Traditional Vehicle Research Articles

IoT Enabled Motor Drive Vehicle for the Early Fault Detection in New EnergyConservation

An IoT-enabled motor drive vehicle integrates Internet of Things (IoT) technology with traditional vehicle systems to enhance control, monitoring, and automation. Sensors and connected devices within the vehicle collect real-time data on parameters such as speed, battery status, motor performance, and environmental conditions. This data is transmitted to cloud-based platforms for analysis, enabling remote diagnostics, predictive maintenance, and optimization of vehicle performance. IoT integration also facilitates features like vehicle tracking, smart navigation, and user-specific adjustments, improving overall efficiency, safety, and user experience. This paper investigates the utilization of IoT enabled Programmable Logic Controller (PLC) technology to enhance fault detection in new energy vehicle (NEV) motor drive systems. With the increasing adoption of electric vehicles, ensuring the safety and reliability of motor drive systems becomes paramount. The study begins with a comprehensive review of existing literature, examining various fault detection methodologies and technologies. Subsequently, simulation analyses are conducted to evaluate the performance of IOT enabled PLC-based fault detection algorithms under different operating conditions. This paper presents the numerical results of fault detection in new energy vehicle (NEV) motor drive systems using Programmable Logic Controller (IOT enabled PLC) technology. Through comprehensive simulations and experimental validations, the IOT enabled PLC-based fault detection algorithms achieved an average detection accuracy of 93% across various fault scenarios. The response time of the fault detection system.

A study of powertrain parameter optimization based on performance requirements of a vehicle platform

Powertrain top down design is a vital process for vehicle platformization and performance development. To obtain the optimal component size of a plug-in hybrid electric vehicle (PHEV) platform, this study proposes an optimization framework with the goal of minimizing performance redundancy. First, we analyze the characteristics of the powertrain architecture, and propose the indicators through performance gradient planning of the vehicle platform. Then we establish the mathematical model of the powertrain, and formulate the optimization problem. Particle Swarm Optimization (PSO) is used to solve the problem and a platform optimization problem with eight vehicle models is performed. Optimization results show that compared to traditional single vehicle powertrain optimization, platform based powertrain optimization can greatly reduce the number of components and save development costs through the sharing of components. With only three electric machines, two engines, two generators, and two sets of transmissions, the performance requirements of the entire platform can be met, and the maximum performance redundancy of different vehicle models is only 0.205. It can be seen that platform based powertrain optimization can greatly reduce vehicle costs and improve profit margins.

Debt Risk Analysis of Automotive Enterprises

In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.

Last mile delivery with drones: A carbon emissions comparison

The development and potential adoption of drones or unmanned aerial vehicles as delivery vehicles creates incredible opportunities and unique challenges for last mile delivery. This research first presents a last mile delivery fleet model with drones that can be further modified and expanded over time. The model shows the optimal number of drones needed based on deterministic or stochastic demand using both traditional charging and battery swapping. The research then compares the carbon emissions of four delivery modes: traditional internal combustion delivery vehicles, all-electric vehicles, plug-in hybrids, and drones within the context of last mile delivery. Findings reveal that the breakdown of carbon emissions by delivery modality depends on parameter assumptions, ambient temperature, delivery radius, electric grid pollution rate, and number of customers.

Pantograph fault prediction of urban rail transit vehicles based on edge feature extraction and detection

Nowadays, as an indispensable part of urban infrastructure, urban rail transit (URT) vehicles have also developed rapidly. A large amount of manpower, material resources, and financial resources need to be invested in the construction process of URT. For URT vehicles, research on more accurate fault prediction methods can save a lot of maintenance costs and improve the reliability of URT construction. As an important electrical equipment for urban rail transit vehicles to obtain electric energy from the catenary, the operation of rail transit vehicles puts forward higher performance requirements for the pantograph. For solving the problems of low accuracy of fault prediction, over reliance on practical experience and high cost of fault prediction in the application of traditional URT vehicle pantograph fault prediction model. Combining sensor network and artificial intelligence algorithm, this paper analyzed the traditional rail transit vehicle pantograph fault prediction model, and verified it through comparative experiments. Through the comparative analysis of the experimental results, this paper can draw a conclusion that compared with the traditional rail transit vehicle pantograph fault prediction model, the rail transit vehicle pantograph fault prediction model has higher fault prediction accuracy, less model response time, lower risk of pantograph failure, higher model application satisfaction, and the accuracy of fault prediction increased by about 6.6%. The rail transit vehicle pantograph fault prediction model can effectively improve the accuracy of vehicle pantograph fault prediction, which can greatly promote the safety of URT and promote the intelligent process of URT.

