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Modelica is a very powerful language to simulate a very large set of systems, including electrical, thermal, mechanical, fluidic, control, and has already been used very extensively for several purposes, as the several Modelica conferences testify. Despite of this large literature, no paper seems to be available regarding the use of Modelica for real-time applications or hardware-in-the loop (HIL). This is a field where applications may be very fruitful. In this paper, the possibility of creating mixed software–hardware experiences (i.e., HIL), through combination of a Modelica program, the related simulation tool, a LabVIEW program, and the corresponding hardware is demonstrated. This demonstration is made using as an example a partial simulator of an electric vehicle running in a stand-alone PC, which communicates via User Datagram Protocol (UDP) packets with another PC running the LabVIEW program, which in turn is physically connected with the hardware-under-test. The obtained results are satisfying, given the inherent delay times due to the UDP communication. [ABSTRACT FROM AUTHOR]

In the COVID-19 era, automobiles with internal combustion engines are being replaced by eco-friendly vehicles. The demand for battery electric vehicles (BEVs) has increased explosively. Treatment of spent batteries has received much attention. Battery life can be extended via both efficient charging and driving. Consideration of the vehicles ahead when driving a BEV effectively prolongs battery life. Several studies have presented eco-friendly driving profiles for BEVs, the cited authors did not develop a BEV driving profile that considered battery life using reinforcement learning. Here, this paper presents a method of driving profile optimization that increases BEV battery life. This paper does not address how to regenerate spent batteries in an eco-friendly manner. The BEV driving profile is optimized employing a deep Q-network (a reinforcement learning method). This paper uses simulations to evaluate the effect of the driving profile on BEV battery life; these verified the applicability of our model. Finally, the speed profile optimization method was limited to improve energy efficiency or battery life in rapid speed change sections. [ABSTRACT FROM AUTHOR]

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system's feasibility. [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]

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]

This paper assesses the transport system of Addis Ababa, Ethiopia, taking factors such as the number of vehicles, roadway width, speed of vehicles, longitudinal grade, and proportion of both fuel and electrical vehicles by dividing vehicles into seven classes, namely, car, minibus, small bus, coach, small truck, heavy truck, and truck trailer, to determine CO2 emission, CO emission fuel consumption, and electric consumption in addition to the percent to replace ICE vehicles. After selecting eight representative road sections in Addis Ababa city, input data was collected from both primary and secondary sources. Simulation of urban mobility (SUMO) is used to model the existing road transport system and two other scenarios, cases being 20% and 40% replacement of internal combustion engine vehicles by electric vehicles. Among the vehicle types studied under this paper, the SUMO results show that coaches are with the highest CO2 emission, releasing an average amount of 28.442 grams of CO2 every time step, while cars are with the lowest CO2 emission value of 6.542 grams. Minibuses are the top CO emitters, releasing an average of 0.420 grams of CO every time step, and truck trailers emit the smallest CO emission, 0.025 grams. Regarding electric consumption, the truck trailer is the vehicle type with the highest electric consumption, with a value of 2.282 kwh (watthour) consumption every time step, and cars are the least electricity-consuming vehicles, with a value of 0.151 kwh. The fourth point is fuel consumption; besides the high CO2 emission, coaches' consumption of fuel is leading by 8.946 grams, and cars use 2.087 grams of fuel every time step. Totally, public transport vehicles are responsible for higher emissions and huge fuel consumption. Therefore, if our transport system encourages the penetration of electric vehicles into the road transport system, a healthy and energy-efficient environment is reserved. Again, from a financial and environmental standpoint, the replacement of 40% of ICE vehicles by EVs enhances us with reduced costs and a green environment. [ABSTRACT FROM AUTHOR]

The rapid deployment of electric vehicles (EVs) in Jordan requires massive efforts to prepare the infrastructures that serve the transportation sector. The lack of EV charging stations is the major obstacle that faces EV drivers. Utilizing renewable energy in EV charging stations contributes to the spread of these stations. Renewable energy technologies are environmentally friendly as they mitigate greenhouse gases that cause the global warming phenomenon. In this paper, an EV charging station, based on a PV-biodiesel-battery hybrid system, is investigated. The importance of this article is to discuss the hybrid system of PV and waste vegetable oil (WVO) along with storage that applies the maximum reliability supply, having an environmentally friendly supply and achieving the lowest energy cost of charging. This station is designed based on renewable and WVO utilization. In fact, WVO, coming from restaurants, is exploited to produce electricity by a diesel generator through direct burning after converting it into biodiesel. The capacity of each station is 14 cars/day with a medium-speed charger of 7 kW. The system is simulated and optimized using iHOGA software where multiobjective optimization is applied to achieve the minimum net present cost (NPC) and CO2 emissions considering three cases, PV-diesel, PV-biodiesel, and PV-WVO, all with battery-hybrid system. These values for the EV charging station that uses the PV-biodiesel-battery hybrid system are 624408 € and 15.4 tons/year, respectively. The corresponding values are 781473 € and 15.14 tons/year and 615310 € and 18.84 tons/year for the systems that work with diesel and WVO, respectively, and energy cost is achieved in best solution to be 0.11 Euro/kWh. Simulation results show that the proposed technique led to enhanced operational efficiency in terms of both NPC and annual CO2 emissions. [ABSTRACT FROM AUTHOR]

This paper innovatively explores BEV (battery electric vehicle) users' risk attitudes and charging inertia, examining their effects on en route charging and charging route choice behavior. An attitudinal survey was conducted to explore the two latent variables of risk attitudes and charging inertia in relation to socioeconomic and travel-related characteristics. ICLV (Integrated choice and latent variable) models are adopted to estimate the latent variables and the charging choice behavior simultaneously. Specifically, uncertainty in energy consumption is first considered in the ICLV model, which is represented by the available range (AR) uncertainty. Multinomial logit (MNL) models directly incorporating socioeconomic attributes are employed as a reference for comparison with ICLV models. Results illustrate that risk attitudes and charging inertia both play significant roles in modeling en route charging choice behavior. Risk-averse users and users having charging inertia value AR uncertainty more. Battery range, charging frequency, and income emerge as the most crucial factors influencing users' intention to charge en route. The results show significant heterogeneity of BEV users in attitudes and charging choice behavior, underscoring the importance of accounting for the heterogeneity in en route charging demand estimation and deployment optimization of public charging stations, particularly for medium-to long-distance trips. [ABSTRACT FROM AUTHOR]

