Search

To improve the ergonomic comfort of electric vehicle charging ports, this paper analyzed the height, angle, position and other layout factors of the charging ports from the perspectives of ergonomics considering human body size, comfortable range of human activities, charging pile and charging gun parameters, and proposed a charging port layout scheme, which was verified and evaluated. In addition, the paper offered design proposals for charging operational visibility and operation space, which served as reference for charging ports of electric vehicle. [ABSTRACT FROM AUTHOR]

Accurate accident reconstruction requires the knowledge of the mass properties of vehicles, namely the centre of gravity location, the mass and the inertia tensor. Such data are seldom available, especially in case of newly produced electric vehicles. In this paper, vehicle inertia measurements, performed at Politecnico di Milano, refer to a number of electric vehicles. In addition to the "simple" measurement of vehicle inertia, measured mass properties are analysed to derive the proper empirical formulae for the estimation of the centre of gravity height and the moments of inertia. Both internal combustion and electric vehicles are considered. Data show a significant difference in the mass properties of the two types of vehicles. The proposed formulae can be effectively employed to quickly obtain a reasonable estimation of the mass properties of any vehicle. The results show that electric vehicles are characterised by higher values of mass with respect to internal combustion vehicles, but they present a lower centre of gravity location and proportionally lower values of the moments of inertia. [ABSTRACT FROM AUTHOR]

This paper addresses the control task of a wireless power transfer (WPT) charger designed for electric vehicles (EVs). The challenge is to maintain a constant battery charging current when the WPT is controlled on the ground side. Indeed, the intermittent latency involved in the wireless data communication between the ground and vehicle sides leads to system instability. To overcome this issue, a new control approach has been proposed in this paper. The proposed technique ensures indirect control of the battery charging current through control of the current on the ground side. The control technique relies on an adaptive hill-climbing algorithm in conjunction with a PI-based controller. The adaptive parameter is adjusted online, during the operation of the charger, only when a new measure of the battery charging current is received on the primary side. This makes it possible to avoid the need for real-time wireless data communication. It should be noted that this aspect is crucial in ensuring the controller's robustness and stability of the system regardless of potential delays in wireless communication and large misalignments between the coils. The validity of the proposed control technique has been confirmed through simulation. In addition, experimental validation, using a laboratory test bed, demonstrated satisfactory results. [ABSTRACT FROM AUTHOR]

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user's multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the World Electric Vehicle Journal titled "Emerging Technologies in the Electrification of Urban Mobility." The special issue focuses on the challenges and innovations in the electrification of transportation systems in urban areas. The nine papers included in the special issue cover topics such as electric vehicle energy management optimization, strategies for new energy vehicles, and intelligent transportation systems. The papers provide insights into the impact of charging strategies on the electricity grid, data security in electric vehicle networks, optimization of battery swapping station locations, and improvement of driving range at low temperatures. The special issue aims to contribute to sustainable transportation and provide policy references for urban transportation planning and management. [Extracted from the article]

The couple between the power network and the transportation network (TN) is deepening gradually with the increasing penetration rate of electric vehicles (EV), which also poses a great challenge to the traditional voltage control scheme. In this paper, we propose a coordinated voltage control strategy for the active distribution networks considering multiple types of EV. In the first stage, the action of on-load tap changer and capacitor banks, etc., are determined by optimal power f low calculation, and the node electricity price is also determined based on dynamic time-of-use tariff mechanism. In the second stage, multiple operating scenarios of multiple types of EVs such as cabs, private cars and buses are considered, and the scheduling results of each EV are solved by building an optimization model based on constraints such as queuing theory, Floyd-Warshall algorithm and traffic flow information. In the third stage, the output power of photovoltaic and energy storage systems is fine-tuned in the normal control mode. The charging power of EVs is also regulated in the emergency control mode to reduce the voltage deviation, and the amount of regulation is calculated based on the fair voltage control mode of EVs. Finally, we test the modified IEEE 33-bus distribution system coupled with the 24-bus Beijing TN. The simulation results show that the proposed scheme can mitigate voltage violations well. [ABSTRACT FROM AUTHOR]

Solar power is mostly influenced by solar irradiation, weather conditions, solar array mismatches and partial shading conditions. Therefore, before installing solar arrays, it is necessary to simulate and determine the possible power generated. Maximum power point tracking is needed in order to make sure that, at any time, the maximum power will be extracted from the photovoltaic system. However, maximum power point tracking is not a suitable solution for mismatches and partial shading conditions. To overcome the drawbacks of maximum power point tracking due to mismatches and shadows, distributed maximum power point tracking is utilized in this paper. The solar farm can be distributed in different ways, including one DC–DC converter per group of modules or per module. In this paper, distributed maximum power point tracking per module is implemented, which has the highest efficiency. This technology is applied to electric vehicles (EVs) that can be charged with a Level 3 charging station in <1 hour. However, the problem is that charging an EV in <1 hour puts a lot of stress on the power grid, and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate. Therefore, a Level 3 (fast DC) EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue. Finally, the simulation result is reported using MATLAB®, LTSPICE and the System Advisor Model. Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day, which is enough to fully charge ~120 EVs each day. Additionally, the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants. For example, instead of supplying EVs with power from coal-fired power plants, 1989 pounds of CO2 will be eliminated from the air per hour. [ABSTRACT FROM AUTHOR]

