Electric Vehicles Research Articles

Enhancing Flexibility and Reliability in Smart Distribution Networks: A Self-Healing Approach

This paper presents a pioneering approach that integrates a Self-Healing System through a Smart Distribution Network, which employs a two-step implementation of a comprehensive Energy Management algorithm and a multi-agent system with an autonomous distributed regeneration method. The novel method improves network operation through optimized management of local microgrids, which include diverse components comprising renewable energy sources, electric vehicle charging infrastructure, and energy storage systems. The first phase concentrates on minimizing network operation costs by adhering to system utilization restrictions and optimal load distribution, while the second phase addresses the optimization of regeneration processes, which decreases discrepancies between recoverable priority loads and switching operations. The evaluation introduces a stochastic programming approach to model and manage uncertainties, and utilizes the Kantorovich method and the roulette wheel mechanism to improve reliability. ...

Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning

The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.

Development and Design of an Online Quality Inspection System for Electric Car Seats.

As the market share of electric vehicles continues to rise, consumer demands for comfort within the vehicle interior have also increased. The noise generated by electric seats during operation has become one of the primary sources of in-cabin noise. However, the offline detection methods for electric seat noise severely limit production capacity. To address this issue, this paper presents an online quality inspection system for automotive electric seats, developed using LabVIEW. This system is capable of simultaneously detecting both the noise and electrical functions of electric seats, thereby resolving problems associated with multiple detection processes and low integration levels that affect production efficiency on the assembly line. The system employs NI boards (9250 + 9182) to collect noise data, while communication between LabVIEW and the Programmable Logic Controller (PLC) allows for programmed control of the seat motor to gather motor current. Additionally, a supervisory computer was developed to process the collected data, which includes generating frequency and time-domain graphs, conducting data analysis and evaluation, and performing database queries. By being co-located with the production line, the system features a highly integrated hardware and software design that facilitates the online synchronous detection of noise performance and electrical functions in automotive electric seats, effectively streamlining the detection process and enhancing overall integration. Practical verification results indicate that the system improves the production line cycle time by 34.84%, enabling rapid and accurate identification of non-conforming items in the seat motor, with a detection time of less than 86 s, thereby meeting the quality inspection needs for automotive electric seats.

Comparison of Bi-Directional Topologies for On-Board Charger: A 10.9 kW High-Efficiency High Power Density of DC-DC Stage

In recent years, the trend in power electronics has been toward high-efficiency and high-power-density converters. Additionally, this trend has allowed electric vehicles to accommodate larger batteries, which necessitate bi-directional capabilities not only for driving but also for vehicle to grid (V2G), etc. This article proposes a comparative analysis of GaN-based bi-directional topologies, namely the dual active bridge (DAB) converter and the CLLC converter. To ensure a fair analysis of the proposed topologies, prototypes with the same target of efficiency above 97.5% and a power density of 5.5 kW/L have been constructed. This research can support the adoption of 10.9 kW bi-directional topologies in GaN-based on-board chargers (OBCs) for EVs.

Generation of hydrogen fuel on board vehicles with internal combustion engines

This article discusses advances in the design of hydrogen fuel engines; the main characteristics of the hybrid engine are studied; prospects for further research are outlined; development and improvement of engine designs, research of processes in engines running on natural gas and hydrogen. Transport is one of the key elements of modern civilization. Its condition and development prospects largely depend on the ability to supply transport power plants with fuel. The depletion of liquid hydrocarbon fuel reserves and problems of environmental pollution may present humanity with a choice - either to reduce transport transportation or to find new ways to supply energy to transport. OBJECTIVE: To review the electrochemical technologies used for the production of hydrogen at gas stations and the operation of hybrid electric vehicle engines using fuel cell batteries. Conduct a comparative analysis of the production and use of energy by electrochemical and traditional methods in vehicles. METHODS are based on analysis of literature data and mathematical calculations. For a passenger electric vehicle, the amount of electricity that can be obtained in a fuel cell by processing 1 kg of hydrogen was calculated. It has been shown that the specific fuel consumption for a hydrogen electric vehicle averages 1 kg of hydrogen per 100 km. Hydrogen has the potential to be the sustainable fuel of the future, reducing global dependence on fossil fuel resources and reducing carbon emissions from the transportation industry.