The present GPS (global positioning system) is widely used in electric vehicle navigation; it is usually installed in the middle-to-rear position on the car's roof, near the rear windshield. Since the GPS antenna operating frequency is 1.575 GHz, the health risk of the passengers in the car in this high-frequency electromagnetic exposure is a matter of concern. In this paper, we construct models for a GPS antenna, an electric vehicle, and a human body. Through the multiphysics field coupling calculation in COMSOL Multiphysics, a finite element simulation software, in both frequency domain and transient analysis, we obtain the electric field strength distribution, specific absorption rate, and temperature distribution of the human body at three positions inside the electric vehicle after being exposed to the GPS antenna radiation for 30 minutes. The peak human-induced electric field is 18.4 V/m, and the peak specific absorption rate is 0.193 W/kg. The 30 minute average maximum human-induced electric field is 1.6906 V/m, which is 3.1% of the ICNIRP limit, the 30 minute average whole-body SAR is 0.0036 W/kg, which is 4.5% of the ICNIRP limit, and the 30 minute average human core temperature rise is 0.06°C, which is 6% of the ICNIRP limit. In addition, we simulate the impact of three different vehicle shell materials on the level of electromagnetic exposure to the human body, which can provide reference for electromagnetic shielding design in automobiles. The results indicate that the induced electric field strength, specific absorption rate (SAR), and temperature rise in various parts of the human body did not exceed the standard limits in the latest version of ICNIRP. This suggests that the electromagnetic exposure from the GPS antenna in a typical automotive environment does not pose a threat to human health. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of the global economy and technology, the demand for fossil fuels in transportation and power industries is ever‐growing, which inevitably leads to lower fossil fuel storage and higher prices. Electric vehicles (EVs) are widely used as an effective solution to energy shortage and environmental pollution. However, there exist numerous obstacles, such as unmatchable capacity and unsuitable location. Therefore, an appropriate size and location of the charging station (CS) are very crucial. Based on a thorough investigation of 138 literature, this paper comprehensively summarizes the past from three aspects including charging technology, CS model, and methodologies. Besides, a comprehensive summary of the charging way and its level is proposed for a more overall analysis with enriched technologies diversity. Furthermore, two different CS models are discussed detailly in the aspects of the objective function, factors, advantages and disadvantages, applicability, and complexity. Additionally, a detailed summary of three methodologies, including meta‐heuristic, multi‐criteria decision making (MCDM), and analytical methods, are proposed and a comprehensive evaluation and their advantages/disadvantages, applicability, and complexity are summarized and assessed thoroughly. Finally, 11 valuable and insightful findings are drawn and two promising challenges, two opportunities, and three future directions are proposed for further research. Highlights: Key technical difficulties and future challenges of sizing and placement of charging stations (CSs) are discussed.Most representative papers are selected and presented in table form for readers to compare more conveniently.Two different kinds of models and evaluation indexes are introduced in detail and each model is compared and evaluated fairly from different angles.Thirty‐three methods of sizing and location determination are summarized and compared strictly, and the advantages and disadvantages, applicability, and complexity of each method are dedicated to being described.Eleven main findings and seven constructive suggestions are put forward because of the development status of electric vehicles. [ABSTRACT FROM AUTHOR]

In recent years, electric vehicles have been widely promoted by virtue of environmental protection and pollution-free advantages. In the future, they are expected to become a mainstream means of transportation. However, as new services and business models become more and more closely related to the distribution network, the traditional AC distribution district has limited ability to respond to the large-scale access of electric vehicles. Therefore, it is becoming increasingly important to improve the ability to respond to electric vehicle access, reduce the negative impact of large-scale electric vehicle access on the grid, and predict and calculate the uncertainty caused by electric vehicle load access. Therefore, this paper proposes the concept of flexible distribution district, constructs the security operational boundary model for flexible distribution district, and uses "security margin percentage" as the measurement mechanism to provide dispatchers with an intuitive system operating status. In addition, this paper also proposes an effective uncertainty quantification method for the problem that it is difficult to measure the changes of bus voltage and line current at network nodes when large-scale electric vehicles are connected to the network. Numerical results demonstrate both the extensibility and the practicality of the proposed security operational boundary. In addition, the proposed uncertainty quantification method can reflect the impact of load changes on the characteristics of the power grid, which is helpful to study the improvement significance of adjusting electric vehicle load changes to distribution network optimization. [ABSTRACT FROM AUTHOR]

In this paper, a novel model of an energy management system (EMS) for a microgrid (MG) under uncertain conditions is proposed. The MG consists of renewable photovoltaic and wind sources along with electric vehicle parking lots. Hence, the model incorporates the uncertainties of renewable DGs, parking lots, and also load. In this study, the MG operation cost and voltage stability index are considered objective functions. A novel combined algorithm (hMOPSO-HS) is proposed for microgrid energy management. The hMOPSO-HS algorithm is a combination of the mutant multiobjective particle swarm optimization (MOPSO) algorithm and the harmony search (HS) algorithm. The simulations are performed in two parts, with and without considering the uncertainty. The comparative analysis involves evaluating the optimization outcomes achieved by the hMOPSO-HS algorithm in contrast to various other metaheuristic algorithms for multiobjective optimization. The simulation findings validate the superior efficacy of the hMOPSO-HS algorithm compared to other approaches. Also, the simulation results showed that in the conditions of uncertainty, the operating cost is 6.1% higher and the microgrid stability index is 6.8% lower. Also, considering the uncertainty has caused the penalty for energy not supplied (ENS) and demand response program (DRP) costs to increase by 3% and 4%, respectively. [ABSTRACT FROM AUTHOR]

The deterioration of the environment in the 21st century has made environmental issues one of the most severe tests for modern society. With this comes a change in energy structure from high-carbon to low-carbon direction, and electric vehicles are gradually developing into the darling of a city with low-carbon transportation and safe travel. This paper carries out a systematic analysis of landscape design and environmental protection in the development of new energy electric vehicle charging facilities in urban habitat. By categorizing the content and provisions of published domestic and international standards, new requirements for standardization are obtained, including barrier-free design, electromagnetic radiation, child safety protection, and urban landscape integration. Among them, ecological landscape public charging facilities can enhance the overall quality of urban environment. This paper analyzes the necessity of landscape design in charging facilities, explores the ecological concepts extended by macroscopic landscape design principles and the problems of public charging facilities, and proposes a design and evaluation method of ecologically landscaped public charging facilities based on hierarchical analysis and neural networks. The hierarchical analysis method is introduced to establish a landscape design assessment index system, and then a neural network is introduced to describe the characteristics of electric vehicle charging, and the landscape design assessment learning samples are trained to establish a landscape design assessment model. Finally, a comparison experiment is conducted with other landscape design assessment methods using specific examples, and the results show that the proposed method has more obvious advantages in ecological landscape public charging facility design assessment with high accuracy, faster landscape design assessment, charging efficiency, and environmental protection. [ABSTRACT FROM AUTHOR]