The power grid and transportation network are coupled by the charging behavior of electric vehicles. Based on the coupled power-transportation network model, this paper first analyzes the effect of the distribution system operator's (DSO) electricity selling price on guiding the charging behavior of electric vehicles in the transportation network and then builds the DSO's optimal pricing formulation. Considering the competition between multiple charging network operators (CNOs), this paper establishes a game model between CNOs and solves it iteratively through the best response dynamic method. An approximation method using the elasticity matrix is proposed to speed up the solution by reducing the multi-layer optimization to a single layer one in each iteration, with its effectiveness validated through numerical tests. Furthermore, the paper discusses the issue of the prisoner's dilemma that arises among CNOs and explores the potential impact of their cooperative strategies on the overall system. [ABSTRACT FROM AUTHOR]

In this paper, we conduct a comparative examination of offline (cold) wallets that are designed for storing cryptocurrency. Our analysis focuses on security, usability, and accessibility, with specific attention given to popular solutions like Ledger and Trezor. We evaluate these wallets based on how practical they are to use without compromising security and efficiency. Furthermore, we explore the integration of Near Field Communication (NFC) technology into offline wallets. This approach aims to make cryptocurrency transactions more secure, convenient, and user-friendly, especially in Electric Vehicle (EV) charging stations. By envisioning a future where transportation infrastructure and cryptocurrency security intersect, our research seeks to advance technologies that balance security with the demands of modern payment systems. Through this effort, we aim to provide valuable insights to stakeholders in the cryptocurrency ecosystem, informing strategic decisions and driving the development of secure, user-centric financial technologies. By shortening the gap between theoretical security concepts and real-world operational needs, our work aims to help other researchers and stakeholders create a future where blockchain-based currencies can be just as practical as efficient. [ABSTRACT FROM AUTHOR]

Efficiency of the battery pack largely depends on the resistive losses and heat generation between the interconnections of the battery cells. Grouping of battery cells usually is done in different ways in industries. However, losses vary depending on applications or states of electric vehicle (EV). Therefore, it is necessary to determine the efficiency and heat generation in battery cells as well as battery packs. In practical situations, some battery cells are charged rapidly in comparison to other battery cells. On the other hand, when an EV is in running condition some battery cells are discharged rapidly. As a results battery pack cannot provide better efficiency and its life span is reduced. As an alternative option the inter-cell connection of battery package is needed to reconfigure in an optimized way. In this paper firstly, a battery pack with switches is modeled and then efficiency and temperature variation with respect to time are determined. Then, an experimental setup is investigated to measure the efficiency and temperature rise with respect to time. Results, explained in the paper, demonstrate that battery pack with switches increases the efficiency if it is measured after switching (97–98 %), while temperature increases from 25 °C to 50 °C for different C-rates. [ABSTRACT FROM AUTHOR]

The population growing faster than before, and availability of transportation options is increasing. Automobiles require combustion engines, which require fuel obtained from underground storage. This underground fuel storage is limited and depleting day-by-day. Many nations have set deadlines up to 2040 to stop producing automobiles that run on underground fuels. Researchers have concentrated on alternative modes of fuel for transportation. The world's largest Sedan marketplaces will transition to all-electric vehicles by 2035, providing a glimpse of greener future other than a significant financial prospect. Not only Sedan, the entire world is focussing on only green electric vehicles to maintain sustainability. However, electric vehicle charging stations are operated by using many conventional resources. Therefore, this paper aims to show how self-charging electric vehicles can help to reduce emissions caused by the direct use of conventional resources in charging stations along with the up-to-date status quo of the EV market. The key descriptions of electric vehicles on top of the battery's type which is randomly used in EVs, how the batteries are proficient in preserving and supplying power continuity itself in vehicles are talked about. Finally, the paper is consulting about charging-discharging system of electric vehicles to make the environment cleaner. [ABSTRACT FROM AUTHOR]

With the huge influx of E-vehicles in the market, there is a significant impetus to the pro-environmental issues discussion regarding e-vehicles. This paper deals with the concerns related to consumer behaviour towards e-vehicles. This paper addresses the facets of the process of e-vehicle manufacturing and the issues concerned with raw materials and their usage. This research the initial parts discusses about how electric vehicles are superior as compared to internal combustion vehicles (ICV). In this letter, half the paper deals with issues like prices of electric vehicles, maintenance cost issues, incentives from the government, life span issues, increasing cost per millage, ease of driving and other factors that concern the potential consumer towards the embarrassing the e-vehicles. This paper contributes to the body of literature by presenting a model, which has variables taken from the existing literature related to consumer behaviour words the e-vehicles This paper unveils the specific factors that drive consumers towards embracing e-vehicles as against conventional vehicles, which is an unbridged issue in this area of research. This paper also presents the major challenges that can influence consumer behaviour towards evehicles, finally, the paper tries to cross the future research agenda for further investigation by future investigators. [ABSTRACT FROM AUTHOR]

