World Electr. Veh. J., Volume 13, Issue 7 (July 2022) – 22 articles

Environment perception is the foundation of the intelligent driving system and is a prerequisite for achieving path planning and vehicle control. Among them, obstacle detection is the key to environment perception. In order to solve the problems of difficult-to-distinguish adjacent obstacles and easy-to-split distant obstacles in the traditional obstacle detection algorithm, this study firstly designed a 3D point cloud data filtering algorithm, completed the point cloud data removal of vehicle body points and noise points, and designed the point cloud down-sampling method. Then a ground segmentation method based on the Ray Ground Filter algorithm was designed to solve the under-segmentation problem in ground segmentation, while ensuring real time. Furthermore, an improved DBSCAN (Density-Based Spatial Clustering of Application with Noise) clustering algorithm was proposed, and the L-shaped fitting method was used to complete the 3D bounding box fitting of the point cloud, thus solving the problems that it is difficult to distinguish adjacent obstacles at close distances caused by the fixed parameter thresholds and it is easy for obstacles at long distances to split into multiple obstacles; thus, the real-time performance of the algorithm was improved. Finally, a real vehicle test was conducted, and the test results show that the proposed obstacle detection algorithm in this paper has improved the accuracy by 6.1% and the real-time performance by 13.2% compared with the traditional algorithm. Full article

Charging of electric vehicles may cause stress on the electricity grid. Grid planners need clarity regarding likely grid loading when creating extensions. In this paper, we analyse the simultaneity factor (SF) or peak power of public electric vehicle charging stations with different recharging strategies. This contribution is the first of its kind in terms of data quantity and, therefore, representativeness. We found that the choice of charging strategy had a massive impact on the electricity grid. The current “naive” charging strategy of plugging in at full power and recharging until the battery is full cause limited stress. Price-optimised recharging strategies, in turn, create high power peaks. The SFs varied by strategy, particularly when using several connectors at once. Compared to the SF of a single connector in naive charging, the SF decreased by approximately 50% for groups of 10 connectors. For a set of 1000 connectors, the SF was between 10% and 20%. Price-optimised strategies showed a much slower decay where, in some cases, groups of 10 connectors still had an SF of 100%. For sets of 1000 connectors, the SF of price-optimised strategies was twice that of the naive strategy. Overall, we found that price optimisation did not reduce electricity purchase costs by much, especially compared to peak-related network expansion costs. Full article

During the charging process of the electric vehicle (EV), a spontaneous combustion accident may occur due to overheating of the battery, causing personal danger and property damage. To address the charging safety of EVs, this paper proposes a new hybrid EV charging process early warning protection method by combining Convolutional Long-Short Term Memory (ConvLSTM), the sliding window method, and the residual analysis method. The method is fully trained by extracting the deep features of EV charging data through ConvLSTM, eliminating the influence of erroneous transmission data through the sliding window method, and setting a reasonable warning threshold through the residual analysis method. The cross-validation results showed that among the four training sets, the ConvLSTM model of training, set three, had the highest prediction accuracy compared with the CNN, LSTM, BiLSTM and CNN-LSTM models, with RMSE reaching 0.029, MAPE reaching 11.37, and r² reaching 0.89. Training set one had the worst prediction in the four training sets, and after using it to set the warning threshold, the alarm task was completed five sampling points earlier. Therefore, the hybrid model can quickly complete the safety warning task, thereby ensuring the safety of EV charging. Full article

At present, the new energy vehicle industry is developing rapidly, but the relative lag in the development of its supporting infrastructure, especially charging stations, has become a bottleneck that restricts the development of the electric vehicle industry. In this paper, we propose a model for constructing a network of new energy vehicle charging facilities based on complex network theory and analyze the operation and the rationality of the layout of the new energy vehicle (NEV) charging stations in Wuhan and Hangzhou, respectively. The results show that the current layout of new energy vehicle charging stations in the city is relatively reasonable, but the allocation of charging pile resources is unreasonable. Our results of the virtual charging station network constructed by adding new charging station nodes show that the change in network structure helps to enhance the performance of the charging station system. Full article

Private cars are the most active and important incremental factor in the electric vehicle market and are expected to account for 80% of the new energy vehicle sales market by 2030. As the most common charging scenario for private cars, orderly charging in the community can optimize the distribution load curve by dynamically adjusting charging time and power of electric vehicles, so as to achieve peak-load shaving and turn electric vehicles into a friendly load to the distribution grid. However, after the traditional orderly charging strategy was released, the complexity of the operation on the user’s side was a heavy strike to the user’s willingness to participate in orderly charging, resulting in the quite low participation rate and insufficient demonstration of the characteristics of EV’s elastic power demand. To solve this issue, the paper proposes a senseless orderly charging strategy with user charging demand prediction and substation capacity constraint considered to minimize user charging fee or maximize service provider revenue. After a five-month practical application, the proposed strategy was found to effectively improve the user participation rate in orderly charging and to regulate electric vehicles as an elastic load to meet the grid demand. Full article