With the popularity of electric vehicles, electric vehicles have become a hot research topic, and the performance of permanent magnet synchronous motor has become increasingly prominent in recent years. Therefore, the application of permanent magnet synchronous motor in electric vehicles has become a possibility. Based on the analysis of the existing permanent magnet synchronous motor technology for electric vehicles, this paper will start from the motor body design. In this paper, the performance parameters of permanent magnet synchronous motor meeting the requirements of vehicle operation are estimated by analyzing the parameters of electric vehicle, and the motor is modeled based on the motor design software Motor-CAD. In this paper, the parametric analysis module in the software is used to optimize the parameters of motor permanent magnet, stator slot width, air gap magnetic density, and air gap length. Finally, this paper uses OptiSLang software to simulate and analyze the permanent magnet synchronous motor. It improves the driving performance of automobile motor and reduces energy consumption, so that the permanent magnet synchronous motor can better meet the requirements of electric vehicles. The experimental results show that the optimal range of motor split ratio is large, in the range of 0.85~0.9. [ABSTRACT FROM AUTHOR]

Summary: The latest breakthroughs in artificial intelligence resulted in their adoption in the electric vehicle's battery management systems. Simultaneously, the remarkable increase in the number of embedded systems in electric vehicles (EVs) has led to the search for new optimized state of charge (SOC) estimation strategies suitable for implementation on small and limited MCUs. In this regard, this paper provides a design and quantization processes of the 1D convolutional neural network (1D CNN) and the GRU‐recurrent neural network (GRU‐RNN), devoted for SOC estimation. Both the NN algorithms are designed and trained using data collected from an 18650 Li‐ion battery. Furthermore, to account for the limited computational resources of the EV MCUs, the pre‐trained models are then converted into highly optimized C‐codes using the latest model quantification techniques. In this regard, this paper investigates, the performances of the STM32Cube.AI and the TensorFlow Lite for microcontroller (TFLite Micro), in quantizing the activation functions and weights of the proposed NN models. This is achieved by converting these latter from 32‐bits floating‐point to 8‐bit integer precision. The obtained experimental results, under different profiles, indicate that the 1D CNN is more accurate than the GRU‐based model with root mean squared error (RMSE) of 2.33% and mean absolute error (MAE) of 1.62%. Furthermore, the impact of the quantization using STM32Cube.AI is compared with that using the TFLite Micro. The obtained results, demonstrated the superiority of the 1D CNN model quantized using the STM32Cube.AI. This latter reduces the flash memory size of the 1D CNN model from 10.82 to 2.89 KB and the RAM size from 2.49 to 1.04 KB, compared to the TFLite Micro, which reduces the RAM size from 7.0107 to 4.23 KB, and the flash from 43.361 to 15.88 KB. [ABSTRACT FROM AUTHOR]

Electric vehicles are not widely adopted without proper charging infrastructure, despite their environmental benefits and growing popularity in transportation. This paper focuses on the location problem of charging infrastructure to achieve a more optimized charging facility layout. The charging demands of electric vehicles can be divided into two categories. The first category is generated at network points such as shopping malls, office buildings, parking lots, and residential areas. The second category is generated along the flow of network paths, such as on the highway and on the way to and from work. The goal of this problem is to maximize both categories of charging demands using a nonlinear integer programming model. We introduce the spatial intersection model to obtain the data on path demand. The spatial intersection model is introduced to obtain data on path demand. In addition, future demand is taken into account in the optimization through data forecasting. Then, the greedy algorithm is designed to solve the optimization model. The effectiveness is proved by a lot of random experiments. Finally, the effects of parameters are analyzed by a case study. The location decision of charging stations for both demands is more reasonable than only one type of demand consideration. The proposed model ensures the coverage and appropriate extension of the charging network. [ABSTRACT FROM AUTHOR]

Equipped with two power sources, the dual-driving powertrain system for pure electric vehicles has a driving mode different from traditional electric vehicles. Under the premise that the structural form of the transmission system remains unchanged, the following transmission schemes can be adopted for double drive electric vehicles according to the demand power: the main and auxiliary electric transmission scheme (two motors are driven separately with dual-motor coupling drive), the transmission scheme in which the two motors always maintain coupling drive, and the speed-regulating type electric transmission scheme (the main motor is always responsible for driving, and the auxiliary motor is responsible for speed regulation). Therefore, a significant difference exists in the design methods of the power transmission system of double drive electric vehicles and existing vehicles. As for such differences, this paper adopts intelligent algorithm to design the parameters of the transmission system and introduces the genetic algorithm into the optimization design of parameters to obtain the optimal vital parameters of the power transmission system based on computer simulation. The prototype car used in this paper is a self-owned brand car; MATLAB/Simulink platform is used to build the vehicle simulation model, which is used for the computer simulation analysis of the vehicle dynamic performance and economy. It can be seen from the analysis result that the system parameters obtained by using the global optimization method proposed in this study can improve the vehicle dynamic performance and economic performance to varying degrees, which proves the efficiency and feasibility of the optimization method. [ABSTRACT FROM AUTHOR]

Summary: The energy sector is undergoing an enormous transition with new market mechanisms affecting the market and the traders. In the smart grid environment, an auction‐based market can be useful to ensure trading between electric vehicles (EVs) and distribution companies (DisComs). However, the participation of EVs in the energy market poses specific issues. The first one is the involvement of aggregator/ auctioneer in trading procedures, which affects the energy trading as well as privacy of the EV owners. Second one is the auction mechanism should fulfill all critical economic properties, strategic and non‐strategic constraints. In order to address these issues, a novel aggregator free mechanism is proposed in this paper allowing EVs to securely participate in the ancillary services market. A smart contract deployed in Blockchain is used for market‐clearing. This paper proposes two kinds of implementation of auction‐based ancillary services market in the distribution system. Through an extensive study performed on a hypothetical group of 76 561 EVs and 13 DisComs, it is found that the performance of the proposed scheme is superior in decentralized implementation compared to centralized implementation. [ABSTRACT FROM AUTHOR]