Optimal power generation of proton exchange membrane fuel cell using ANFIS based MPPT algorithm

Fuel cells are the most promising energy source for the future energy demand. The automobile industry is looking at the integration of fuel cells with electric vehicles (EV). This integration comes with many challenges like dynamic operational behaviors. For operating the fuel cell with maximum efficiency, this work proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) method. The hydrogen flow rate, pressure and stack temperature are the parameters considered to track the maximum power point of the fuel cell. The ANFIS-MPPT algorithm has been integrated with the 1.26 kW fuel cell in MATLAB/Simulink® and validated in different scenarios like dynamic variation in hydrogen pressure, stack temperature, load variation. The performance has been observed and compared with the conventional MPPT algorithms of Perturb and Observe (P&O) algorithm and Incremental Conductance (InC) algorithm. The proposed ANFIS-MPPT algorithm improves the power stability by 10–15% than the P&O and InC methods. Also, the proposed ANFIS-MPPT has 30% faster response as compared to the P&O algorithm, and 23% than the InC algorithm. From the analysis, it is observed that the ANFIS, P&O and InC methods are having the response time of 2.5 s, 3.6 s and 4.5 s respectively. Also the ANFIS method delivers the maximum power output of 1.26 kW, whereas the P&O and InC deliver 1.13 kW, 1.19 kW respectively. The detailed simulation analysis and results are presented in this paper.

Tuning Molten-Salt-Mediated Calcination in Promoting Single-Crystal Synthesis of Ni-Rich LiNixMnyCozO2 Cathode Materials

High Ni-content LiNixMnyCozO2 (NMC) cathodes (with x ≥ 0.8, x + y + z = 1) have gained attention recently for their high energy density in electric vehicle (EV) Li-ion batteries. However, Ni-rich cathodes pose challenges in capacity retention due to inherent structural and surface redox instabilities. One promising strategy is to make the Ni-rich NMC material in the form of single-crystal micron-sized particles, as they resist intergranular and surface degradation during cycling. Among various methods to synthesize single-crystal NMC (SC-NMC) particles, molten-salt-assisted calcination offers distinct processing advantages but at present, is not yet optimized or mechanistically clarified to yield the desired control over crystal growth and morphology. In this project, molten-salt-mediated transformation of Ni0.85Mn0.05Co0.15(OH)2 precursor (P-NMC) particles to LiNi0.85Mn0.05Co0.15O2 particles is investigated in terms of the crystal growth mechanism and its electrochemical response. Unlike previous studies that involved large volumes of molten salt, using a smaller volume of molten KCl is found to result in larger primary particles with improved cycling performance achieved via partial reactive dissolution and heterogeneous nucleation growth, suggesting that the ratio of molten salt volume to NMC mass is an important parameter in the synthesis of single-crystal Ni-rich NMC materials.

Electricity Bill Savings from Reduced Household Energy Consumption in Apartment Complexes