With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers' purchase intentions. According to the data of customers' vehicle experience evaluation and personal information, this paper develops the analysis models of influencing factors using the analysis of variance algorithm (ANOVA) and Kruskal–Wallis algorithm. Then, the purchase intention model for EVs is proposed using the random forest method. Finally, the optimization model for the EV sales plan was built. The results show that the main factors influencing customers' purchases are different for different vehicle brands. However, the customer's evaluation of the vehicle experience has a greater influence on the customer's purchase. Compared to other prediction models, the random forest model has the highest accuracy. For 3 EV brands, the prediction accuracies are 97.8%, 98.9%, and 97.6%. In addition, this paper predicts the purchase intentions of 15 customers. By optimizing the sales plans for 3 EV brands, the predicted purchase rate of 15 customers increased from 40% to 53%. The research work contributes to the sales of electric vehicles, the accurate positioning of customers, and the identification of more potential customers. [ABSTRACT FROM AUTHOR]

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles. [ABSTRACT FROM AUTHOR]

This article addresses the braking controls for an electric vehicle with DC motors such that the voltage in the motors is used for controlling the wheel angular velocity. Other papers on the anti-lock braking system (ABS) handled how to derive the braking torque (or braking pressure) for controlling the wheel angular velocity. However, heavy or prolonged braking can cause brake fade or wear. According to EURO 7 regulations, brake fade or wear is not desirable, since the regulations refer to the reduction in particles emitted from brake pads. For avoiding heavy or prolonged braking, this paper does not use a brake unit, such as electro-mechanical brake units or hydraulic brake units, for vehicle stop. Instead, the motor voltage is used for controlling the wheel angular velocity. While a vehicle moves, the goal of this paper is to provide automatic braking controls in real time, so that the vehicle stops safely and smoothly without slippage before colliding with an obstacle. In practice, road conditions can change depending on weather conditions, such as rain or snow. Moreover, road slope can have an effect on the braking distance for the vehicle. Thus, this article introduces automatic braking controls, while considering both road slope and road conditions. This article is unique in presenting automatic braking controls for the smooth stop of electric vehicles with DC motors, while considering both road slope and road conditions. In addition, this article is unique in controlling the motor voltage for controlling the wheel angular velocity, while not requiring any brake units. [ABSTRACT FROM AUTHOR]

In order to eliminate the potential safety hazard that arises when metal foreign objects intervene in the wireless charging area of electric vehicles, this paper proposes that a metal foreign object detection method be applied to the wireless charging system of electric vehicles based on the optimal design of the array detection coil. Firstly, the equivalent circuit model of the metal foreign object detection system is established, then the principle of the foreign object detection system is analyzed, and the scale factor β is introduced as the optimization index of the detection coil. Secondly, the change of the scale factor β with the circuit parameters is analyzed and the appropriate circuit parameters are compared and selected. Thirdly, on the basis of the planar square spiral coil, Ansys Maxwell finite element simulation software is used to optimize its structural parameters, combination mode, and resonant circuit, as well as design the anti-series and anti-parallel enhanced detection coil sets with the decoupling and elimination of detection blind spots. Finally, the feasibility of the proposed detection method of metal foreign objects is verified by experiments. The results show that the two array detection coil sets can detect small-sized common metal foreign objects such as paper clips and the proposed double-layer reinforced structure can significantly improve the detection sensitivity of the system. [ABSTRACT FROM AUTHOR]

The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper's model can save the distribution cost of electric vehicles. [ABSTRACT FROM AUTHOR]

Many studies have incorporated particular models with various methods and algorithms to resolve the site selection problem for electric vehicle charging stations (EVCS). This paper systematically reviews research that evaluates geographic information systems (GIS) based EVCS location techniques and the variables used for decision making. We classify and characterize those techniques and variables to determine important linkages within the literature. A variety of databases were referenced to extract research published from 2010 to March 2023 pertinent to this specific location optimization problem, and 74 papers were selected after thorough evaluation. The models used in each paper were examined along with the methods for selecting variables and ranking alternate locations. Site selection for EVCS requires a multi-criteria decision making approach to meet the sustainability, efficiency, and performance goals of communities adopting electric vehicle mobility. Our results indicate that map algebra and data overlay methods have been used more frequently with GIS-based analysis than other techniques, while geographic and demographic variables are commonly the most significant site selection characteristics. The reviewed methods have most often been applied to urban locations; however, the transfer of these techniques to a rural EVCS site selection problem has been rarely explored in the current literature. This research assessment contributes relevant guidance for the application of methodologies useful in policymaking and provides recommendations for future research based on these findings. [ABSTRACT FROM AUTHOR]

The article presents abstracts on topics related to electric vehicles including the wide use of electric vehicles, the impact of electric vehicles in the world, and the use of electric vehicles as a means of transportation.

