Electric Cars Research Articles

The Role of Country Origin and Product Knowledge in Wuling Electric Car Purchase Decision in Indonesia

In today's competitive marketing landscape, understanding factors influencing consumer purchase decisions is crucial. This study examines the impact of country-of-origin image and product knowledge on the purchasing decisions for Wuling electric vehicles in Indonesia. Employing a descriptive quantitative approach with a sample of 104 respondents, the research uses literature reviews and questionnaires to gather data. Multiple linear regression analysis reveals that both country-of-origin image and product knowledge significantly affect purchasing decisions, with positive coefficients indicating their substantial influence. Specifically, a favorable perception of China's technological advancements and product quality, alongside strong product knowledge, plays a vital role in shaping consumer choices. The findings suggest that Wuling should focus on enhancing its country image and leveraging social media to increase brand awareness. Future research could expand to include additional variables and countries for a broader understanding of purchase decision factors

A Solar-Wind based EV Charging Station Model using MATLAB Simulink

Abstract Transportation is becoming more and more necessary every day. One such form of transportation that has been around for a century is the electric vehicle (EV). It has less of an effect on pollution, climate change, and resource depletion because it requires less resources. Many people find it challenging to charge their electric cars in off-grid locations, which lowers the uptake of EVs. In this scenario, a charging station powered by locally available renewable energy sources like solar, wind, biogas, and tidal energy would be a better option. In this paper an attempt has been made to examine a charging station model from renewable energy sources Solar and Wind. The solar-wind energy-based charging system drastically cuts down on the amount of fossil fuels used to generate electricity, which also minimizes emissions of CO2 and carbon-related pollutants. Wind and solar energy are used in conjunction and are complementary to one another. A charging controller which takes into account State of Charge (SOC), the number of linked EVs, and the arrival time of those EVs is used. The power transfer will be managed by this controller. By carefully considering each level, including the solar plant, wind plant, battery storage system, and controlling technology, the charging station is modeled in the best possible way. Simple model is used to describe each stage of the charging station, and designed with appropriate mathematical formulae. By understanding the demand for charging and pairing it with the appropriate energy storage system and auxiliary devices, this charging model can be used in off-grid locations.

Consumer Adoption and Perception of Electric Vehicles

The ever-increasing need for environmentally friendly modes of transportation is the primary force behind the tremendous transition that the global automobile industry is currently undergoing. Electric cars, often known as EVs, have emerged as a potentially useful alternative to conventional automobiles powered by internal combustion engines. These vehicles offer a positive impact on the environment as well as technical developments. For policymakers, industry stakeholders, and researchers who are interested in accelerating the transition to a low-carbon transportation system, it is crucial to have a solid understanding of how consumers embrace electric vehicles and how they perceive them. A detailed study of consumer acceptance and perception of electric vehicles (EVs) is presented in this research article. The analysis is based on a review of relevant literature, empirical investigations, and case examples. Several key aspects that influence customer decision-making regarding the adoption of electric vehicles are investigated. These elements include environmental consciousness, government incentives, technical improvements, cost of ownership, availability of charging infrastructure, driving experience, and perception of the brand (brand perception).

Analytical study on highway thermal runaway propagation and inter-electrode dynamics in lithium-ion battery applications: Insights into battery safety

It is essential to ensure thermal safety in lithium-ion (Li-ion) batteries to facilitate their increased use in electric cars, thereby enhancing the safety of individual batteries. When high-power Li-ion batteries are subjected to abusive conditions, they frequently experience thermal runaway (TR) due to a sequence of exothermic reactions resulting from electrode contacts. These events generate a significant amount of heat and have the potential to cause thermal runaway propagation (TRP) throughout the battery module. This paper presents an in-depth TRP analysis approach, combining thermo-kinetic and electrode interaction assessments at the cell level. The method is implemented on the TR properties of a cylindrical Li-ion cell equipped with nickel-rich and silicon-carbon electrodes. The developed TRP approach, incorporating various heat dissipation techniques, is applied to a prototype high-energy Li-ion chamber to evaluate TRP susceptibility under different conditions, including ambient temperature, cell spacing, initiating cell location, external heating power, and heat dissipation rates. Additionally, this research provides insights into the various TRP pathways, focusing on aspects such as the propagation rate, the exothermic processes’ thermal, required thermal energy input, and external heat release. The comprehensive model, considering both heat transfer and chemical interactions between electrodes under TR, can address accurate TR- propagation examinations for batteries. Moreover, it can be expanded to larger battery systems, ultimately improving overall battery safety.