Summary: Electric and hybrid electric vehicle technology demonstrates a performance to replace the internal combustion engines (ICE) in the current scenario. It attracts attention with improved fuel or energy efficiency with lower emissions. The overall system performance depends on powertrains, types of energy sources, electro‐electronic interfaces, and energy management strategies (EMS). Significant issues of battery‐powered electric vehicles (EV) are effects on the range, battery life, EV performance, battery maintenance, and replacement cost. Hybrid power source system (HPSS) solves EV challenges to a large extent. A hybrid combination of battery and supercapacitor (SC) to power the EV enhances the overall performance and life of the vehicle. Learning and integrated‐based EMSs are gaining attention, with their ability in accurate and fast response in power handling among various sources. This paper analyses various DC‐DC converter topologies of HPSS and compares multiple EMS with recent developments. In the context of challenges involved in EVs and research gaps that are discussed in the paper, EMSs need to be enhanced. The EMSs must consider the inputs for varying driving behaviors, road traffic, load, and environmental conditions to assure the flexibility of EV among different users across the globe. This is achieved by the management of SC power available to support the vehicle during sudden power requirements and enabling it to recuperate braking energy to improve the energy efficiency throughout the trip. Lastly, recommending precise research directions to achieve the development and improvement of the EMS and power electronic interfaces. [ABSTRACT FROM AUTHOR]

Recently, electric vehicles (EVs) have received more and more attention, but the problem of the insufficient range is still the main reason that hinders electric vehicles to travel long distances. Under the premise of the battery capacity without technological innovation, the path planning method can ensure the safety and efficiency of electric vehicles in long-distance travel. This paper studies the actual freeway network to optimize the vehicle driving path and give the charging strategy based on the shortest travel time of a single vehicle. In this paper, a path and charging strategy planning model is proposed. In this model, the shortest travel time of a single vehicle is taken as the objective function, and the state of charging equipment in the actual road network and the safe electric quantity are considered as constraints. And the genetic algorithm is used to solve the model. Through case analysis, the rationality and optimization efficiency of the model proposed in this paper are verified. Finally, the sensitivity analysis of the three parameters of traffic volume, temperature, and travel speed is carried out with the Shanghai-Nanjing freeway network. The experimental results can get the nodes with the highest service pressure in the network, which can provide a theoretical basis for charging nodes' expansion in the freeway network in the future. [ABSTRACT FROM AUTHOR]

Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios. [ABSTRACT FROM AUTHOR]

Researchers are paying attention to the current trend of electric vehicles (EVs), but there are not many places to charge them and serve as electric vehicle charging stations (EVCSs). In order to prevent the lack of EVCS for battery EVs (BEVs) on any road with all modes of operation, this paper proposed the design of a novel configuration and control structures for the isolated charging station (ICS) based on the solar PV system for efficient energy utilization to charge battery EVs (BEVs) with flexible double function cascaded converter (MPPT and DC-link voltage regulation). This was done with the aid of a battery energy storage system (BESS) as a backup/auxiliary source. The PV system is reliant on solar radiation and the daily load profile. Therefore, the BESS is employed to provide continuous power to BEVs and overcome PV's intermittent nature. The cascaded converter is used to achieve the MPP and to regulate the DC-link voltage to its reference value (100 V), while the buck-boost matching bidirectional DC-DC voltage converters (MBDVCs) are used for charging and discharging mode/buck and boost mode, respectively, of BEVs and BESSs. Additionally, the MBDVCs are used as an interface between the ICS sources with the load through the developed control systems. The outcomes of various operating modes show that the system functions effectively. To verify the system performance, the MATLAB/Simulink application builds the system setup. [ABSTRACT FROM AUTHOR]

Summary: Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium‐ion battery (LIB) and a supercapacitor (SC)‐based HESS (LIB‐SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal‐sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system‐level optimizations, include component sizing (CS) and power‐energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB‐SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB‐SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB‐SC HESS are provided to inspire researchers for further studies. Highlights: Lithium‐ion battery (LIB) and supercapacitor (SC)‐based hybrid energy storage system (LIB‐SC HESS) suitable for EV applications is analyzed comprehensively.LIB‐SC HESS configurations and suitable power electronics converter topologies with their comparison are provided.System‐level optimization of LIB‐SC HESS and generalized steps involved in implementing the optimization algorithm for component sizing (CS) and power‐energy management strategy (PEMS) are discussed.A rigorous study on CS and PEMS is presented to develop efficient LIB‐SC HESS.Current challenges and future recommendations for the development of LIB‐SC HESS for EV applications are provided. [ABSTRACT FROM AUTHOR]

With the continuous development of urban intelligence, as traffic, power grids, and electric vehicles are new ideas to solve energy shortages and air control problems, they have received widespread attention from the society and strong support from the government. The charging pile is a key hub for data exchange and has typical characteristics of IoT terminals. However, the guidance of the grid connection of electric vehicles is not standardized, and the security and stability of the power grid will inevitably be affected. The blockchain has the characteristics that data is difficult to tamper with and decentralized. Based on these two characteristics, the information recorded by the blockchain is more authentic and reliable. This paper is aimed at realizing the global unified identification and management of large terminal equipment based on ubiquitous power Internet of Things equipment with the help of blockchain technology and at providing safe, efficient, and stable online management services for a large number of devices in the ubiquitous power Internet of Things. In this paper, we analyze the application possibility of blockchain technology in the current electricity market and apply blockchain technology to the electricity market to solve the drawbacks of the electricity market, as well as adding blockchain analytics to the renewable energy electricity market. The experimental data analysis shows that with the increase of the length of the blockchain, the blocking time of the new area increases correspondingly, but the overall performance declines slightly, so this scheme can meet the practical needs of a large number of concurrent access terminals in the ubiquitous power Internet of Things. It can be seen that the successful application of blockchain technology based on the power Internet of Things in electric vehicle charging piles has greatly improved work efficiency. [ABSTRACT FROM AUTHOR]

The charging profiles of plug-in electric vehicles (PEVs) have large volatility. It has brought great challenges for aggregator to accurately complete load identification and aggregated configuration. Therefore, an analysis and configuration method of responsive capacity based on clustering and the Markov model are proposed in this paper. Firstly, the adaptive two-scale clustering algorithm based on spectral clustering (ATCSC) is applied to the clustering of charging piles. The offset compensation of the extreme points is used to form the distance measurement in the clustering process. Then, the responsive aggregated power can be obtained after the change control of suitable charging piles. Finally, the variation characteristics of the actual charging profiles based on the Markov model are introduced to the reliability evaluation in the load curtailment service. Simulation results reveal the following. (1) The robustness of the proposed method is better especially for the PEV charging profiles with strong volatility. (2) The validity of the aggregated configuration is verified. Additionally, the sum of power deviation is 0.0707 kW when the change interval of control strategy is 15 min. (3) The maximum shortage of configuration is −98.0875 kW as the entropy of the volatility is 37.027. [ABSTRACT FROM AUTHOR]