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey--Clarke--Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application. [ABSTRACT FROM AUTHOR]

Background and Objectives: Today, replacing gasoline-powered vehicles with electric vehicles (EVs) and connecting them to an electric power source have made the optimal usage of energy-saving resources. Therefore, wireless Power Transfer (WPT) outstands as an alternative technology to improve the user perception about the charging process of the EVs. Superconducting coils (SCs) with high-temperature have an applied feature in decreasing losses of wireless power transmission (WPT). The Magnetic force has effects of overall deformation modes between two current carrier superconducting coils, i.e. axial extension, torsion, and bending. Coil misalignment is a fundamental problem and its impact on wireless power transmission efficiency is very complex. The analysis of a magnetic force which is presented in this paper are beneficially for the design and application of the WPT systems. Here, a fast analytical solution is presented to obtain the magnetic force between the transmitter and receiver helical superconducting coils in different positions. Methods: In this paper, a new method applied to solve the numerically magnetic force solutions in different superconducting coils mismatch states for WPT. Finally, for improvement of efficiency, the WPT system has been designed on the basis of mutual inductance changes which receiving helical coil was moved inside the transmitting helical coil. Hence, the magnetic force calculation of movable YBCO superconducting helical coils inside each other is presented. These models have been compared with the FEM. Results: Results show that the presented equations are reliable as well. According to the comparing the analysis and FEM data, the obtained results indicated the errors with less than 0.0064%. Also, results show an excellent agreement with respect to the finite element method. Conclusion: In this paper, the numerical solutions of magnetic force in different superconducting coils mismatch states were solved by a new method. The magnetic force analysis basics introduced in this paper are useful to develop and apply for wireless power transmission system. The simulation results show that only by applying some constraints, the efficiency of the transmitted wireless power will be optimized. Then, analytical models have been presented which make it possible to calculate the axial force which was exerted between two axially Helical magnetized and two thin coils in air. Also, the analytical stiffness calculation applied between these distributions of magnetic source has been presented. These models have been compared with the FEM to show an appropriate consistency. [ABSTRACT FROM AUTHOR]

The current non-renewable energy-based transportation system is getting us places while simultaneously killing us. The global oil and gas markets are the most sizeable - 4677.45 billion-dollar industries - in the world in terms of revenue due to human dependency on fast transportation. In pre-industrial revolution times, the average rate of global temperature increase was 0.13-degrees Fahrenheit. The introduction of fossil fuel-based vehicles has more than doubled those numbers, thus resulting in accelerated global warming. Electrical vehicles have a few challenges of their own that we have discussed in this paper, finding solutions to them are based on Yoichi Hori's paper to make our model more energy efficient. Our model has been validated by researches done in the past, this paper combines the findings of formerly published papers to produce a working model of our current design with proof of concept. To reduce the carbon footprint of coal and fossil fuel-based electricity production, solar energy has been considered to charge the vehicle. It is not only an efficient choice, but it is also economical. It saves the consumer around $1778 annually in fuel cost and 38.5g/km in carbon emissions. Using a wireless charging station solves a major UX design problem, making charging an effortless experience. This method is 88.05% efficient at 31.5A. Supercapacitors have high power densities, therefore, being a good power source for the EV in theory, however, research shows that current supercapacitor technology is unable to perform as a standalone power source. Hence our model combines supercapacitors with conventional batteries to power the EV, additionally a break recovery system stores energy in the supercapacitor. This powers the car's acceleration. Our model priorities the use of materials that can be repurposed hence carbon-based electrodes are used - Graphene is a potential choice as it consists of high energy cycles. [ABSTRACT FROM AUTHOR]

Pure electric vehicle (PEV) equipped with a dual-motor coupling drive system can make full use of the high efficiency working range of the motor in order to improve vehicle efficiency. In order to further expand the application range of the system and to improve its practical application, this paper designs and proposes a new dynamic coupling drive system of three axis-double working modes, which is based on the Simpson planetary gear train. The new system adopts two planetary gears (P1 and P2), and the two sun gears of double rows, planetary carrier of P1 and gear ring of P2 are bunded. The power output of the P1 gear ring (mode 1) and P2 planetary carrier (mode 2) is realized by a controlling wet clutch. This paper adopts the linear interpolation method, least square method and 5-fold CV cross validation method to establish the full load speed characteristics and efficiency characteristics models of two motors (13 and 30 kW). This paper proposes an optimization design method based on an improved simulated annealing (I-SA) algorithm for new system parameter matching and working mode switching strategy determination. The results show that the modeling accuracy of the two motors is high, and the mean value of MAPE is 4.337%. The proposed optimization design method achieves the demand goal of PEV effectively. The I-SA algorithm has good effectiveness and fast convergence, the mean efficiency of the optimized PEV is 83.91% under all working conditions, the maximum speed is 142.56 km/h and the power utilization rate of the dual-motor is 100%. This study proposes a new hardware system and a design optimization method on software and provides a direct reference for the research of PEV drive systems by combining hardware with software. [ABSTRACT FROM AUTHOR]

Since the beginning of the 19th century, the natural environment of the planet has been placed under the dire threat of climate change. This has been caused by greenhouse gas (GHG) emissions from the burning of fossil fuels. GHG emissions threaten to alter the planet's ecosystems disastrously and permanently. Statistics reveal that Australian individuals are among the highest GHG emitters on the planet, and that the transport sector contributes nearly one-fifth of the nation's GHG emissions. It is suggested that significant reductions in Australian GHG emissions are urgently required, and it is considered that those reductions might be helped by a transition to electric vehicles (EVs) in the transport sector. This paper looks at the consumption of motor vehicle fuels in Australia's transport sector and suggests how a reduction in GHG emissions might be achieved. It suggests that the electrification of Australian motor vehicles could eliminate up to 20 per cent of existing Australian GHG emissions. The paper presents further findings from a case study that was conducted on a Mitsubishi Outlander Plug-in Electric Hybrid Vehicle (PHEV) in remote Western Australia from 2016-17. That study is updated and extended in this paper to October 2021. The paper uses published statistical data from the Organisation for Economic Co-operation and Development (OECD) and Australian government agencies to support its findings, conclusions and suggestions for further research. It looks at the rate of transition to electric vehicles and concludes that, while the transport sector's growing contribution to Australia's overall GHG emissions could be significantly reduced by the transition to electric vehicles, there is some way to go. The paper suggests that there are significant economic factors inhibiting the adoption of electric vehicles in Australia. However, rising fuel prices could encourage the transition away from vehicles powered by environmentally damaging internal combustion engines towards electric vehicles in Australia. [ABSTRACT FROM AUTHOR]