Car-following model based on artificial potential field with consideration of horizontal curvature in connected vehicles environment

Connected vehicles (CVs) will gradually replace traditional vehicles to become the main components of traffic flow. Studying the car-following behavior characteristics is crucial for improving traffic flow stability and safety in CVs environment. Additionally, the radius of road curvature significantly impacts vehicle driving behavior, making it necessary to consider it for the car-following models of CVs. The artificial potential field (APF) theory can more accurately and comprehensively depict various microscopic driving behaviors, offering a new approach for modeling vehicle microscopic behavior. Firstly, this paper constructs the attractive and repulsive potential fields considering horizontal curve curvature based on a road coordinate transformation model. Secondly, an Artificial Potential Field-Based Car-Following Model Considering Curvature (APFCCM) in connected vehicles environment is proposed. Finally, the model is calibrated and validated using the Hangzhou - Xifu Freeway dataset from the Tongji Road Trajectory Sharing (TJRD TS) platform, and compared with the full velocity difference model(FVDM), the Intelligent Driver Model (IDM) and the Driving Risk Potential Field Model (DRPFM). The results show that the APFCCM performs well in trajectory simulation, model accuracy, and scenario adaptability, and it has the lowest mean absolute error(MAE) and root mean square error(RMSE) in position, speed, and acceleration metrics.

ANALYSIS OF TYPES OF VEHICLE POWER PLANTS: ADVANTAGES AND DISADVANTAGES

This paper analyzes the advantages and disadvantages of vehicle power plants, examines the development and modern types of hybrid power plants used in transport. An analysis of carbon dioxide (CO2) emissions into the atmosphere from vehicles equipped with different types of powertrains, including internal combustion engines (ICE), hybrid drives, and electric motors, was performed. Studies have been conducted of emissions that occur during the operation of vehicles, as well as emissions associated with the production of high-voltage batteries for electric vehicles and the production of electricity required for their charging. It was concluded that electric vehicles, despite their popularity as environmentally friendly vehicles, can have a significant impact on the environment. This is due to high CO2 emissions during the production of batteries, which are a key component of electric vehicles. The production of these batteries requires significant energy costs and the use of various materials, the extraction and processing of which also lead to greenhouse gas emissions. In addition, the environmental impact of electric vehicles largely depends on the sources of electricity used to charge them. When electricity is generated from fossil fuels such as coal or natural gas, overall CO2 emissions can be significantly higher, reducing the environmental advantage of electric vehicles over internal combustion engines. On the other hand, the use of renewable energy sources, such as solar or wind energy, to charge electric vehicles significantly reduces CO2 emissions into the atmosphere. The work also considers hybrid vehicles that combine an internal combustion engine and an electric motor. Such cars usually have lower CO2 emissions during operation compared to traditional diesel vehicles, but their environmental impact also depends on the production and disposal of batteries. To make informed decisions about the choice of powertrain and the development of transport infrastructure, all aspects must be taken into account, including the production of vehicles, their operation and disposal. It is also important to develop technologies to reduce emissions and increase energy efficiency at all stages of a vehicle's life cycle. The work concluded that to reduce the overall impact of vehicles on the environment, it is necessary not only to develop electric vehicles, but also to combine the advantages of internal combustion engines and electric motors (develop a hybrid drive), improve battery production technologies, expand the use of renewable energy sources, and implement other measures to reduce CO2 emissions.

Interior sound quality evaluation and forecasting of passenger vehicles based on hybrid optimization neural networks

The interior noise of vehicles directly affects the comfort of the occupants, necessitating precise evaluation and control. Existing research has focused on constructing mappings between objective parameters and subjective perceptions of noise, where back propagation neural networks (BPNNs) are widely used due to their strong nonlinear mapping capabilities. However, the selection of initial weights and thresholds can affect the predictive accuracy of BPNN. This study developed a BPNN model optimized by an intelligent algorithm for predicting the level of subjective annoyance of passengers during the movement. Initially, objective parameters of interior noise were obtained through acoustic signal processing techniques, and five parameters were selected for studying subjective annoyance through correlation analysis and two-tailed tests. Meanwhile, the actual subjective ratings of passengers on interior noise were captured for subsequent training of the model and testing of the results. Finally, the established sparrow search algorithm (SSA) and genetic algorithm (GA) optimized BPNN were used to predict subjective evaluations. The predictive accuracy and efficiency of the model were significantly improved upon validation, providing a viable alternative to traditional passenger vehicle noise assessment experiments and valuable references for future noise control and optimization efforts. The experimental results are consistent with the view that the neural network model optimized with a mixture of intelligent algorithms is closer to the passenger’s subjective annoyance level having higher accuracy and efficiency.

Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data

Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling conditions were collected. Key influencing factors on the thermal-related health of batteries were identified. The ML model’s effectiveness was evaluated against experimental test data. The ML model proved effective in predicting and analyzing battery thermal health, suggesting its potential for use with the thermal management system.

Estimating vehicle sideslip angle through kinematic and dynamic contributions: Theory and experimental results

Vehicle lateral stability plays an important role within vehicle passenger safety. The study of lateral stability is typically related to investigating the dynamics of relevant vehicle states: among these, the vehicle sideslip angle ([Formula: see text]) emerges as a prominent candidate. Sideslip angle measurement is expensive and impractical, hence estimation techniques are often used, typically based on Kalman filters or neural networks, both with their issues. This work presents an alternative estimation method based on the idea of splitting sideslip angle into kinematic and dynamic contributions, and by observing that the kinematic contribution is straightforward to estimate. Therefore, efforts are devoted into estimating dynamic sideslip angle, which is herein obtained through a parametric interpolation harnessing lateral acceleration. Only data available from traditional vehicle onboard sensors are used in the process. Experimental results are presented along several manoeuvres on a full-scale vehicle, with the estimator running online within a dSPACE unit, ultimately supporting the efficacy and real-time feasibility of the proposed approach.

Enhancing small and medium-sized businesses through digitalization

SMEs play an important role in all developed and developing countries contributing highly to employment, new business ideas, and the economy. However, owing to various challenges related to the modern digital environment and various limitations in terms of financial and technical capacities as well as digital literacy, there are numerous challenges that SMEs experience in improving their businesses’ performances through effective use of digital technologies. This research aims to examine the adopted trends of SMEs from Slovenia in the deployment of digital technology, the challenges they encounter, and the support they require in their digital evolution. Reacted firms mainly utilize traditional vehicles such as websites and teamwork platforms for online communication and presence. Nevertheless, utilization of such sophisticated digital tools as blockchain is still quite scarce because they are considered challenging to implement and applicable to a narrow range of industries. Variance analysis identifies differences in digitalization challenges between small, micro, and medium enterprises. Small companies confront greater financial constraints and require tailored support compared to larger SMEs, with a focus on improving employee digital competencies. While digitalization brings benefits like elevated processes and remote working, SMEs still struggle with differentiation and cultural changes.

Study on the Development Status and Promotion Strategy of Zero-Emission Commercial Vehicles in China under the Background of the Dual Carbon Target

The adoption of new energy vehicles (NEVs) is an effective strategy for pollution reduction, especially for high-emitting commercial vehicles. This paper systematically reviews the promotion policies and development status of zero-emission commercial vehicles (ZECVs) in China, with a focus on diverse application scenarios. Comprehensive policies, including subsidies, right-of-way, infrastructure development, and environmental protection incentives, have significantly advanced NEV adoption, as demonstrated by Shenzhen’s full electrification of buses and the extensive deployment of zero-emission trucks. Despite the overall slow development of ZECVs, regions in southern China and developed areas exhibit better progress. Medium and large passenger vehicles (MLPVs) have achieved a zero-emission rate of around 40%, contrasting with the significantly lower rates of 1.52% for mini and light trucks (MLTs) and 0.44% for medium and heavy trucks (MHTs). Electrification promotion varies significantly in different application scenarios, with buses leading at over 90% zero-emission rates, followed by the airport (24%) and port (16%) vehicles. The electrification of sanitation, logistics, and key industry transport, through lagging, is enhanced by targeted policies and local industry. Buses are designated as the highest priority (Level 1) for electrification transition while intercity logistics and vehicles in key industries are categorized as the lowest priority (Level 4). In addition, policy recommendations, including tailored strategies for ZECV promotion and emission reductions in traditional commercial vehicles, are put forward to provide guidance and reference for setting future zero-emission promotion goals and policy direction for commercial vehicles in subdivided application scenarios.