With the substantial increase in the number of electric vehicles, the charging of electric vehicles without regulation and scale control will bring about problems, such as overloading of distribution transformers; the proportion of new energy power generation is also increasing year by year, and the access of new energy to the power grid will cause volatility. In order to solve the problems above, this paper proposed a coordinated and orderly scheduling strategy considering new energy consumption, which protects the interests of both users and the integrated power grid. First, a two-level vehicle-network interaction model considering both supply and demand sides was established. The upper-level model optimized the indicators on the distribution grid side, and a term of charge-discharge margin as well as grid-side load variance model was proposed. The lower-level optimization model was set based on the users' condition. The average discharge rate index was defined to evaluate the battery loss satisfaction in the scheduling strategy, which fully considered users' charging and discharging cost, and finally achieved a win-win situation between the power grid and the user. Secondly, the fast nondominated sorting genetic algorithm (NSGA-II) was used to figure out the effect of the strategy proposed in this paper, and a community is taken as an example for simulation. The results confirmed the economy and rationality of the above strategy, by rationally scheduling the charging and discharging behavior of electric vehicles, consuming new energy, restraining the fluctuation of the remaining new energy power generation, realizing the dynamic balance between the charging and discharging load and the output of new energy in a certain area, and finally effectively suppressing the fluctuation of the power grid load while improving the availability of clean energy. [ABSTRACT FROM AUTHOR]

Summary: With the commercialization of the electric vehicles, the large‐scale lithium‐ion cells as the power of electric vehicles are to be retired. The second‐use of retired cells is of great significance to improve the battery economy. A fast classification and regroup evaluation method of the retired lithium‐ion cells are proposed in this paper to improve the classification efficiency of retired lithium‐ion cells and adapt to the regroup under different conditions. The lithium‐ion cells after being balanced in parallel are charged in series with a constant current. A support vector regression (SVR) model with the parameters optimized by the particle swarm optimization (PSO) algorithm is built for the fast capacity estimation and the error will not exceed 0.3%. Different cells regrouped means different performance. In order to improve the consistency of retired cells and satisfy different using conditions, a Weighted‐K‐means algorithm is proposed in this paper to regroup the cells with the known capacity and internal resistance. The classification method is evaluated by the voltage consistency of cells using different working conditions, which indicates capacity occupied a large proportion can meet the requirement of energy condition meanwhile keep a good consistency. But the resistance will dominate in algorithm under conditions which have requirement for instantaneous power. [ABSTRACT FROM AUTHOR]

Objective: The main goal of this paper is to comprehensively model demand dispatch (DD) operation concept on prosumer plug in electric vehicles (PEVs) in the framework of a microgrid in a smart distribution grid. Method: Here, PEVs role as demand side assets that make commitments as dispatchable time‐flexible prosumers with microgrid operator (MGO). Therefore, they directly help MGO in efficient operation of the microgrid. Modeling and formulation of the proposed one level MG‐PEVs‐DD optimization problem, considering convenience of PEV drivers, MG operation reliability and loss of load, are investigated in this paper. In order to investigate the effect of various levels of DD commitment on MG operation cost and quantify worthiness brought by DD, a DD participation index (DDP) is defined. Moreover, extensive simulations are carried out on different days of the year to verify weather conditions, which influence MG generation and operation cost. The LP optimization operation problem for 24 hours of the day is solved by classic optimization in MATLAB. Results: The effectiveness of the proposed model is demonstrated on a real‐test MG system under different scenarios of conventional operation and other scenarios of applying DD. Simulation results reveal that the proposed MG‐PEVs‐DD model yields much more decrement in microgrid operation cost in comparison with conventional operation methods. Results show that increasing DDP index of PEVs, significantly decreases the cost of microgrid operation. Moreover, PEV driver's convenience has been suitably considered by the MGO through the suggested method. MG total operation cost for the cloudy coldest day of the year, is approximately twice higher than the sunny hottest day due to considerable decrement of output solar power generation in cloudy days. Discussion: Consumer‐side asset optimization by utilizing dispatchable assets, like PEVs or dispatchable loads would considerably help in handling the uncertainty in MG operation through demand dispatch operation concept with lower operation cost. [ABSTRACT FROM AUTHOR]

Summary: Fossil fuel depletion and its adverse impact on global warming is a major driving force for a recent upsurge in the development of hybrid electric vehicles technologies. This paper is a conglomeration of the recent literature in the usages of an energy storage system and power conversion topologies in electric vehicles (EVs). An EV requires sources that have high power and energy density to decrease the charging time. Commonly used energy storage devices in EVs are fuel cells, batteries, ultracapacitors, flywheel, and photovoltaic arrays. The power output from energy storage sources is conditioned to match load characteristics with the source for maximum power delivery. A DC‐DC converter topology performs this task by way of transforming voltage under the condition of power invariance. In addition, power electronics is also required to power DC/AC motors efficiently with precise control as these motors provide tractive efforts and acts as prime movers. This paper therefore brings out a critical review of the literature on EV's power conversion topologies and energy storage systems with challenges, opportunities and future directions by systematic classification of EVs and energy storage. [ABSTRACT FROM AUTHOR]

Aiming at the braking energy feedback control in the optimal energy recovery of the two-motor dual-axis drive electric vehicle (EV), the efficiency numerical simulation model based on the permanent magnet synchronous motor loss was established. At the same time, under different speed and braking conditions, based on maximum recovery efficiency and data calculation of motor system, the optimization motor braking torque distribution model was established. Thus, the distribution rule of the power optimization for the front and rear electric mechanism was obtained. This paper takes the Economic Commission of Europe (ECE) braking safety regulation as the constraint condition, and finally, a new regenerative braking torque distribution strategy numerical simulation was developed. The simulation model of Simulink and CarSim was established based on the simulation object. The numerical simulation results show that under the proposed strategy, the average utilization efficiency of the motor system is increased by 3.24% compared with the I based braking force distribution strategy. Moreover, it is 9.95% higher than the maximum braking energy recovery strategy of the front axle. Finally, through the driving behavior of the driver obtained from the big data platform, we analyze how the automobile braking force matches with the driver’s driving behavior. It also analyzes how the automobile braking force matches the energy recovery efficiency. The research results in this paper provide a reference for the future calculation of braking force feedback control system based on big data of new energy vehicles. It also provides a reference for the modeling of brake feedback control system. [ABSTRACT FROM AUTHOR]

