Vehicle Industry Research Articles

Optimizing electric vehicle routing under traffic congestion: A comprehensive energy consumption model considering drivetrain losses

Energy consumption is a critical concern within the electric vehicle (EV) industry. Despite its substantial impact, drivetrain losses have been inadequately addressed in some studies. This paper introduces drivetrain losses as a nonlinear function of velocity, incorporating them into the comprehensive energy consumption model. Considering the influence of traffic congestion on vehicle speed, our proposed model integrates drivetrain losses, aerodynamic losses, tire losses, kinetic energy losses, and potential energy losses. The objective is to optimize routing with the aim of minimizing energy consumption. To tackle this problem, we present a dedicated adaptive large neighborhood search algorithm. Our numerical experiments reveal key findings: (1) Drivetrain losses represent a higher percentage of total energy consumption compared to aerodynamic and tire losses; (2) Drivetrain losses influenced by traffic congestion constitute a significant share, ranging between 63.82% and 72.08% of total energy consumption; (3) Energy consumption experiences notable increases under the impact of traffic congestion.

Open ECU EPS System, preliminary design using a Hardware-in-the-loop approach

The introduction of mechatronic systems in vehicles opens the possibility to a wide range of vehicle functionality. These systems allow active control of vehicle enabling a new level of driving comfort and safety. The development of these complex technological systems, however, requires a long time to integrate mechanical, electrical and software components. Innovative development strategies and devices are hence crucial to accelerate the integration and deployment of these mechatronic systems.In order to fulfil these needs, this paper presents the process to size a development electronic power steering which aims to support vehicle industry during testing and validation of innovative steering-based control strategies, including autonomous driving, steer-by-wire and all-wheel steering applications. Using an hardware-in-the-loop test bench for a wide range of vehicle maneuvers a stock power steering unit has been characterized taking into account both driver and assistance motor torque contributions.

A Review on Poles Demagnetization in Permanent Magnet Motors

Permanent magnet motors like BLDC and PMSM are widely used in industry and electric vehicles. Demagnetization of permanent poles in motors is the biggest problem. To design a machine that has good efficiency and no demagnetization problems, real-time fault diagnosis is necessary. Various causes of demagnetization, detection, and measuring methods are discussed by researchers analyzed in this paper. Apart from the comparison of different methods of fault detection, a general comparison among the different methods of various strategies has been made based on the torque ripple, analysis of harmonics, current waveform, back e.m.f. waveform and losses of the PM motor. Finite Element Analysis (FEA) and analytical method adopted by some researchers to get the results. Moreover, considering the importance of BLDC in the electric vehicles (EVs) industry, the selection of the best method to detect demagnetization has been discussed.

Investigation of Air Quality for Malegaon Region

Abstract: The purpose of this study is to examine the quality of the air in the Malegaon region by identifying, quantifying, and evaluating contaminants and their effects on the environment and public health. The Malegaon region's air quality has been a rising topic of concern because of industrial operations, vehicle emissions, and other human-caused factors. elevated concentrations of many pollutants, including Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Particulate matter (PM2.5, PM10), and Ozone (O3).It's an intriguing problem to comprehend the mechanics of air pollution in a semi-urban location like Malegaon. The dispersion and concentration patterns of pollutants are influenced by various factors, including terrain, meteorological circumstances, and industrial expansion. The challenge is further complicated by evaluating the combined effects of several pollution sources on air quality. In order to thoroughly evaluate the state of the air in the Malegaon region, this study uses a multidisciplinary method that combines statistical analysis, atmospheric modeling, and air quality monitoring.

Development of a New Vertical Dynamic Model of a Rail Vehicle for the Analysis of Ride Comfort