A Two-Echelon Routing Model for Sustainable Last-Mile Delivery with an Intermediate Facility: A Case Study of Pharmaceutical Distribution in Rome

This paper introduces a two-echelon optimization model for the integrated routing of an electric vehicle (EV) and a traditional internal combustion engine vehicle (ICEV) in an urban environment. The scientific context of this study is sustainable urban logistics. The case study focuses on the distribution of pharmaceuticals in the metropolitan area of Rome. Distributing pharmaceuticals in large cities presents significant challenges, including heavy traffic congestion, the need for strict temperature control, and the maintenance of the integrity and timely delivery of sensitive medications. Furthermore, the complexity of urban logistics and adherence to regulatory requirements introduce additional layers of difficulty. Therefore, the implementation of fast and sustainable distribution mechanisms is crucial in this context. Specifically, the model seeks to minimize both total CO2 emissions and transportation costs while optimizing the use of an EV and an ICEV, all while ensuring that service level requirements are met. Computational results demonstrate the effectiveness of the proposed method in improving the sustainability of pharmaceutical distribution.

Machine Vision-Based Method for Reconstructing the Vehicle Coordinate System in End-of-Line ADAS Calibration

To ensure the accuracy and reliability of Advanced Driver Assistance Systems (ADAS), it is essential to perform offline calibration before the vehicles leave the factory. This paper proposes a method for reconstructing the vehicle coordinate system based on machine vision, which can be applied to the offline calibration of ADAS. Firstly, this study explains the preliminary preparations, such as the selection of feature points and the choice of camera model, combining actual application scenarios and testing requirements. Subsequently, the YOLO model is trained to identify and obtain feature regions, and feature point coordinates are extracted from these regions using template matching and ellipse fitting. Finally, a validation experiment is designed to evaluate the accuracy of this method using metrics such as the vehicle’s lateral and longitudinal offset distances and yaw angle. Experimental results show that, compared to traditional vehicle alignment platforms, this method improves reconstruction accuracy while reducing costs.

Evolutionary Game-Based New Energy Vehicle Supply Chain Strategies That Consider Carbon Reduction and Consumers’ Low-Carbon Preferences

The rapid development of the new energy industry has intensified the competition among companies. Finding solutions to achieve technological innovation, carbon reduction, and to earn consumers’ confidence has become a pressing challenge. In this research, we aim to develop a four-party evolutionary game model involving government, manufacturers, dealers, and consumers to examine the strategic decisions made by these parties in order to accomplish carbon emission reduction goals. We will perform numerical simulations to analyze the strategic choices of each party and the relevant influencing factors. The results suggest the following: (1) The tax hike on traditional car production is less than the innovation expenses for new energy vehicles, leading manufacturers to lean towards manufacturing traditional vehicles. (2) The rise in taxes resulting from the manufacture of conventional vehicles will influence manufacturers’ strategic decisions, whereas the expenses related to technological advancements will have a more significant effect on manufacturers’ strategic choices. (3) Compared to dealers, manufacturers’ strategic choices are more significantly influenced by consumers’ awareness of low-carbon preferences. (4) In the early stages of technological innovation, the government typically offers incentive subsidies to manufacturers to boost technological innovation activities. Whereas, in the later stages of technological innovation, the government usually provides direct subsidies to consumers to encourage the market acceptance and widespread use of innovative products.

An interaction-enhanced co-evolutionary algorithm for electric vehicle routing optimization

Recently, the electric vehicle routing problem (EVRP) has emerged as a significant challenge in the transportation field. Unlike traditional vehicle routing problems, EVRP requires optimizing not only the customer route but also the charging scheme. This implies that routes must be planned while considering the availability of charging stations, and charging decisions must be made based on the specific route structure. Although the dual-population-based co-evolutionary algorithm (DPCA) has been proposed to address this coupling relationship, there is still room for further performance improvement. Therefore, this paper proposes an interaction-enhanced co-evolutionary algorithm (IECA) that facilitates a more effective collaborative search between routing and charging optimizations. For routing optimization, we propose an improved ant colony optimization method with a novel pheromone update approach to generate diverse route solutions. Specifically, we utilize information from the charging population to guide the pheromone update process. In contrast to DPCA, which solely relies on the best solution, we establish interaction among multiple solutions from both the routing and charging populations, which can better contribute the information interaction between two populations. Furthermore, we devise a novel population update strategy that enhances the selection of promising solutions. Experimental results validate the effectiveness of the proposed algorithm by demonstrating its ability to avoid local optima. Moreover, it achieves a reduction of approximately 4% in the average route distance across two public test suites. The source code is available at https://github.com/ShouLiang-Z/IECA.