Deep learning technology has advanced rapidly and has started to be applied for the detection of welding defects. In the manufacturing process of power batteries for new energy vehicles, welding defects may occur due to the high directivity, convergence, and penetration of the laser beam. The accuracy of deep learning prediction relies heavily on big data, but balanced big data of welding defects is hard to acquire at the battery production site. In this paper, the authors construct a dataset named RIAM, which consists of images captured from an industrial environment for laser welding of power battery modules. RIAM contains four types of images: Normality, Lack of fusion, Surface porosity, and Scaled surface. The characteristics of RIAM are carefully considered in the application scenarios. Moreover, this paper proposes a gradient-based unsupervised model named Grad-MobileNet, which can be trained with only a few normal images and can extract the feature gradients of the input images. Welding defects can then be classified by the gradient distribution. This model is based on MobileNetV3, which is a lightweight convolutional neural network (CNN), and achieves 99% accuracy, which is higher than the accuracy expected from supervised learning. [ABSTRACT FROM AUTHOR]

The transportation sector is a significant contributor to greenhouse gas emissions, which contribute to climate change. In recent years, Electric Vehicles (EVs) have emerged as a promising solution to reduce the carbon footprint of transportation. However, one of the biggest obstacles to its widespread adoption is the lack of adequate charging infrastructure. Solar-powered EV charging stations present a promising solution to this problem, as it can provide a clean and renewable energy for charging EVs while reducing the strain on the grid during peak hours. This paper presents a detailed analysis of the technical requirements for designing and developing solar-powered EV charging stations. This includes considerations such as the location, size, and capacity of the stations, as well as the types of Evs which are designed to charge. The research also examines the economic feasibility of these stations, including the initial capital costs, operating costs, and potential revenue streams. [ABSTRACT FROM AUTHOR]

A shared autonomous electric vehicle (SAEV) fleet and the vehicle-to-grid (V2G) strategy both have great potential to reduce GHG emissions. As these concepts have complementary value, they are even more promising combined. However, to the best of our knowledge, no research has yet been conducted on locating charging infrastructure for SAEVs with V2G feasibility. For this construction, the challenge lies in the fact that both mobility demand (mainly for SAEVs) and energy (for any installation of charging infrastructure) have a major influence on this problem. To find the optimal charging infrastructure (CI) allocation for SAEVs with V2G operations, both mobility requirements and grid constraints must be considered. In this paper, we find that optimization models are the most frequently used method to solve the CI allocation problem. We conduct separate examinations of the V2G and SAEVs location optimization models that have been formulated in the literature, for which objective functions are used, and which constraints are considered (with respect to mobility and the electric grid). We find that SAEV and V2G models have overlapping elements, but remain disjunct in their respective perspectives. CI allocation for SAEVs mainly takes mobility into account, but tends to ignore grid constraints or impacts. On the other hand, CI allocation for V2G focuses on the distribution network and the grid, forgetting about mobility demand. To take advantage of the SAEV-V2G potential, future research should combine mobility and grid aspects to find the optimal CI locations for SAEVs with V2G feasibility. [ABSTRACT FROM AUTHOR]

With the development of electric vehicles (EVs), a large number of electric vehicle charging stations (CSs) have been rapidly rolled out to meet the charging demand of EVs. However, high construction costs and long payback periods motivate investigations to improve the profits of CSs. Considering the profit improvement of CSs and carbon emission reductions, this paper first proposes a carbon revenue model for CSs to participate in the carbon trading market. A charging price strategy is proposed to share the carbon revenue with EV users to reduce the charging cost of users, increase the charging income of CSs, and reduce carbon emissions. By describing the EV users' response to the charging price based on the fuzzy theory, this paper establishes the charging behavior model of EV users and solves the profit optimization of the dynamic charging price model by particle swarm optimization algorithm. Finally, the results of the simulation case demonstrate the effectiveness of the proposed strategy. A sensitivity analysis of various grid power purchase prices illustrates the difference between the fixed and dynamic charging price methods. The dynamic charging price method is superior, and under the power purchase price of 0.5 yuan/kWh, it can lower EV user charging costs by 16.25%, improve CS profits by 30.09%, and reduce carbon emissions by 74.68%. [ABSTRACT FROM AUTHOR]