The rail vehicle industry wants to produce vehicles with higher speeds, to maintain and increase its market share. However, when the speed of the vehicle increases, it may have an undesirable effect on ride comfort, in terms of ride dynamics. Recent developments towards lighter and faster vehicles make the problem of ride comfort at higher speeds increasingly important. Focusing on the behavior of flexible rather than rigid body behavior should not be neglected when designing long and light car bodies. There are several approaches to incorporate body flexibility in multibody simulations and they have some superiorities and weaknesses. In this study, an efficient and accurate vertical dynamic model for the ride comfort analysis is developed and implemented in a commercial object-oriented modeling (OOM) software Dymola (2015 FD01) which uses the open-source code Modelica. This model includes car body flexibility with the assembling of a rigid body approach. The developed model is compared to a three-dimensional vehicle model in the commercial Vampire software (Pro V5.50) at different velocities. For the vertical ride comfort analysis, the ISO 2631-1 standard was used for both the developed model and the three-dimensional model. The results are presented as acceleration history and awrms—weighted r.m.s (root mean square) of accelerations—as required by the standard. The developed model has shown its feasibility in terms of its efficiency and accuracy for the vertical ride comfort analysis. The accuracy of the model is evidenced by the fact that the car body vibration level at high speeds shows minor differences compared to the results of the Vampire, which is a validated commercial software in the area of rail vehicle dynamics. The approach involving the assembly of rigid bodies is applied for the first time for high-speed trains in dynamical modelling, with flexible car bodies for ride comfort analysis. Furthermore, it can be used for parametrical studies focusing on ride comfort, thereby offering a quite beneficial framework for addressing the challenges of ride comfort analysis in high-speed rail vehicles. Improvements for and analyses of other aspects are also possible, since the optimization and other useful libraries are readily available in Dymola/Modelica.

The Impact of Innovation Adoption on Sustainable Business Development in China: the role of Environmental, Social, and Corporate Governance Performance

ESG as a phenomenon appears to be consistent with high quality development of economy of country like China, hence, implementation of ESG is essential to embrace sustainability. Thus, it is imperative to scrutinize the its effectiveness and, in this lieu, the study attempts to scrutinize the effectiveness of innovation adoption on the environmental, social and governance performance, and SBD of the electric vehicle industry in the context of China. Along with it, mediating role of ESG performance has also been explored in the study. Primary data was collected for the study and PLS-SEM methodology was used to evaluate the collected data. Results showcase that innovation adoption is positively correlated with ESG performance and SBD of the electric vehicle industry in China. The outcomes also exposed that the ESG performances significantly mediates the relationship between innovation adoption and SBD. In the light of the evidence, the analysis recommends that organizations must value their ESG performance in order to formulate relatable policies.

A thorough investigation of the systems supporting E-vehicle charging stations and emerging trends

The percentage of electric vehicles (EVs) in the automobile industry which is increasing; yet, internal combustion engines (ICEs) remain operates a significant number of automobiles. The main barriers to EV use and environmentally friendly travel are their high price, short range, lack of battery charging infrastructure, and electricity pollution from EV chargers these barriers need to be taken down. Energy storage systems (ESS) with a substantial amount of energy are expensive, which contributes to the expensive of EVs. This article offers an analysis of EV technology, focusing on four primary areas of automobile technology. A detailed investigation is conducted into how grid architecture may be integrated with charging facilities and approaches to extend the longevity and productivity of electric vehicles. Since EVs are multienergy systems, reliable power control and management are necessary to maximize the consumption of energy. The assessment of EVs on the electric grid is concluded by an examination of various battery power management and control solutions in this paper. The review article concludes the suggested avenues for further study will be very helpful for researchers and EV users.

USING OF BIG DATA TECHNOLOGIES IN VEHICLE INDUSTRY

This work contains information about some aspects of the use of Big Data technologies in the transport sector, such as predictive analysis of vehicle breakdowns, issues of delivering large amounts of data from rolling stock. Some options for directions for further research on the use of Big Data technologies are considered. One of the problems of using Big Data technologies in the field of transport is the problem of delivering large amounts of information to data processing centers. If for urban transport, where coverage by networks of mobile cellular operators approaches 100%, there are no problems in sending large amounts of data (data can be accumulated on board the vehicle and transmitted at other times, for example, when the vehicle is in the park), then for this transport, such as trucks, trains, cargo planes, there is a certain problem in delivering large amounts of data. Keywords: Big Data technologies, vehicle industry, collecting data, DFM, risks.