Summary: Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric vehicles (EVs). The increasing penetration of uncertain wind power generation and transportation networks via EV charging stations has introduced challenges for system operators to manage power systems and market operations. In this context, this paper presents a stochastic AC security‐constrained unit commitment (SCUC) model to clear day‐ahead energy and reserve markets considering transportation–electricity networks through EV charging stations in the presence of uncertain wind power and generator contingency. Pre‐contingency ranking is a common strategy for reducing the time of the SCUC problem, but it provides high‐impact outages. To address this issue, generator outages ranked first are identified using the post‐contingency generator response ranking approach. The main contribution of this paper is the development of a structure with the most effective outages in the presence of uncertain wind power and transportation networks. The stochastic optimization approach is modeled through scenarios with corresponding probabilities to manage the wind uncertainty. The computationally complex proposed model is solved by a prominent bender decomposition approach. Case studies are shown on a modified IEEE 118‐bus system with 26 nodes and 74 connected links of the electricity transportation network. The results show that optimal EV travel scheduling can optimize the transportation network and a sufficient energy reserve, confirming the security of the power system with minimum operating cost. [ABSTRACT FROM AUTHOR]

Summary: Extension of driving range and battery run time optimization are necessary key points in the modeling of Electric Vehicle (EV). In this view, Battery Management System (BMS) plays a major role to ensure a safe and trustworthy battery operation, especially when using Lithium‐ion (Li‐ion) batteries in an electric vehicle. Key function of BMS is State of Charge (SoC) estimation. A well‐parameterized battery model is required for accurate state estimation. Consequently, the major factors to be considered in battery modeling are the SoC estimation and charging methodology of an effective BMS development. By focusing on these features, in this paper, the well‐known battery models such as the electrochemical model, equivalent circuit model, and data‐driven model are comprehensively reviewed along with their strengths and weaknesses. Further, the SoC estimation of a battery is also discussed by using standard methodologies such as direct estimation methods and model‐based estimation methods. The comparisons of the three most distinct battery models and the classification of SoC estimation techniques to develop a proper BMS for EV with the focus on accuracy, configuration effort, computational complexity, ease of implementation, and real‐time applications are systematically reviewed. In addition to this, convenient battery charging approaches with the consideration of some constraints such as charging time, charging efficiency, state of charge, state of health, charging voltage threshold, capacity fade, power fade, aging effect, capacity utilization, impedance rise, and temperature rise of the battery in EV are presented. Finally, the perspectives of the existing work and the recommended future research work of BMS are summarized. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have various advantages over traditional internal combustion engines (ICEs), including reduced carbon emissions, greater energy efficiency, and a lessened reliance on petroleum supplies. The use of EV charging infrastructure and power levels are reviewed in this article. Battery performance is affected by the design of the battery as well as the charger parameters and infrastructure. In this paper, the off-board and on-board charging methods with bidirectional and unidirectional power flow are compared. Hardware restrictions and connectivity concerns are eased with a unidirectional charger. The bidirectional charger enables both battery energy injection back into the grid and the vehicle. Power is constrained by the onboard charger due to its size, weight, and price. Both conductive and inductive onboard chargers are viable. For high current rates, which are not supported by EVs, it is feasible to develop an off-board charger. The time required for charging, amount of power, cost, equipment, location, infrastructure configurations, and other parameters are provided, compared, and reviewed for different power level chargers, such as level-1 (slow), level-2 (semi-fast), and level-3 (fast). [ABSTRACT FROM AUTHOR]

In recent years, renewable resources have widely been used to provide necessary energy due to the increasing fossil fuel prices, environmental pollution concerns, and the necessity to meet the growth in energy demands. The output of renewable resources especially solar and wind energies is associated with meteorological parameters, so their reliability creates many challenges for the energy sector. The consumption peak of the gas network is taken into account to adjust the frequency of the microgrid (MG). Both gas network load and electric load distributions are adjusted at the same time. In a multicarrier network, the frequency is regulated in a nonlinear manner. Meanwhile, new necessary loads for production and electric vehicles have imposed new loads on the power network; if proper management is not performed to respond to these new loads, the increase of network frequency deviations may lead the network to fail and even break down. In this paper, a network of various sources including the wind turbine, solar panel, storage (battery and flywheel), electric vehicle (EV), diesel generator (DG) electric power generation, and multicarrier energy hub (MCEH) with combined heat and power (CHP) was designed to examine vehicle-to-grid (V2G) electric vehicles. The ANFIS adaptive fuzzy control method was used to provide a fine-tuning frequency of the network. A comparison between the suggested approach and a fuzzy controller system was carried out to examine the superiority of the introduced approach to the frequency control. The simulations were obtained using MATLAB/SIMULINK software. The simulation outcomes indicated that the SMART controller can achieve good efficiency in frequency regulation and reliable output power in the examined microgrid. Further comparison in terms of effective (RMS) values and maximum frequency deviation indicates the superior performance of the proposed method over the fuzzy method. [ABSTRACT FROM AUTHOR]

This paper presents the impact of uncoordinated and coordinated charging management of electric vehicles (EVs) on the loading capability of major distribution system equipment, voltage quality, and energy loss in a distribution system. The main emphasis is given to the overloading of distribution transformers, primary feeders, and a substation transformer. The voltage quality of load points along the feeders and the system energy loss are also underlined. The load profile for uncoordinated EV charging is simulated by a Monte Carlo method with several deterministic and stochastic variables involved. To mitigate the overloading of the system components, a coordinated charging (also known as smart charging) model formulated as a linear programming problem is proposed with the objective of maximizing the total amount of energy consumption by EVs and the sum of all individual final states of charge (SoCs), and minimizing the sum of the absolute deviation of individual SoCs from the overall average SoC. The optimization problem is subject to equipment capability loading and planning criteria constraints with low, medium, and high EV penetration levels. The voltage quality problem and energy loss are also analyzed by an unbalanced three-phased power flow model. A case study of a real and practical 115/22 kV distribution system of the Provincial Electricity Authority (PEA) with a 50 MVA substation transformer, 5 feeders, and 732 distribution transformers shows that the possibility of overloaded system components, voltage drops along the feeders, and the system energy loss can be identified in the uncoordinated charging scenario and offer the readiness for equipment replacement and network reinforcement planning. The proposed smart charging model allows the distribution system to accommodate more EVs by appropriately managing the power and the start times of charging for the individual EVs over the timeslots of a day. The study results confirm no violation of the system components and voltage regulation imposed by the system planning guidelines. In addition, the system peak demand and the system energy loss are significantly lower in the smart charging scenario, thus deferring investment upgrades, offering better asset utilization, and retaining network security and service quality. [ABSTRACT FROM AUTHOR]