Solar-powered electric car: validity and effectivity of prop in energy conversion learning

Solar-powered electric car: validity and effectivity of prop in energy conversion learning

Optimizing public space design through odd-even policy: Reducing traffic congestion and pollution in DKI Jakarta

Background: The Provincial Government of DKI Jakarta issued Governor Regulation No. 155 of 2018 and its amendments regarding Traffic Restrictions with the Odd-Even System. This regulation aims to reduce congestion levels on 16 main roads. By decreasing congestion, the expected outcome is a reduction in air pollution levels in DKI Jakarta. However, there is concern that the implementation of Governor Regulation No. 155 of 2018 might lead to an increase in conventional car purchases, as affluent individuals may buy two cars with odd and even license plates. This choice is driven by the continued use of fossil fuel-based energy. This study aims to evaluate the effectiveness of Governor Regulation No. 155 of 2018 in reducing traffic congestion and air pollution in DKI Jakarta. Additionally, the study seeks to understand the relationship between the implementation of the odd-even traffic restriction system and electric vehicle sales. Methods: The research was conducted by analyzing secondary data obtained from government agencies and other institutions. Furthermore, a questionnaire survey was administered to individuals active on the 16 main roads where the odd-even policy is enforced. The study also analyzed the purchase levels of electric cars, which is one method for Jakarta residents to avoid fines on odd-even roads. Result: Based on the research findings, Governor Regulation No. 155 of 2018 and its amendments have not yet effectively reduced traffic congestion and air pollution in DKI Jakarta. Conclusion: The increase in electric car purchases potentially introduces new issues, such as the unsustainable management of nickel mines required for electric vehicle battery production. Therefore, other sustainable strategies are needed to address traffic congestion and air pollution. Novelty/Originality of the study: This study shows that Governor Regulation No. 155 of 2018 has yet to reduce congestion and air pollution in DKI Jakarta effectively. In addition, the increase in the purchase of electric cars as a solution to avoid fines has raised new problems related to the environmental impact of electric vehicle battery production.

Design of Radial Flux PM Synchronous Motor for EV Applications

Abstract: High-performance electric propulsion systems are becoming increasingly important as the automotive industry rapidly shifts to electric cars (EVs). The electric motor is an essential part of these systems as it determines the power, efficiency, and overall performance of the vehicle.This project uses Altair Flux, a full-featured finite element analysis (FEA) software suite, to build and optimize a radial flux motor (RFM) with permanent magnet synchronous motor (pmsm) . The main goal is to increase the power density and efficiency of the electric propulsion system for electric cars by utilizing Altair Flux's simulation and analytical capabilities.

Innovative Solutions, Green Initiatives for Sustainable Economic - The Hungarian Example

Green development has become a major goal for most world powers and is taking an increasingly prominent place in the global economy, with China and Europe being no exception. Both have set green growth as a goal to achieve carbon neutrality in the coming years. As the green economy is now global, cooperation is essential to improve quality of life everywhere. Chinese experts are helping to green Hungary's transition taking part in the Hungarian energy economy. The relationship between the two countries has been dominant for centuries, but until recently it needed to be redefined. The Chinese President in 2024 visited Hungary and met the Prime Minister and the Hungarian Head of State. Within the framework of the One Belt, One Road strategy, the two countries have drawn up an expanding list of projects for joint investment and development. The summit between the two countries delivered results, with the parties signing eighteen important agreements. Hungarian experts collaborate with Chinese partners on clean energy, sustainable energy production and green technology innovation. In a summary, the agreement covers cooperation in nuclear research and development, training, the expansion of the electric car charging network, and the exploration of the possibility of jointly connecting the domestic and Serbian oil pipeline systems. In our research, we explore areas for further cooperation between China and Hungary, identifying key areas of mutual benefit and common interest.

Study on enhanced real time applications of compound semiconductor (SiC and GaN) power devices with AI and IoT Technologies

Compound semiconductors, composed of two or more elements, differ from single-element semiconductors like silicon. These materials are crucial because they have a direct band gap, unlike elemental semiconductors such as silicon and germanium, making them ideal for optoelectronic applications like LEDs, semiconductor lasers, and photo detectors. Robots rely on sophisticated sensors to collect vital data for their operation, including internal data on temperature, moisture, movement, and position, as well as external data from images, infrared light, and sound, processed through semiconductor units. Compound semiconductors are integral to numerous technologies around us, including electric cars, solar panels, satellites, spacecraft, and smart phones. Future innovations like driverless cars and artificial intelligence will also heavily depend on these materials.