Apartments account for 64.6% of all housing units in the Republic of Korea, and most of them receive electricity under a contract, which includes a progressive rate plan. Recently, due to the electrification of energy used in homes and the growing adoption of electric vehicles, electricity consumption in apartment complexes has been gradually increasing. Given the characteristics of the progressive rate system, an increase in electricity usage results in a significant higher rise in electricity bills. Thus, an effective alternative is required to reduce electricity bills for each household. In this paper, the savings in electricity bills achieved by reducing household electricity usage are analyzed from both apartment complex and individual household perspectives, using metering data from 13,332 households. When households are sorted by the amount of savings in descending order, the resulting values are found to follow a negative exponential curve. This indicates that the benefits from reducing electricity usage in households with higher saving are significantly larger compared to other ones. We analyzed bill savings when electricity usage reductions were selectively applied to the top 10%, 20%, and 30% of households with the largest savings. From the results, it is found that the largest savings in electricity bills for households are achieved when usage reductions are applied to the top 10% of households. It is expected that this amount of savings would encourage these households to reduce their electricity consumption. Additionally, it is found that the savings for apartment complexes and the total savings for selected households are not the same, resulting in changes in the bills for households that do not reduce their usage. From the results, it was observed that when the usage reduction of selected households is small or the proportion of households reducing usage is low, the common area charges for non-reducing households tend to increase, leading to higher electricity bills. On the contrary, when the usage reduction of selected households is large or the proportion of households reducing usage is high, the common area charges for non-reducing households tend to decrease, resulting in lower electricity bills.

High-entropy doping for high-voltage LiCoO2 with enhanced electrochemical performances

Lithium cobalt oxide (LiCoO2), as a pioneering layered oxide cathode material for lithium-ion batteries (LIBs), possesses exceptional theoretical specific capacity and cycling stability, positioning it as a leading candidate for commercial LIB applications. Boosting the upper-limit of charge voltage of LiCoO2 is a promising strategy to increase the capacity of batteries, vital for advancing energy storage solutions from portable electronics to electric vehicles. Nevertheless, hybrid redox reactions at high voltages speed up oxygen evolution, electrolyte decomposition, and irreversible phase transitions, ultimately causing rapid capacity fading.To address these challenges, we present a novel approach to enhance the structural stability and electrochemical performances of LiCoO2 cathode via La-Al-Mg-Y high-entropy doping. This strategy significantly improves the high-voltage cycling stability of the cathode material by retaining 72.5 % of its initial capacity after 500 cycles at 5 C under 4.6 V. Furthermore, the structural change caused by the phase transition between the O3 and H1-3 phases is remarkably suppressed. This advancement in LIB cathode technology paves the way for the development of high performance batteries and it is crucial for meeting the growing demand for sustainable energy storage.

Using Statewide Transportation Planning Model to Forecast Demand for Electric Vehicle Charging at Stations Along Intercity Highways

The availability of Charging Stations (CSs) with adequate capacity is critical for the growth in ownership and usage of Electric Vehicles (EVs). In the United States, the state Departments of Transportation (DOTs) are responsible for planning CSs along the Interstate, national, and state highways. While the state DOTs have their Statewide Transportation Planning Models (STPMs), most of these models do not represent EVs internally. The state DOTs can only rely on the existing STPMs to forecast the demand for EV charging at specific CS locations in a state’s highway network. This research proposes a methodology that utilizes an existing STPM, EV and non-EV vehicle registration data, and hourly traffic volumes at strategic counting stations to predict the hourly demand for EV charging (charging port occupancy) at proposed CSs. This approach first estimates the volume of all passenger cars that will pass the vicinity of a CS along intercity highways, and identifies the Traffic Analysis Zones (TAZs) of their trip origins. Based on the EV adoption rates of the TAZs, EV range, and EV owner’s charging behavior, the methodology forecasts the volume of EVs that pass the vicinity of a CS and the number of EVs that will stop at the CS to charge every hour of the day. An example problem demonstrated the application of the methodology in the State of Texas. It not only forecasted the hourly occupancy at CSs along three major corridors, but also demonstrated how to determine the capacities of the CSs. The challenges of using the existing STPM and their solutions are discussed. This proposed methodology enables the state DOTs to forecast the demand for EV charging and the capacities at the proposed CSs along intercity highways, thus allowing the DOTs to apply for federal funds to accelerate the deployment of the CSs. (298 words)

Optimization of PV benefits for Electric Vehicles Charging Station Using a Deterministic Method and Fuzzy Inference System Method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of -863.4 c€/day, whereas the FIS method delivers greater savings of -898.7 c€/day. For fast charging, the deterministic approach yields savings of -524.6 c€/day, while the FIS method results in even higher savings of -540.7 c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20%, compared to 81.60% with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Boosting Electric Vehicle Adoption with PCT Policy: Insights from China's Private Transportation