Transportation is the leading source of logistics, and people contribute the significant primary emissions for increasing global warming, so we should avoid such a situation and concentrate on zero emissions. The range of the vehicle is the major problem (range-anxiety) in electric vehicles. Nowadays, researchers focus on range extender optimization since range extenders significantly improve the range of the vehicle with an auxiliary power unit (APU), which can prove consumer satisfaction. However, range extenders can recover energy by proposing the various configurations and systems of extended-range electric vehicles (EREV). Many industries and researchers summarize these efforts to optimize and find the solution for range-extenders. This paper reviewed the most suitable technologies for energy recovery and state-of-the-art topological constructions. The analysis of the study primarily concentrated on optimizing the cost of the systems, fewer emissions, and more fuel economy. The research is based on the range-extenders evaluation and summary for electric vehicles to guide automakers and researchers to invent other state-of-the-art configurations and topologies to obtain optimized EV ranges, namely, enhanced functionality and fewer emissions. A survey on specific extend-range electric vehicles and classification of EREV technologies, the performance of APUs, different control strategies of energy management in EREVs, and various existing difficulties are discussed and further enhanced for the future researcher EREV forecast offered by advanced technologies. [ABSTRACT FROM AUTHOR]

In this paper, the fault-tolerant lateral control problem is considered for autonomous electric vehicles with unknown parameters and actuator faults. In the real world, the physical characteristics of vehicles are time-varying, causing that the parameters are unknown and even piecewise. Besides, the actuators may encounter with intermittent actuator faults, which can destroy the system's performance. To deal with the piecewise unknown parameters and actuator faults, quadratic damping terms are designed in the controller and estimators are introduced to estimate those uncertainties. Then, by utilizing the backstepping technique, a novel adaptive fault-tolerant lateral control scheme is proposed, which guarantees that the tracking errors converge into a compact set. Simulation results are provided to validate the effectiveness and robustness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

As climate change has become a pressing concern, promoting electric vehicles' (EVs) usage has emerged as a popular response to the pollution caused by fossil-fuel automobiles. Locating charging stations in areas with an expanding charging infrastructure is crucial to the accessibility and future success of EVs. Nonetheless, suitable planning and deployment for EV fast-charging stations is one of the most critical determinants for large-scale EV adoption. Installing charging stations in existing fuel/gas stations in the city may be an effective way to persuade people to adopt EVs. In this paper, we aim to optimally locate a fast-charging station in an existing gas station in the real-world scenario of Aichi Prefecture, Japan. The purpose is to locate and size fast-charging stations in such ways that drivers can get access to these charging facilities within a rational driving range while considering real-world constraints. Furthermore, we include the investment cost and the EVs users' convenience cost. This problem is formulated by five integer linear programming using a weighted set covering models. The developed model determines where to locate charging stations as well as how many chargers should be installed in each charging station. The experimental results demonstrate that an appropriate location scheme can be obtained using the model M 5 . A computational experiment identifies the best infrastructure solutions for policymakers to consider in the context of growing environmental policies. [ABSTRACT FROM AUTHOR]

This paper proposes a new control method to improve transfer efficiency for dynamic wireless charging systems of electric vehicles (EVs). In the charging process, the equivalent impedance in the receiving side varies according to the state of charge of the battery system that reduces the transfer efficiency. An impedance control circuit is constructed on the receiving side to track the optimization impedance that transfer efficiency is maximized. However, the optimization impedance depends on the coupling coefficient. Therefore, in this paper, the coupling coefficient, which varies according to the EVs position, is online estimated only from the receiving side. A 1.5 kW dynamic wireless charging system prototype is built in the laboratory environment. In experiment results, the greatest transfer efficiency obtains 94.14% when the EVs move in aligned on the charging lane. Furthermore, the proposed control method improves by 6% on the transfer efficiency in the case of 30% misalignment when the transfer efficiency obtains 91%. [ABSTRACT FROM AUTHOR]

Summary: Lithium batteries are prone to swelling during overcharge, which leads to a huge external force in modules and poses a potential threat to electric vehicles. Therefore, it is extremely important to understand the swelling mechanism of lithium batteries in‐depth. In this paper, expansion force evolution during overcharge‐induced thermal runaway process for large format lithium‐ion battery with Li(Ni0.5Co0.2Mn0.3)O2 cathode is investigated. The rupture of the battery shell due to huge swelling is proved as the trigger of the thermal runaway event. In detail, the expansion force increases dramatically with the state of charge, which can be up to 3400 N. And it is a highly dynamic quantity with four obvious inflection points during overcharge, indicating a strong relationship with failure mechanisms. The inherent failure mechanism behind expansion force is investigated using both in‐situ and ex‐situ measurement techniques. The results revealing that the early abnormal expansion of the battery is caused by lithium dendrite and lithium plating. The rapid expansion mainly caused by CO, which is produced by incomplete electrolyte oxidation, leads to the rupture of the battery shell and thermal runaway events. This paper aims to unravel the mystery of lithium battery swelling during overcharge and provide valuable guidance to mitigate the overcharge risk induced by battery swelling. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the design computation and prototype model implementation of a Modified Mechanical Structure (MMS) Electric Bike (E-Bike). It is the technology that allows the vehicle to operate with the assistance of electrical energy. All conventional cars will be converted to electric vehicles (EVs) in the future. EVs will be affordable to all types of people, allowing them to fly comfortably and safely. As a result, this paper proposes a design estimate and model implementation of the MMS E-Bike with the smallest number of parts, lowest expense, and lightest weight possible. The most important parts of the designed MMS E-Bike are the battery, MMS, BLDC motor, and electronic commutator with their controller. Because of its adapted mechanical frame nature, the designed E-Bike is low in cost and weight, and it can also go up to 25 kmph. Furthermore, the rider will be able to ride the built MMS E-Bike without any pain to their bodies and should be able to sit comfortably during their journey. In comparison to the Ampere Angel and DMW Electra 20 E-Bikes, the performance of the developed model is tested under various operating conditions, as well as their battery backup. [ABSTRACT FROM AUTHOR]

Mileage anxiety is one of the most important factors that affect the driving experience due to the limitation of battery capacity. Robust and accurate prediction of the energy consumption of the journey of the electric vehicle can guide the driver to allocate the power rationally and relieve the anxiety of the mileage. Since vehicle sharing is the biggest application scenario of electric vehicles, it is a critical challenge in share mobility research area. In this paper, a travel energy consumption prediction model of electric vehicles is proposed in order to improve the mobility of shared cars and reduce the anxiety of drivers because they are worried about insufficient power. A recurrent neural network with attention mechanism and deep neural network is used to build the model. To validate the proposed model, a simulation is demonstrated based on both traffic and vehicle information. After the simulation, experimental results show that the proposed model has high prediction accuracy, and we also show through visualization how the model finds high relevant road segments of the road network while dealing with corresponding traffic state input. [ABSTRACT FROM AUTHOR]