Multi-agent simulation of autonomous industrial vehicle fleets: Towards dynamic task allocation in V2X cooperation mode

The smart factory leads to a strong digitalization of industrial processes and continuous communication between the systems integrated into the production, storage, and supply chains. One of the research areas in Industry 4.0 is the possibility of using autonomous and/or intelligent industrial vehicles. The optimization of the management of the tasks allocated to these vehicles with adaptive behaviours, as well as the increase in vehicle-to-everything communications (V2X) make it possible to develop collective and adaptive intelligence for these vehicles, often grouped in fleets. Task allocation and scheduling are often managed centrally. The requirements for flexibility, robustness, and scalability lead to the consideration of decentralized mechanisms to react to unexpected situations. However, before being definitively adopted, decentralization must first be modelled and then simulated. Thus, we use a multi-agent simulation to test the proposed dynamic task (re)allocation process. A set of problematic situations for the circulation of autonomous industrial vehicles in areas such as smart warehouses (obstacles, breakdowns, etc.) has been identified. These problematic situations could disrupt or harm the successful completion of the process of dynamic (re)allocation of tasks. We have therefore defined scenarios involving them in order to demonstrate through simulation that the process remains reliable. The simulation of new problematic situations also allows us to extend the potential of this process, which we discuss at the end of the article.

Высокотехнологичное автосборочное производство

In modern automotive production, high-tech robotic complexes and assembly lines are widely used for all four main production stages: pressing, welding, coating and assembly. The paper views industrial robot-assisted high-tech incomplete vehicle production using robotic manipulators. Studies have been conducted on the efficiency and productivity of automotive production through the use of a robotic assembly. Studies have been conducted on the efficiency and productivity of automotive production using a robotic assembly. A brief overview of scientific works on automation of production using robotic systems is presented. The discussed robotic complexes include various robots: assemblers, manipulators, auxiliary robots. The payback period of the robotic complex ranges from three to five years. Robotic complexes are successfully used in various factories, e.g. 115 robots are used at Volkswagen, 112 robots are used at Renault, 106 robots are used at Nissan. The average time period for assembling one basic vehicle product at the factory is 25 hours. The main characteristics of a robotic automobile assembly such as cycle of the assembly line; work pace; assembly line load factor are viewed. The influence of the main characteristics of the assembly line on productivity in robotic automotive production is estimated. The results show an increase in product output of up to 10% with an increase in the load factor by one tenth. The robotic assembly line efficiency calculation for automotive production shows that within five years the increase in net profit when using just one robotic assembly line will amount to about five million rubles. In addition, the output of products will significantly increase with RPA in automotive production. The effectiveness of using a robotized assembly line in automotive production is also shown.

Responding to consumer environmental awareness: A variational inequality approach to green manufacturing optimization

In response to increasing consumer environmental awareness, our study introduces a sustainable supply chain optimization model that stimulates competition among multiple green manufacturers within an uncertain market context. Each manufacturer crafts a selection of products showcasing various degrees of greenness, purposefully designed to meet the expanding demand of environmentally conscious consumers. This strategic model emphasizes profit maximization for green manufacturers, necessitating the definition of ideal greenness levels and production quantities for an array of product types. By employing variational inequality theory, we transmute the preliminary optimization model into an interconnected variational inequality problem. We propose an improved Euler algorithm, a proficient tool for attaining equilibrium solutions. Empirical scenarios inspired by the electric vehicle industry lend credibility to our model, indicating that the surge in consumer environmental awareness compels manufacturers to diversify their offerings and accentuate their products’ greenness levels. Intriguingly, the research suggests that escalating competition can incentivize manufacturers to elevate their products’ greenness levels, strategically countering potential profit erosion. In addition, the study reveals that intensifying consumers’ green utility sensitivity doesn’t invariably augment demand for manufacturers, even with significant improvements in product greenness. This paradox highlights the necessity for manufacturers to strategically adjust their production approaches in light of varying consumer preferences. Moreover, when the level of consumer environmental awareness achieves a certain level, the fierce competition might not always be harmful to some manufacturers and the industry if they can adopt effective strategies to improve green utility sensitivity.

Wireless Charging Technology Improvement for New Energy Vehicles

In the operation of new energy vehicles, the energy supply method is directly related to the range and stability of the vehicle, and wireless charging technology is becoming a hot spot in the industry because of its efficient and convenient characteristics. This paper will discuss the core technology of wireless charging in new energy vehicles, covering the related equipment construction, compensation network design, and the design of key components of wireless charging technology improvement in new energy vehicles. The article will analyse the detailed results of the actual operation of the system, aiming to provide theoretical support and practical guidance for the application of wireless charging technology in new energy vehicles, and at the same time promote the further development of the new energy vehicle industry.