Personal Carbon Trading (PCT) motivates low-carbon consumption and travel by rewarding sustainable behaviors. This study examines the impact of PCT policies on electric vehicle (EV) adoption in China's private transportation sector. Using a Stackelberg game model involving the government, EV manufacturers, and consumers, it finds that PCT policies can boost EV adoption rates by 2.29% to 14.3%. Each addition of 100,000 new EVs could reduce carbon emissions by 30,000 tons. Effective PCT policies require an independent carbon pricing system and should be implemented where infrastructure and public willingness to pay are high. Both allocation methods are conducive to the promotion of EVs: one facilitates the government in setting regional emission reduction plans, while the other ensures that the government does not incur policy-related expenditures, and different allocation methods should be chosen according to the time and circumstances in different periods of EV market development.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

Dynamic analysis of two-stage steering reducer for electric vehicle based on ROMAX

Abstract. With the rapid development of electric vehicles in the modern market, the efficiency and performance requirements of electric vehicle transmission systems are getting higher and higher. As an important part of the electric vehicle reducer, the steering reducer can make full use of the advantages of the two-stage deceleration transmission, achieve more efficient power transmission, and improve the acceleration performance and driving performance of electric vehicles. This paper aims to design a two-stage steering reducer for an electric vehicle, which adopts planetary gear transmission type. Firstly, the working principle of the electric vehicle steering reducer is analyzed, and the overall design scheme of the reducer is drawn up. Secondly, the transmission system parameters of the reducer are designed and calculated, including transmission ratio distribution, tooth number ratio calculation, etc. Secondly, the transmission structure of the reducer is designed, including input shaft, output shaft, first planetary gear train, second planetary gear train, reducer shell, bearing, etc., and the reducer model is drawn by three-dimensional software Solidworks. Finally, the strength and deformation check analysis of the two-stage reducer is carried out by using the finite element software ROMAX to evaluate the performance of the designed reducer under this working condition. The full text design has a certain reference, and can be applied to other electric vehicle reducer design to provide theoretical guidance.

Deciphering the future of electric vehicles amid emissions and adoption drivers.

Climate change and CO2 emissions are critical challenges for the environment and humanity. There is extensive literature on greenhouse gas (GHG) emissions, in particular CO2 emissions. However,comprehensive analyses focusing on electric vehicles (EVs) and their impact are lacking. This study fills this gap by conducting a bibliometric analysis of 1143 peer-reviewed studies from 1989 to 2023. We aimed to identify influential contributions, understand the field's structure, and reveal research gaps. Analysis included citation networks, research impact, authorship patterns, content, and publication trends. We utilized bibliometric techniques to identify the most dominant countries, institutions, authors, journals, articles, and thematic areas related to EVs and emissions. Additionally, we overviewed publications associated with key search terms. Guided by five research dimensions (EVs, emissions, adoption, policies, and infrastructures), we framed specific research questions. This research provides valuable insights for environmentalists, policymakers, regulators, and academic researchers, facilitating access to crucial data on EVs and emissions.

Electric Vehicle Sentiment Analysis Using Large Language Models

Sentiment analysis is a technique used to understand the public’s opinion towards an event, product, or organization. For example, sentiment analysis can be used to understand positive or negative opinions or attitudes towards electric vehicle (EV) brands. This provides companies with valuable insight into the public’s opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performance compared to traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming significant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimised BERT (RoBERTa), and a generalised autoregressive pre-training method (XLNet) using Lucid Motors and Tesla Motors YouTube datasets. Results evidenced that LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically when fine-tuned. Furthermore, our findings present the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with an F1 score of at least 92%.