The transportation system contains many fossil fuel-based automobiles equipped with the internal combustion engine that results in the pollution of the environment and greenhouse gas emissions. In recent years, to replace these automobiles with clean choices, electric vehicles are developed. So far, three kinds of electric vehicles including hybrid, plug-in, and full-electric vehicles are introduced. In the hybrid and plug-in electric vehicles, both the internal combustion engine and electric motor are used to move the vehicle. However, in the full-electric vehicle, the movement of the vehicle is done only by the electric motor. Due to the development of the electric vehicles in the transportation system, different aspects of these vehicles such as reliability must be studied. The reliability indices of the electric vehicles are affected by the failure rate of the composed components. Thus, to exactly determine the reliability performance of the electric vehicles, the failure rate of the main composed components affected by different parameters such as speed of the vehicle and temperature is taken into account. In the present paper, to accurately study the reliability of all-electric vehicles, the impact of variation in the temperature and vehicle speed on the failure rate of the composed components including battery, inverter, electric motor, and other static and rotation parts of the full-electric vehicle and consequently the failure rate of the vehicle is investigated. To determine the impact of operating temperature on the failure rate of composed components, the Arrhenius law is proposed. Based on the variation in the vehicle failure rate in terms of the vehicle speed and temperature, the reliability of the electric vehicle at different conditions is determined. It is concluded from numerical results performed in the paper that the failure rate of the understudied full-electric vehicle varies between 3.5 and 6 failures per year when the temperature varies between 0 and 50°C and the vehicle speed varies between 0 and 200 km/h. [ABSTRACT FROM AUTHOR]

Summary: In order to solve the problem that the unsatisfactory accuracy of SOH estimation method, which seeks the relationship between battery life and external characteristics through experiments, is restricted by battery consistency in a large number of battery applications, this paper proposes an SOH estimation framework which can automatically correct the errors caused by the battery consistency problem online. The SOH framework realizes the automatic online fast correction of SOH estimation error through the designed closed‐loop feedback framework. Another advantage of this framework is that it can achieve accurate estimation for the batteries state of health (SOH) during the irregular charging and discharging process of electric vehicles. And in this framework, a new equivalent circuit based on the autoregressive (AR) model is proposed to reduce the complexity of the battery method while ensuring the accuracy of the estimation, which has better robustness in practical applications. Finally, it is proved that the online estimation of lithium‐ion batteries SOH during discharge proposed in this paper has better practicability and higher estimation accuracy by comparing with the traditional SOH method of external feature relationship. [ABSTRACT FROM AUTHOR]

In the energy crisis and post-epidemic era, the new energy industry is thriving, encompassing new energy vehicles exclusively powered by lithium-ion batteries. Within the battery management system of these new energy vehicles, the state of charge (SOC) estimation plays a pivotal role. The SOC represents the current state of charge of the lithium-ion battery. This paper proposes a joint estimation algorithm based on genetic algorithm (GA) simulating biogenetic properties and support vector regression (SVR) to improve the prediction accuracy of lithium-ion battery SOC. Genetic algorithm support vector regression (GASVR) is proposed to address the limitations of traditional SVR, which lacks guidance on parameter selection. The model attains notable accuracy. GASVR constructs a set of solution spaces, generating initial populations that adhere to a normal distribution using a stochastic approach. A fitness function calculates the fitness value for each individual. Based on their fitness, the roulette wheel method is employed to generate the next-generation population through selection, crossover, and mutation. After several iterations, individuals with the highest fitness values are identified. These top individuals acquire parameter information, culminating in the training of the final SVR model. The model leverages advanced mathematical techniques to address SOC prediction challenges in the Hilbert space, providing theoretical justification for handling intricate nonlinear problems. Rigorous testing of the model at temperatures ranging from −20 ∘ C to 25 ∘ C under three different working conditions demonstrates its superior accuracy and robustness compared to extreme gradient boosting (XGBoost), random forest regression (RFR), linear kernel function SVR, and the original radial basis kernel function SVR. The model proposed in this paper lays the groundwork and offers a scheme for predicting the SOC within the battery management system of new energy vehicles. [ABSTRACT FROM AUTHOR]

As the ownership of electric vehicles (EVs) continues to rise, EVs are becoming an integral part of urban microgrids. Incorporating the charging and discharging processes of EVs into the microgrid's optimization scheduling process can serve to load leveling, reducing the reliance of the microgrid on external power networks. This paper proposes a novel two-stage, dual-layer distributed optimization operational approach for microgrids with EVs. The lower layer is a distributed control layer, which ensures, through consensus control methods, that every EV maintains a consistent charging/discharging and state of charge (SOC). The upper layer is the optimization scheduling layer, determining the optimal operational strategy of the microgrid using the multiagent reinforcement learning method and providing control reference signals for the lower layer. Additionally, this paper categorizes the charging process of EVs into two stages based on their SOC: the constrained scheduling stage and the free scheduling stage. By employing distinct control methods during these two stages, we ensure that EVs can participate in the microgrid scheduling while fully respecting the charging interests of the EV owners. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid (V2G) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid. This paper analyzes the relevant research progress and hotpots of V2G by using CiteSpace 6.1.R6 software to construct a visualization graph, which includes keyword co-occurrence, clustering, and burstiness, and further systematically summarizes the main trends and key results of V2G research. First, the connection between electric vehicles and the grid is outlined and the potential advantages of V2G technology are emphasized, such as energy management, load balancing, and environmental sustainability. The important topics of V2G, including renewable energy consumption, power dispatch, regulation and optimization of the grid, and the smart grid, are discussed. This paper also emphasizes the positive impacts of V2G technologies on the grid, including reduced carbon emissions, improved grid reliability, and the support for renewable energy integration. Current and future challenges for V2G research, such as standardization, policy support, and business models, are also considered. This review provides a comprehensive perspective for scholars and practitioners in V2G research and contributes to a better understanding of the current status and future trends of V2G technology. [ABSTRACT FROM AUTHOR]

