinforcement learning (RL) presents a promising approach for AV control, as it utilizes trial and error to enable optimal decision-making. However, traditional RL algorithms are unsuitable for safety-critical applications, as they may explore unsafe actions, potentially resulting in accidents. Safe reinforcement learning (SRL) algorithms have been developed to address this issue, prioritizing safe and reliable decisions. These algorithms incorporate constraints to prevent unsafe actions or utilize techniques to estimate action risk and avoid actions deemed excessively risky. Despite computational challenges, SRL holds significant promise for AV control, and is likely to play a crucial role in developing safe and reliable systems. SRL methods are critical for the general adoption of autonomous vehicles by guaranteeing their safety and reliability. These algorithms utilize methods like uncertainty and risk estimation along with penalty functions, to avoid excessively risky actions and have the potential to significantly reduce accidents and build public trust in autonomous driving. However, there are challenges that need to be addressed, such as the dynamic nature of real-world traffic, high computational costs, and the diversity of road design; and these varying conditions make the designing, testing, and validating of SRL algorithms difficult. Despite these challenges, SRL presents a promising solution, through integrating new sensing technologies and machine learning techniques, to develop safe, efficient, and environmentally friendly transportation systems.