Smart Charge-Optimizer: Intelligent Electric Vehicle Charging and Discharging

The important steps toward a low-carbon economy and sustainable energy future is switch to Electric Vehicles(EVs).The rapid development of EVs has brought a risk to reliability of the electrical system.However, the high electricity consumption of EVs will lead to the overload of power grid transformers.Strategies for scheduling charging and discharging that work are essential to reducing the negative grid effects of EVs.In order to reduce the overload of power grid transformers,this paper explores two strategies for intelligent charging and discharging scheduling.The first one is Long Short-Term Memory coupled with Integer Linear Programming(LSTM-ILP)and the second one is Q-learning.The LSTM-ILP aims to minimize the charging and discharging schedules delay.The Q-learning method makes use of reinforcement learning to ascertain the best course of action for EVs in relation to their state-of-charge and the demand on the grid.The outcomes of this research show that both strategies are successful in lowering the peak-to-average ratio of the grid and lessening the influence of EV charging demands.This research aims to Couple Long Short-Term Memory with Integer Linear ProgrammingApplying Q-learning to minimize the peak to-average ratio of grid load through effective peak shaving and valley fillingMinimizing EV charging costs for users while respecting their mobility needs

Multi-objective model for electric vehicle charging station location selection problem for a sustainable transportation infrastructure

The transportation industry mostly depends on conventional vehicles, leading to significant adverse effects on the environment. The widespread usage of electric vehicles can be seen as a relief for this problem. However, the success of electric vehicles largely depends on the availability and proper deployment of charging station infrastructure. It is crucial for cities to strategically select suitable locations for charging stations with adequate capacity levels to promote sustainable and environmentally-friendly transportation options. Hence, in this study, a multi-objective model is proposed for the electric vehicle charging station location selection and capacity allocation problem. The model aims to maximize customer satisfaction, minimize total risk, and minimize costs as key objective functions. To manage the demand effectively, the region of interest is divided into grids. The proposed multi-objective model is applied to the European side of Istanbul and solved by using AUGMECON2 technique. Finally, computational analyses are presented based on scenarios including different demand values. These analyses provide valuable insights into the effectiveness of the proposed model and its implications for achieving sustainable transportation in Istanbul.

Nudging with colors to promote electric vehicle rentals

The symbolic meaning of colors plays a crucial role in individual decision-making. Two experiments were conducted to explore how color influences tourists' rental decisions of electric vehicles. The first study employs a 3 (color hue) × 3 (color lightness) experimental design and analyses 300 responses to reveal that light-green and light-blue vehicles were perceived as most environmentally friendly. The second study conducts a single-factor scenario-based experiment with 600 participants. Light-green vehicles enhance perceived environmental friendliness and encourage rentals, while light-blue vehicles reduce decision-making time by evoking mental imagery. This research is innovative in applying color-based nudges into the context of sustainable self-driving tourism, contributing to nudging theory and color psychology. The findings help tourism businesses make electric vehicles more appealing.

Design and evaluation of a standalone electric vehicles charging station for a university campus in Argentina

The increasing popularity of electric vehicles in recent years has led to a growing demand for charging stations. In this context, universities are an ideal setting for their installation, as they have a large number of students, professors, and staff who could benefit from them and, at the same time, it serves as teaching material to raise awareness in the use of renewable energy. This work presents the design and proposal of an electric vehicle charging station for the campus of the Universidad Nacional de Rafaela (UNRaf). The station will be located in an area of the campus where the construction of more buildings and sport facilities is planned. This area will not be connected to the electrical grid and instead, will have an energy storage system to guarantee supply. The station will have the capacity to simultaneously charge 4 bicycles and 2 light electric vehicles, with an average energy demand of 0.786 kWh per hour. Homer Pro software was used for the calculations. The most economically viable option was a 100% renewable solution powered only by solar energy. It is expected to consist of a 15-kW solar system that will produce 22,922 kWh/year and a bank of 30 batteries of 3 kWh plus a single battery of 1 kWh. The installation of the electric vehicle charging station on the UNRaf campus will contribute to promoting the adoption of sustainable transportation, which will help reduce greenhouse gas emissions without using the public power grid.