Predictive energy-saving control (PEC) is aimed at reducing energy consumption by designing the vehicle speed while considering future road and traffic information. In particular, the slope of the road ahead is necessary and critical for PEC. This paper proposes a road slope prediction method for production vehicles that uses the nonlinear autoregressive (NAR) neural network model based on road slope sensors. To adaptively balance the energy savings and trip time, this paper proposed a real-time variable weight PEC method for a four-wheel-drive (4WD) intelligent electric vehicle. The weight coefficients are automatically changed according to the characteristics of the road slope, where the vehicle energy-saving rate on the steep downhill road can be maximized. The results of real-time simulation on the dSPACE platform indicated that the road slope predictive model can be run in real time and adapted to changes in road slope and speed. The root mean square error (RMSE) of the predictive results is 0.3063. On a steep downhill road, the energy-saving rate of the proposed PEC method can reach 30.87% at a small expense of time of 3.75%. On uphill and flat roads, energy can be saved by 6.35% at a time cost of 3.0%. Compared with the PEC with constant weight factors, the two control objectives of energy savings and traveling time can be better balanced on various types of roads. [ABSTRACT FROM AUTHOR]

The escalating growth of electric vehicle (EV) load has emphasized the growing importance of effective scheduling strategies. Due to the discrepancies among EV owners, their responses to scheduling can differ significantly. Therefore, to achieve better scheduling results, it is crucial to consider the impact of these discrepancies on the optimal scheduling. This paper proposes a classified scheduling method for different types of EV owners. According to charging and vehicle-to-grid (V2G) data of EV owners, the K-means clustering algorithm (K-means) is used to classify EV owners, and the demand response (DR) model is established based on the classification results. The DR model is designed to account for the diverse responses of different EV owners, and the price elasticity, time gap elasticity, and preference time elasticity are important factors in the model. This paper adopts the maximization of smart grid's revenue as the optimization objective through three approaches: (1) modifying the charging and V2G of EV; (2) obtaining V2G prices for all types of EV; and then (3) adjusting the power output of each unit. To evaluate the proposed method, the IEEE 10-unit system is employed for simulation, and the optimization problem is solved using the CPLEX solver. Compared to previous studies, the proposed classified scheduling method exhibits significant improvements in terms of revenue maximization, load distribution among different types of EVs, generation cost savings, and load variance reduction. [ABSTRACT FROM AUTHOR]

New energy vehicles have better clean and environmental protection characteristics than traditional fuel vehicles. The new energy engine cooling technology is critical in the design of new energy vehicles. This paper used oneand three-way joint simulation methods to simulate the refrigeration system of new energy vehicles. Firstly, a k-ε turbulent flow model for the cooling pump flow field is established based on the principle of computational fluid dynamics. Then, the CFD commercial fluid analysis software FLUENT is used to simulate the flow field of the cooling pump under different inlet flow conditions. This paper proposes an optimization scheme for new energy vehicle engines' "boiling" phenomenon under high temperatures and long-time climbing conditions. The simulation results show that changing the radiator's structure and adjusting the thermostat's parameters can solve the problem of a "boiling pot." The optimized new energy vehicle engine can maintain a better operating temperature range. The algorithm model can reference each cryogenic system component hardware selection and control strategy in the new energy vehicle's engine. [ABSTRACT FROM AUTHOR]

Purpose: In the electronic assembly industry, low-temperature soldering holds great potential to be used in surface mounting technology. Tin–bismuth (Sn–Bi) eutectic alloys are lead-free solders applied in consumer electronics because of their low melting point, high strength and low cost. This paper aims to investigate how to address the problem of hot tear crack formation during Sn–Bi low-temperature solder (LTS) in the mass production of consumer electronics. Design/methodology/approach: This paper explored the development of hot tear cracks during Sn–Bi soldering in the fabrication of flip chip ball grid arrays. Experiments were designed to simulate various conditions encountered in Sn–Bi soldering. Quantitative analysis was conducted on the number of hot tear cracks observed in different alloy compositions and solder volumes to explore the primary cause of hot tear cracks and possible methods to suppress crack formation. Findings: Hot tear cracks existed in Sn–Bi solders with different bismuth (Bi) contents, but increasing the solder volume reduced the number of hot tear cracks. Experiments were designed to test the degree of chip transient thermal warpage with temperature change, and, according to the results, glue was dispensed in specific areas to reduce chip warpage deformation. Finally, the results of combined process experiments pointed to an effective method of low-temperature soldering to suppress hot tear cracks. Research limitations/implications: The study focuses on Sn–Bi solders only without other solder pastes such as SAC305 or Sn–Zn series. Practical implications: With the growing popularity of smart electronics, especially in intelligent terminals, new energy vehicles electronics, solar photovoltaic and other field, there will be more and more demand for low- temperature, energy-saving, lead-free solders. Therefore, this study will help the industry to roll out LTS (Sn–Bi) solutions rapidly. Social implications: In the long term, lean and green manufacturing is expected to be essential for maintaining an advanced manufacturing industry across the world. Developing new LTSs and soldering processes is the most effective, direct solution for energy conservation and emission mitigation. With the growing popularity of smart electronics, especially in intelligent terminals, new energy vehicles and solar photovoltaics, there would be an increased demand for low-temperature, energy-saving, lead-free techniques. Originality/value: Although there are many methods that can be used to suppress hot tear cracks, there is little research on how to control the hot tear cracks caused by the low-temperature soldering of Sn–Bi in laptop applications. The authors studied the hot tear cracks that developed during the world's first mass production of 50 million personal laptops based on low-temperature Sn–Bi alloy solder pastes. By controlling the Bi content, redesigning the solder paste printing process (e.g. through a printer's stencil) and adding dispensing processes, the authors obtained reliable and stable experimental data and conclusions. [ABSTRACT FROM AUTHOR]