Opportunities and challenges for commercial encryption applications in the Internet of Vehicles

The rapid development of vehicle networking technology has brought broad opportunities for commercial encryption applications, but it is also accompanied by many challenges. As an important guarantee for the information security of the Internet of vehicles, commercial encryption applications have obtained significant development opportunities in terms of technological innovation, market demand and policy support. However, the commercial encryption applications of connected vehicles are also facing challenges such as technical difficulties, lack of standardization, and increasing security threats. This paper aims to deeply analyze the opportunities and challenges of the commercial encryption application of the Internet of vehicles, and put forward corresponding development suggestions, in order to provide reference for the security development of the Internet of vehicles industry.

Partially observable deep reinforcement learning for multi-agent strategy optimization of human-robot collaborative disassembly: A case of retired electric vehicle battery

The burgeoning electric vehicle (EV) industry has precipitated a commensurate surge in the consumption of EV batteries, which are currently labor-intensive and inefficient for the recycling and disassembly of EV batteries. However, it is a potential trend to enhance the efficacy and safety of the disassembly of EV batteries based on human-robot collaboration (HRC) method. Because of the uncertainty of retired EV battery disassembly and the inefficiency of the existing disassembling sequence, it is difficult to be fully accomplish through HRC disassembly. The collaborative disassembly of EV batteries by humans and robots can be conceptualized as agents engaging with and learning from the environment, and modeled as a multi-agent Markov game process. This paper aims to address the challenge of HRC in the disassembly of EV batteries by recognizing the dual attributes of partial observability and non-smoothness in the suitable disassembly scenario. A partially observable multi-agent reinforcement learning environment is constructed, incorporating the structural aspects of the EV battery and the disassembly task. The framework is extended to the QMIX-HRC algorithm on the QMIX architecture (as a value-based multi-agent deep reinforcement learning algorithm), specifically designed to tackle the sequence problem in human-robot collaborative disassembly of EV batteries. The optimization results would yield a task sequence to offer maximal global co-benefit during the exploration iteration, facilitating a reduction in labor costs and an enhancement of co-efficiency. The viability of the QMIX-HRC disassembly strategy would be verified through the eventual disassembly sequence of a simulated battery pack through a real human-robot collaborative disassembly station.

Analysis of area saving and efficiency enhancement of photovoltaic system combined with thermoelectric generator

The use of solar photovoltaic systems for home, industrial, and electric vehicles has grown dramatically in recent years. It generates energy from a natural source known as sunlight, and the use of irradiation to the solar panel generates heat, which correctly restricts the Photo Voltaic (PV) panel's power generation. When irradiation increases, output power grows in proportion to input power, but efficiency falls due to temperature. Heat affects the entire area of the solar panel that generates electricity, making the panels less efficient at producing energy. To emphasise, several cooling systems for reducing heat have been introduced. Likewise, heat can be reduced by transforming energy, such as thermal energy, into electrical energy. Thermoelectric Generators (TEGs) are required in PV panels to reduce heat through energy conversion. In this work, experiments were carried out to examine the efficiency of PV panels with and without TEG combination. This experiment demonstrates at of solar irradiation at 730 W/m2. As a result, PV-TEG with a heat sink reduces the temperature of the PV panel up to 6 °C. In addition, this combination improves the TEG voltage up to 0.7 V and provides overall gain up to 1.8 times that TEG alone. Also, the area will be conserved due to the use of TEG behind the PV panel, and experimental verification shows that 15 % of the space is saved, and the overall efficiency has been raised by 14 - 16 % when compared to the PV panel working alone.

A novel fault diagnosis method for permanent magnet synchronous motor based on VS-Inception

The new energy vehicle industry has an increasing demand for safety, stability, and efficiency in the operation of essential equipment, particularly in the intelligent means of fault diagnosis of motor equipment, which is rising annually. For the past several years, there has been an increasing demand for accuracy and precision in troubleshooting fault diagnosis of permanent magnet synchronous motors. This paper presents a variable-scale Inception (VS-Inception) permanent magnet motor malfunction diagnostic method on the basis of R parameters. Firstly, the Time Series Generative Adversarial Network is utilized to expand the dataset samples of the collected vibration signals, resulting in many virtual signals with motor fault characteristics. Then, the variable parameter variable scale structure of the VS-Inception model is employed to carry out the fault diagnosis of permanent magnet synchronous motors on the expanded dataset. Finally, comparison experiments of VS-Inception with the existing state-of-the-art methods and with the original method are conducted to confirm the superiority even further and stability of the VS-Inception method in the field of permanent magnet synchronous motor malfunction diagnostic.