IoT Integrated Electric Car Energy Monitoring System: Case Study of IMEI TEAM UMSIDA

The increasingly rapid development of technology accompanied by the ever-increasing consumption of fossil fuels has a negative impact on environmental sustainability and provides challenges related to innovation regarding these problems. Electric fuel vehicles are one of the relevant innovations to reduce air pollution and minimize exhaust emissions from fossil fuel vehicles on global warming. IMEI TEAM UMSIDA continues to contribute to the development of vehicles powered by electric energy by participating in energy-saving vehicle competitions such as KMHE and Shell Eco Marathon. In developing electric cars, the IMEI TEAM UMSIDA experienced problems related to obtaining information on the car's energy consumption in real time or when the car was on the track. This problem prompted researchers to create a tool that is capable of monitoring car energy consumption in real time and can be accessed remotely. The experimental method is the right method for this research, because with this method the researcher carries out many experiments to ensure that the tool system works well. The parameters produced in this research are voltage, current, power and energy values in units of time. This tool is equipped with an IoT-based communication system with an internet network as a link between the user, hardware system and software system which functions to carry out remote monitoring in real time. The test results show that this tool functions well and is able to carry out voltage measurements with an error percentage of less than 0.1% and current measurements with an error percentage of less than 0.2%.

Climate change concern as driver of sustainable mobility and reduced car use

Climate change concern has been linked to climate-friendly behavior, but little is known on whether and how it may promote sustainable mobility. This paper investigates how worry about climate change relates to attitudes and intentions towards the car as well as the actual use of conventional and electric cars in Oslo and Viken in Norway. Findings indicate a relationship between worry about climate change and reduced car use, mediated by mode preferences and willingness to live car-free. Those concerned about climate change were less likely to commute by a conventional car and used conventional cars less both on weekdays and weekends. Worry about climate change was also related to living in a denser neighborhood, preference for travel modes other than the car, and willingness to live car-free. Raising greater awareness of climate change could contribute to climate change mitigation by promoting car-reduced or car-free lifestyles and sustainable mobility.

Charging Station Power System Data Prediction Model Based on Deep Learning

The increase growth and adoption of electric vehicles (EVs) are playing a crucial role in advanced transportation system, helping to minimizing the emissions of harmful greenhouse gases and enhancing environmental sustainability. The need for EVs to be charged immediately has gained significant attention due to the rise in EVs sales over the last years. As a result of this, need for electric car charging is important to reducing the impact of electric network and providing minimum charging fares. In order to calculate the demand for charging EVs, a novel Deep Learning (DL)-based Long-Short Term Memory (LSTM) recurrent neural network predictor model is attempted to be developed in this research study. The Modified Aquilla Optimizer Algorithm (MAOA) is used to optimizing the parameter of the new Deep LSTM (DLSTM) neural predictor models and Independent Component Analysis (ICA) is utilized to solve the input time series data while conserving its properties. In this research, a novel ICA—AOA—DLSTM neural predictor model was developed to addressed the challenges of vanishing and exploding gradient in basic recurrent neural learnings. The predictor model was tested on the EV charging dataset from Georgia Tech, Atlanta, USA, indicating its performance. During simulation, the predictor achieved a prediction accuracies of 96.24% with a mean absolute errors of 0.1083 and a RMSE errors of 3.0629 × 10^-5, outperforming previous techniques. Additionally, the mean absolute error was found to be 0.2083, with a mean square error of 3.25516 × 10^-10. These results highlight the effectiveness of the novel deep learning LSTM neural predictor for this dataset compared to existing techniques.