The effects of Electromagnetic Interference (EMI) in Inductive Power Transfer (IPT) for electric vehicle charging circuits are elaborated with Common Mode (CM) emission. Electromagnetic Compatibility (EMC) has to be sustained in the circuits such that it is prone to the effects of interferences, noises and disturbances. These issues can be addressed at the initial product development stage, with accurate identification of the parameters that influence the CM emission in the circuit. The analysis of EMI focuses on three features, namely Source of EMI, Coupling path and Receptor. In the IPT model, the primary source of the EMI is the power converter used for high-frequency supply to the inductive coils. The power converter switches and the heat sink with its Printed Circuit Board (PCB) design are predominant in power electronic circuits. This paper facilitates the reduction of EMI without including complex filters. The system is tested for the violated standard SAEJ2954. In addition, the article corroborates precognitive modeling by implementing compensation networks used in IPT applications and their effectiveness in reducing the leakage current caused by the converters and the coils. The paper insights the air gap variation of the coil with the power transfer efficiency and its influence on electromagnetic interferences. The Fast Fourier Transform (FFT) analysis used in PSIM shows leakage current and harmonic reduction with the compensation network and Pulse Width Modulation (PWM) technique. The fabrication model for the front end power supply to the IPT is tested. The electrical noise emission is estimated using predictive modeling. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the mileage energy consumption for an electric passenger vehicle in terms of introducing numerous speed limits. Regulations concerning the limiting of vehicle speed to30 km/h in cities or residential areas are particularly common. This restriction is intended to increase traffic safety, but at the same time introduces increased mileage fuel or energy consumption in electric drivetrain. Regardless of the energy carrier, any increase in energy causes negative effects for the environment. The analysis was focused on the mileage energy consumption of electric passenger cars for a constant speed under real traffic conditions. During the tests, the tested vehicles' speed on a specially designated road section was changed gradually by 10 km/h, simultaneously recording the car's traction parameters and mileage energy consumption. An analysis of the mileage energy consumption was then carried out for the assumed fleet of cars travelling one after another (in a so-called traffic jam), while maintaining a safe distance. This allowed for the calculation of the environment's energy burden caused by a fleet of vehicles travelling on a given road section, indicating that a reduction in vehicle speed causes an increase in the vehicles' energy consumption. Both total and mileage energy consumption of electric vehicles were analysed during the tests. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are a promising solution to resolve the greenhouse emission issue as well as the fossil fuels scarcity problem in the future. However, as the number of EVs significantly increases, the excessive charging demand of EV parking lots (EVPLs) in the charging station may incur an instability problem to the electricity network. In this paper, the optimal allocation and sizing of EVPLs were carried out in order to minimize the costs of the EVPLs installation and EV users' distance traveled and recharge. As a consequence, it encourages the participation of vehicle users in a demand response program related to smart charging and time of use tariffs. It is important to mention that the increasing inclusion of EVs negatively affects the voltage level and power losses of the distribution system. Therefore, this paper also performs the optimal allocation and sizing of distributed energy resources (DERs) in order to mitigate these problems. The non-dominated sorting genetic algorithm II (NSGA-II) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the IEEE 33-node test feeder and 25-node transportation network. Furthermore, it was considered a spatial distribution of EVs for the 24-h from the closeness centrality of graph theory. The optimal planning of EVPLs and DERs promoted a 35%, 23.14% and 34.11% reduction of the travel distance, voltage deviation and power losses, respectively. [ABSTRACT FROM AUTHOR]

With the update of the power transaction mode and the access of increasing electric vehicles with high randomness to the power grid, the existing carbon emission flow calculation method cannot consider the influence of carbon emission due to the fluctuation in electric vehicle charging and various transaction modes. Given the above shortcomings, this paper proposes an improved carbon emission flow model considering electric vehicle charging fluctuations and hybrid power transactions. The model first considers different transaction modes, allocates the network loss to both sides of power generation, and forms a lossless network, realizing the decoupling of the bilateral transaction mode, pool transaction mode, and network loss and then calculating the day-ahead network's carbon emission. Then, the changes in different transaction modes caused by the fluctuation in electric vehicle charging under different transaction modes are analyzed and the nodes of 'spontaneous change' are found to form a day-ahead intra-day deviation network and calculate its carbon emission flow. Finally, the calculation results are combined to obtain an improved power system carbon emission flow considering electric vehicle charging fluctuations and transaction modes. In this paper, the 33-node system is used for verification. [ABSTRACT FROM AUTHOR]