Summary: In recent years, commercial building technology integrating renewable energy power generation has developed rapidly. However, the stochastic, intermittent renewable energy power generation and the error of load forecasting of a commercial building (CB) are easy to cause frequent fluctuation of tie‐line power, which brings challenges to the economic and safe operation of CB. In order to reduce the impact of renewable energy and load uncertainty on CB, and improve the power stabilization ability of CB, this paper proposes a multiple time‐scale (MTS) economic dispatch strategy for CB with building virtual energy storage system (VESS) and vehicle to building (V2B) under demand response mechanism. Firstly, taking CB as terminal load carriers, a new heat balance model based on the R‐C network is proposed, and the charging and discharging process of building VESS is analyzed. Secondly, by analyzing the travel trip law and charging and discharging behavior of EVs, an interactive control strategy between EVs and CB is proposed, that is, the control strategy of V2B. Then, Day‐ahead optimal economic dispatch mode with 1 hour as the time interval is established with the objective of minimizing the daily operation cost and considering the user temperature comfort factor of CB. Based on the Day‐ahead dispatching plan, the coordinated scheduling of VESS and V2B on the long time scale (15 minutes) and short time scale (1 minute) is used to establish a MTS rolling optimization model in Intra‐hour adjustment stage. This model is used to modify the Day‐ahead plan and stabilize the tie‐line power fluctuation. Finally, an example shows that the proposed Day‐ahead dispatching model can reduce the daily operating cost, and the Intra‐hour adjustment model can stabilize the tie‐line power fluctuation on MTS. [ABSTRACT FROM AUTHOR]

Summary: Global burgeoning pollution levels have led to massive efforts being made to electrify all modes of transportation in the coming decade. Most of the Electric Vehicles (EVs) in the automotive domain are powered by Lithium‐ion batteries (LIBs). Steady increase in number of EVs has led to generation of enormous amounts of data. Most of this data is related to the usage of LIBs and its parameters such as voltage, current, and temperature values. Recent data‐driven techniques possess the ability to extract meaningful insights from this data such as predicting the range of an Electric Vehicle or estimating State‐of‐Health of the batteries. Improvements in these models have been severely restricted due to limited access to proprietary experimental data and privacy concerns. This paper aims to introduce a Generative Adversarial Network based approach to produce high‐fidelity synthetic data to overcome the limitation of limited data. The synthetic data produced can be used for training neural networks to improve the accuracy of battery parameter predictions. The objectives of this research work are threefold; first we try to produce high‐fidelity synthetic time series data, secondly, heterogeneity in generated data is maintained, and thirdly, we test whether generated synthetic data enables improvements in accurate State‐of‐Charge estimation. [ABSTRACT FROM AUTHOR]

Rwanda is an East African Community (EAC) nation with rapid and remarkable past development in different sectors and still with the ambitious targets and plans to be achieved in the coming years ahead. The government plans universal electricity access by 2024 with 52% grid connection and 48% off-grid connections. In the transport sector, the concept of electric vehicles has been initiated and started in order to contribute to the UN Paris agreement and decrease the reliance of the transport sector on gaseous fuels which are one source of air pollutants leading to climate change, premature deaths, and morbidity associated with poor air quality. With higher electricity demand than the generation of the Rwandan power grid, different energy strategies are being developed with the overall objective to achieve the targeted universal energy access. In order to overcome the aforementioned issue, this paper proposes an integration of solar PV microgrids for the satisfaction of electric vehicle (EV) technology in Rwanda. Using HOMER Grid software, a managed EV charging station is simulated to a grid connected solar PV microgrid with storage in order to assess the economic impact. The results show that the proposed technology can lower the levelized cost (LCOE) of electricity by 139.7%. This study can contribute to further research developments in either different perspectives related to the integration of distributed energy resources (DERs) with electric vehicles or studies related to affordable and environment-energy systems. [ABSTRACT FROM AUTHOR]

This paper mainly studies the multiclass stochastic user equilibrium problem considering the market share of battery electric vehicles (BEVs) and random charging behavior (RCB) in a mixed transport network containing electric vehicles and gasoline vehicles (GVs). In order to analyze the random charging and path choice behaviors of BEV users and extract the differences in travel behaviors between BEV and GV users, an improved logit-based model, multilabel algorithm, and queuing theory are applied. The influencing factors of charging possibility mainly include the initial state of charge (SOC), the SOC at the beginning of charging, and the psychologically acceptable safe SOC threshold arriving at the destination. Diversity choices of user paths and charging locations will result in changes in queuing traffic and differences in queuing time. Conversely, different stations have different queuing dwell times, which will also affect the routing and charging locations for BEVs with RCB. The path-based method of successive averages (MSA) is adopted to solve the model. Through the simulation of the test network Sioux Falls, the equilibrium traffic flow and possible charging flow under different market shares and initial SOC are predicted, and the properties of the model and the feasibility of the algorithm are verified. [ABSTRACT FROM AUTHOR]

A coupled-inductor-based high step-up high step-down DC-DC converter with bidirectional capability suitable for electric vehicle (EV) applications is proposed in this paper. The voltage gain in both step-up and step-down operations is increased by a two-winding coupled inductor. The current ripple in the low-voltage port is low making it suitable for renewable energy sources. In addition, the low number of components causes the efficiency of the proposed converter to be competitive with similar topologies in the literature. Due to the use of only three bidirectional switches with low voltage stress, the cost and volume of the converter are reduced. To demonstrate the performance of the proposed bidirectional converter, an analysis of the operation modes in the steady state is presented. Also, a comparison between the suggested converter and other similar converters in the literature is provided. The results of these comparisons show that the proposed converter has a lower number of components and higher efficiency than the others. In addition, lower voltage stress and noticeable voltage gain in comparison with others are further results of these comparisons. Eventually, the experimental results in step-up and step-down operations are presented. In step-up operation, the experimental prototype is tested under fs = 50 kHz, VLow = 20 V, VHigh = 110 V, rated power = 120 W, with 94.60% efficiency. Furthermore, VHigh is considered 100 V with VLow = 33 V and 94.43% efficiency at 50 W rated power in step-down mode operation. [ABSTRACT FROM AUTHOR]

This paper demonstrates the use of an agent-based model (ABM) to study the mechanism of social influence in the diffusion of new energy vehicles. We introduce the "consumat" cognition model so that agents with different need satisfaction thresholds have different cognitive processes. In addition, supported by survey data, our study considers more characteristics of opinion leaders, such as innovative behavior, lower sensitivity to price influence, and a better ability to judge the product quality. Through the primary group and control group experiments, the simulations demonstrated that the opinion leaders play a significant role in the spread of information and the percentage of product adoption. The results indicate that targeting opinion leaders will be a valuable marketing strategy for new energy vehicles. It also provides some advice for assessing policies that promote sustainable behaviors. [ABSTRACT FROM AUTHOR]