A Financial Analysis and Valuation of Tesla

The pressing issue of global warming, fueled by excessive carbon dioxide emissions, has spurred global concern. Researchers highlight the potential of new energy vehicles, particularly electric cars, in significantly mitigating greenhouse gas emissions. As demand for electric vehicles rises, attention turns to companies like Tesla, spearheading this transformative industry. This study delves into Tesla's financial landscape in 2023, juxtaposing it against other electric vehicle manufacturers, aiming to unveil Tesla's comparative advantages. Leveraging data sourced from Yahoo Finance, the study employs a comprehensive financial analysis, focusing on liquidity, solvency, profitability, and investment metrics. Results underscore Tesla's remarkable performance across these dimensions, showcasing its impressive profitability, operational prowess, financial stability, and investment appeal relative to its competitors. This report stands poised to guide investors intrigued by Tesla's potential, offering valuable insights into the company's financial health and positioning within the burgeoning electric vehicle sector. Through such analysis, stakeholders can make informed decisions in navigating the dynamic landscape of sustainable transportation investments.

How environmentally friendly are electric cars? The relationship between lithium usage and water resources, Chile example

AbstractThe extraction of minerals such as lithium, widely used in the battery technologies of electric vehicles, is a process that necessitates consideration of its environmental impacts. This study aims to examine the environmental impacts of electric vehicles and the effects of lithium mining on water resources, particularly in regions abundant in lithium reserves like Chile. The analyses indicate a long‐term relationship between lithium mining and irrigation water sources. This study establishes a foundation for understanding the relationship between the environmental sustainability of electric vehicles and the environmental impacts of lithium mining.

Modeling electric Vehicle's and improving battery lifetime using AI tools case study: Postal cars

The rapid development in the field of electric vehicles requires a careful evaluation of the design process. The presence of a simulation model of the electric vehicle can effectively detect many faulty areas during the development process without risks. The MATLAB and Simulink environment is considered one of the most important tools used in the simulation process. In this paper, we will present the model of electric car used for transporting postal parcels (postal cars). The model includes simulating the operation of a permanent magnet synchronous electric motor. We will assume that the car is moving according to a driving cycle. The results will show the torque forces required to achieve the required speed. We will further calculate the traction and the resistance forces during the driving cycle and the engine efficiency in addition. Perhaps the most important problem facing electric car designers is calculating the amount of energy consumed from the battery or hydrogen fuel, and this is what was achieved as the result of the simulation process in this research. In the end, use one of the artificial intelligence tools (fuzzy controller) to improve battery life by providing the electric car driver with an alert system that will increase the ability to monitor the battery condition and thus increase battery life. The benefit of this paper emerges in realizing the importance of modeling and using simulation using artificial intelligence in developing the design of the electric car, specially the electric motor and battery size, and thus achieving one of the most important goals of the United Nations of preserving the environment and reducing carbon emissions.

A Study on an Energy-Regenerative Braking Model Using Supercapacitors and DC Motors

This study presents an energy regeneration model and some theory required to construct a regeneration braking system. Due to the effects of carbon dioxide (CO2) emissions, there is increasing interest in the use of electric vehicles (EVs), electric bikes, electric bicycles, electric buses and electric aircraft globally. In order to promote the use of electric transportation systems, there is a need to underscore the impact of net zero emissions. The development of EVs requires regenerating braking system. This study presents the advantages of regenerative braking. This system is globally seen in applications such as electric cars, trams, and trains. In this study, the design specification, design methodology, testing configurations, Simulink model, and recommendations will be outlined. A unique element of this work is the practical experiment that was carried out using 1.5 Amps with no load and 2.15 Amps with a load. The discharge voltage was purely from the 22 W bulb load connected to the capacitor bank as we limited this study to the use of 1.5 Amps and it took 15 min for a full discharge cycle, after which no charge was left in the capacitor bank. The results showed that the discharge rate and charging rate for the regenerative braking system were effective but could be improved. The objective of this paper is to investigate how a supercapacitor works alongside a battery in regenerative braking applications. This study demonstrates that the superconductor used can deliver maximum power when required. Also, it can also withstand elevated peaks in charging or discharging current via the supercapacitor. Combining a battery with a supercapacitor reduces the abrupt load on the battery by shifting it to the capacitor. When these two combinations are used in tandem, the battery pack’s endurance and lifespan are both boosted.