Electric automobility and the race to road transfer: ‘Formula E’ and ‘Extreme E’ in documentary film

As the need for changes in transportation grows, the transition to sustainable mobility is being envisioned in varying ways. Shifting from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs), along with changing mobility habits, will both be necessary. What has been neglected from the study is the role of sport in aiding this transition. Motorsport has long served dual roles of serving as a technological testbed and center for culture formation around street cars. Coupling one of the largest sporting platforms in the world with the missions of climate change awareness and technological advancement, two racing leagues are catalyzing the move to electric mobility. Formula E and Extreme E have released documentary films showcasing their future goals. To understand the role of electric racing in shaping the transition to sustainable mobility, this paper uses a grounded theory approach to identify relevant themes of the EV transition, while a narrative analysis is used to foreground the overall storytelling aspect of the ecocinematic films. The racing EV emerges as a site of collective collaboration: meeting the needs of the future within the framing of traditional motorsport, and merging traditionally masculinist narratives of automotive technology with prototypically feminized environmentalism.

Research on whether lithium batteries can achieve pollution-free new energy vehicles and other new energy vehicles way

Under the conditions of rapid development in the world today, all kinds of new inventions emerge in an endless stream. Among them, new energy vehicles are developing rapidly around the world and have become the focus of research and development and application in major automotive countries. Firstly, this paper expounds on the strategic significance of developing new energy vehicles from the aspects of energy saving, emission and pollution-free, low energy consumption, and high efficiency. Compared with traditional LPG vehicles, new energy vehicles have an unknown advantage. However, can new energy vehicles really do good without harm, as we know? When we watch many electric car promotion exhibitions, we can always hear them speak highly of the product, but from the perspective of the manufacturer, they will also be vague about some potential problems in order to sell. For example, for electric cars, the pollution caused by the production of batteries and the disposal of batteries after they are scrapped has not been mentioned. While looking forward to the future development trend of electric vehicles, hybrid vehicles, and electric vehicles, we also do not forget to question their potential dangers. Through the investigation and analysis of information, this paper finally summarizes the advantages and shortcomings of electric vehicles and the prospects for the future of new energy vehicles.

Study of Interaction Interface of Vehicles for Automatic Driving

With the rapid development of the vehicle and information industries, the traditional vehicle has been transformed from a simple travel tool to an intelligent vehicle with comprehensive functions, intelligence, and rich information. As a standard function of intelligent vehicles, automated driving will greatly reduce or even replace humans for vehicle control, and the human-machine interaction system pays more attention to displaying the driving status and prompting the driver in an intuitive way, to make the driver fully trust the vehicle and obtain safe driving experience. In this paper, I study the development and current application of the interaction interface of vehicles. The main study objective is HUD (Head-Up Display). By introducing different technologies of HUDs, the study describes the examples of interaction interface of HUDs, analyzes the pros and cons of the different applications, and thus presents the direct impression and typical futuristic experience of interaction interface of vehicles. The paper also discusses the human’s natural behavior while driving and related future technology advancement is developing, and the driving environment which is the most important topic for the whole industry. The paper analyzes the interaction requirements of automated driving vehicles and summarizes the development trend of the human-machine interaction system for automated driving, which results in better understanding of interaction interface of vehicles.

Analysis of the Impact of Hybrid Power System on Teachers' Psychology and Teaching Environment under Smart Energy Transformation and Energy System Planning

For this paper we study the structure and operational characteristics of plug-in hybrid Vs, Using the life-cycle evaluation method, The analysis model of energy consumption and environmental impact of plug-in hybrid electric vehicle fuel cycle suitable for China's national conditions is established, With the unit mileage of the plug-in hybrid car as the functional unit, Using the whole vehicle NEDC test cycle and combined with the relevant literature, Fuel consumption and environmental emission factors for this functional unit, The fuel cycle energy consumption, conventional gas emissions and greenhouse gas emissions are obtained, Evaluate the effect of power structure change on the above indexes, And compared the plug-in hybrid vehicles with conventional gasoline vehicles and compressed natural gas vehicles. As follows: compared with the current technical level of gasoline cars, under the background of road traffic, plug-in hybrid car fuel consumption is reduced by 40.7% less than gasoline cars, VOC, NOx, SO2 and greenhouse gas emissions than traditional gasoline vehicles reduced by 31.5%, 15.2%, 66.7% and 55.8%, this to alleviate increasingly severe urban air pollution is of great significance. Plug-in hybrid vehicles offer advantages over traditional gasoline cars in terms of energy consumption and emissions, both at the vehicle level and throughout the fuel cycle. Energy consumption is 56.3% lower, including a 56.4% reduction in crude oil usage, reducing oil dependency and promoting energy security. Environmentally, plug-in hybrids emit significantly less than gasoline cars, with VOC, NOx, SO2, PM10, and CO emissions reduced by up to 56%. Greenhouse gas emissions, including N2O, CH4, and CO2, are also lower. Additionally, emissions tend to be concentrated upstream, facilitating centralized pollution control.