Carbon emission reduction potential analysis of fuel cell vehicles in China: Based on GRA-BiLSTM prediction model

The development and promotion of fuel cell vehicles is an important way to reduce carbon emissions in transportation. This study predicts the future ownership of fuel cell vehicles in China based on the GRA-BiLSTM combined model, and it is expected that under the low-growth, benchmark, and high-growth scenarios, China's fuel cell vehicle ownership could reach 549,500, 2,351,700, and 8,844,100 vehicles by 2035, respectively. A life cycle assessment of passenger cars and commercial vehicles with different fuel types has shown that the promotion of fuel cell vehicles can significantly reduce greenhouse gas emissions, particularly for commercial vehicles. By 2035, it is expected that China's road transport sector will reduce greenhouse gas emissions by approximately 23.83 Mt CO2,eq. The results of the study provide data support and a decision-making basis for relevant Chinese enterprises and governments to adjust the layout and promotion policies of the fuel cell vehicle industry in the future.

Forecasting EV Stock Trends Based on ARIMA Model Represented by Tesla and BYD

In recent years, an increasing number of people have turned their attention to renewable energy, and sales of electric vehicles have skyrocketed, making electric vehicles (EVs) a very popular topic. To better understand the development of the electric vehicle market and provide some insights for investors, this study forecasts EV stock trends of two leading companies in the Electric Vehicle industry, Tesla and BYD, by the Autoregressive Integrated Moving Average (ARIMA) model. The data in this research includes a daily time series of stock prices. ARIMA model is chosen in this research for its effectiveness in predicting stationary time series data, for its effectiveness in predicting stationary time series data, particularly suitable given the limited data available in the recently emerged EV industry. This research demonstrates that both Tesla and BYD’s stock closing prices are not stationary data. After differencing, for Tesla, the predicted result suggests a slightly increasing trend, while BYD's prices are predicted to remain at the same level and relatively stable in future 100 days. The study provides insights into the EV stock market, indicating a potential upward yet not pronounced trend in the future. But such a stable price might be a good choice for the investor low-risk takers.

A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems

Now, the present electric vehicle industry is focusing on the fuel cell technology because its features are high flexibility, continuous power supply, less atmospheric pollution, fast startup, and rapid response. However, the fuel cell gives nonlinear power versus current characteristics. Due to this nonlinear behavior, the maximum power extraction from the fuel stack is quite difficult. So, in this work, an adaptive genetic algorithm with an adaptive neuro-fuzzy inference system (ACS with ANFIS) MPPT controller is introduced for finding the MPP of the fuel stack system thereby extracting the peak power from the fuel stack. The proposed hybrid maximum power point tracking (MPPT) controller is compared with the other MPPT controllers which are enhanced incremental conductance-fuzzy logic controller (EIC with FLC), improved hill climb with fuzzy logic controller (IHC with FLC), adaptive beta with FLC, enhanced differential evolutionary with FLC (EDE with FLC), and marine predators optimization with FLC (MPO with FLC). Here, these hybrid controllers’ comprehensive investigations have been carried out in terms of tracking speed of the MPP, oscillations across the MPP, settling time of the converter voltage, maximum power extraction from the fuel stack, and working efficiency of the MPPT controller. The fuel stack generates a very low output voltage which is improved by using the boost DC-DC converter, and the overall fuel stack-fed boost converter system is designed by utilizing the MATLAB/Simulink tool. From the simulation results, the AGA with ANFIS MPPT controller gives high MPP tracking efficiency when compared to the other hybrid controller.

Development Status and Evolution Analysis of Cooperative Innovation Network in The New Energy Vehicle Industry

As a strategic emerging industry, new energy vehicles are very important to the economic development of a country. The new energy vehicle industry has entered a new stage of accelerated development. However, at present, there is still a gap between the driving force of technological innovation in China's new energy vehicle industry and developed countries, and the cooperation and innovation of enterprises in the industrial chain still need to be improved in terms of complementarity and closeness of cooperation. Based on the cooperative patent data of enterprises in the new energy vehicle industry from 2010 to 2020, this paper divides it into three stages, and uses the spatial analysis tool of ArcGIS software to systematically analyze the spatial distribution characteristics of cooperative innovation networks in various provinces. The results show that more and more enterprises are choosing collaborative innovation. The conclusions of this study can put forward relevant policy suggestions for enterprises in China's new energy vehicle industry to achieve high-quality technological innovation.