Thermal analysis of batteries and prediction with artificial neural networks

PurposeIn this study, it is aimed to develop cooling models for the efficient use of batteries and to show how important the busbar material is. Batteries, which are indispensable energy sources of electric aircraft, automobiles and portable devices, may eventually run out. Battery life decreases over time; the most critical factor is temperature. The temperature of batteries should not exceed the safe operating temperature of 313 K and it is recommended to have a balanced temperature distribution through the battery.Design/methodology/approachIn this study, the effect on the battery temperature caused by using different busbar materials to connect batteries together was investigated. Gold, copper and titanium were chosen as the different busbar material. The Air velocities used were 1 m/s and 2 m/s, the air inlet temperatures were 295 and 300 K and the discharge rates 1.0–1.5–2.0–2.5C were chosen for cooling the batteries.FindingsThe best busbar material was identified as copper. Because these studies are long-term studies, it is also suggested to estimate the data obtained with ANN (Artificial Neural Networks). The purpose of ANN is to enable the solution of many different complex problems by creating systems that do not require human intelligence. Four different program (BR-LM-CGP-SCG) were used to estimate the data obtained with ANN. It was found that the most reliable algorithm was BR18. The R2 size of the BR18 algorithm in the test phase was 0.999552, the CoV size was 0.007697 and the RMSE size was 0.005076.Originality/valueWhen the literature is considered, the cooling part of the battery modules has been taken into consideration during the temperature observation of the battery modules, but busbar materials connecting the batteries have always been ignored. In this study, various busbar materials were used and it was noticed how the temperature of the battery model changed under the same working conditions. These studies are very time-consuming and costly studies. Therefore, an estimation of the data obtained with artificial neural networks (ANN) was also evaluated.

Optimizing Electric Racing Car Performance through Telemetry-Integrated Battery Charging: A Response Surface Analysis Approach

Optimizing Electric Racing Car Performance through Telemetry-Integrated Battery Charging: A Response Surface Analysis Approach

Simulating Traffic Networks

For driving the roads of cities into enjoyable and relaxing places with parks, trees, and seating, a paradigm change in everyone’s commuter behavior is needed. Still, individual transport via cars increases, and thus, the space required for parking and driving these cars shapes our cities — not the people. Next to the space needed, vehicles pollute the environment with CO2, diesel particulate, and even electric cars with tire abrasion. Alternative modes of locomotion, like public transportation and shared mobility, are still not attractive to many people. Intelligent intermodal mobility networks can help address these challenges, allowing for efficient use between various transportation modalities. These mobility networks require good databases and simulation combined into digital twins. This paper presents how such a digital twin can be created in the Simulation of Urban Mobility (SUMO) software using data from available and future city sensors. The digital twin aims to simulate, analyze, and evaluate the different behaviors and interactions between traffic participants when changing commuting incentives. Using the city of Osnabrück and its different available sensor types, the data availability is compared with other towns to discuss how the data density can be improved. Creating a static network from open street data and intersection side maps provided by the city of Osnabrück shows how these data can be integrated into SUMO for generating traffic flows and routes in SUMO based on a database of historical and live data. Within the conclusion, the paper discusses how developing a digital twin in SUMO from static and dynamic data can be improved in the future and what common misconceptions need to be overcome.

Big Data Technology Usage in Electric Transportation Industry

In the context of critical challenges related to global warming and the necessity of reducing carbon footprint, the electric car sector is experiencing significant growth. This progress inevitably leads to the need for expansion and modernization of the charging station infrastructure. This article conducts a detailed analysis of how big data processing technologies can contribute to the optimization of this infrastructure’s use, the efficiency of charging stations, and the development of personalized services for electric vehicle users. Strategies for solving current problems, particularly in the areas of data security and standardization, are discussed, along with the impact of big data on the formation of new commercial models in the electric transport sector. Special attention is given to the analysis of existing scientific works and publications, which reveal a noticeable deficit of research focused on adapting big data technologies to specific regional conditions and analyzing the behavioral models of electric vehicle consumers. The article identifies key directions for future research aimed at exploring the potential of big data for the intelligent optimization of electric vehicle transport, particularly in areas such as demand forecasting, effective management of charging stations, development of new user services, and integration with broader urban transport management systems. Additionally, the article highlights challenges related to ensuring the confidentiality and security of collected data, the need to integrate diverse data and systems, and the current demand for qualified professionals in the field of big data analysis. The final section of the article focuses on the prospects of using big data in electric transport, their potential contribution to the development of smart city concepts, infrastructure improvement, and enhancing the quality of services for end consumers. Recommendations are provided for key stakeholders in the industry, with the aim of facilitating the adoption of strategic decisions that would take into account future opportunities and challenges. An analytical review of current literature sources and online publications is included, emphasizing the innovative nature of the research conducted.