Greenhouse gas emissions, produced by various sectors, including transportation, are significantly impairing the environment and drive climate change. Battery electric vehicles are increasingly seen as a way to alleviate these problems, but they must be charged with electricity produced through environmentally friendly methods. This paper investigates a possible relationship between battery electric vehicles and solar photovoltaic panels using ENABLE.EU household survey data from ten European countries in autumn 2017–spring 2018. Based on the estimates from a recursive bivariate probit model, it is found that the probability that a household owns a battery electric vehicle increases significantly if said household owns solar photovoltaic panels. This suggests that a policy encouraging the home charging of battery electric vehicles using solar photovoltaic panels that includes an energy storage facility could speed up the transition to the use of these vehicles. Full article

It is well known that the classical topologies of Buck–Boost converters drain pulsating current from the power source. These pulsating currents entail acceleration of the aging rate of the fuel cell. In this paper, we are considering a Buck–Boost DC–DC converter topology featuring continuous input current. The converter interleaved structure ensures the substantial increase in power density compensating power losses related to the converter switching nature. The control objective is to enforce the DC-bus voltage to track its desired value despite load uncertainties and to ensure adequate current sharing between the different parallel modules of the fuel cell interleaved Buck–Boost converter (FC-IBBC). The point is that the internal voltage of the fuel cell is not accessible for measurement. Therefore, the state-feedback control, which consists of nonlinear control laws, is designed on the basis of a nonlinear model of the FC-IBBC system. We formally prove that the proposed controller meets its objectives, i.e., DC-bus voltage regulation and equal current sharing. The theoretical proof relies on the asymptotic stability analysis of the closed-loop system using Lyapunov stability tools. The theoretical results are well confirmed both by simulation, using MATLAB^®/Simulink^®, and by experimental tests using DS 1202 MicroLabBox. Full article

This paper focuses on the performance analysis, modeling, and control of permanent magnet synchronous generator (PMSG)-based wind energy conversion. This work analyzes controllers for the machine-side converter (MSC) and grid-side converter (GSC) and presents a new direct torque control (DTC) scheme based on a 12-sectors polygonal DTC for variable speed control of the PMSG. The proposed method solves the drawbacks faced by conventional six-sectors DTC control. The proposed method utilizes 12 sectors of 30° each compared to 60° in the conventional 6-sectors DTC. The 12-sectors technique was applied to voltages and flux vectors to increase the degrees of freedom for the selection of optimal vectors and, thus, reduce the torque ripple. This work analyzed the aforementioned DTC methods using MATLAB/Simulink, comparing the dynamic response of the proposed 12-sectors DTC with the conventional 6-sectors DTC control, and the results verified the effectiveness of the proposed DTC control. Full article

This study examines the potential role that an Electric Vehicle Battery (EVB) can play in Home Energy Management System (HEMS) based on a future development on the performance and costs of batteries. The value of EVB in an HEMS with different home connection settings and energy consumption/storage/generation capacities are investigated to advise the optimal future HEMS setups. Solar PV are considered as the residential renewal energy supply, which is the main resource of uncertainty of the system. A novel forecasting model is deployed which incorporates geographical information, solar radiation forecast and weather-related conditions into an exponential-based method to simulate day-ahead solar PV output. Optimal flows of energy and usage of storage (batteries) are then captured by a Stochastic Programming (SP) model and solved by CPLEX. Managerial insights and optimal designs of the HEMS are drawn based on the results obtained. Full article

Wireless power transfer (WPT) chargers are promising solutions for charging electric vehicles (EVs). Due to their advantages such as ease and safety of use, these chargers are increasingly replacing conductive ones. In this paper, we first provide a detailed analysis to illustrate the effect of varying parameters on the operation of the WPT charger. Secondly, we present the main design steps of the charger elements while respecting the recommendations of the SAEJ2954 standard in terms of operating frequency, efficiency and misalignments. Regarding the design of the ground-side and vehicle-side coils, we propose three different circular geometries whose parameters are determined using an iterative approach. The latter is compared with a finite element analysis performed under Ansys Maxwell software showing the convergence between theoretical calculations and the simulation results. Finally, an experimental prototype with a power of 500 W is realized. In addition, different test scenarios are performed to validate the proposed design approach. In this respect, an efficiency of 90% is obtained for a power of 500 W and a distance between coils of 125 mm. Moreover, the test of the charger in the most unfavorable operating case (misalignments of Δx = 70 mm, Δy = 10 mm and Δz = 150 mm) gives an efficiency of 83.5%, which remains above the limit of the SAEJ2954 standard. Full article

Wireless energy transfer (WET) is a revolutionary method that has the power to tackle the energy and longevity challenges in wireless sensor networks (WSN). This paper uses a mobile charger (MC) to discover the procedure of WET based on a wireless sensor network (WSN) for a periodic charging technique to maintain the network operational. The goal of this work is to lower overall system energy consumption and total distance traveled while increasing the mobile charger device vacation time ratio. Based on an analysis of total energy consumption, a new metaheuristic called mayfly algorithm (MA) is used to achieve energy savings. Instead of charging all nodes at the same time in each cycle, in our strategy, the mobile charger charges only energy-hungry nodes due to their levels of energy. In this strategy, when the first node reaches the calculated minimum energy, it notifies the base station (BS), which computes all nodes that fall under threshold energy and sends the MC to charge all of them to the maximum energy level in the same cycle. Mathematical results show that the mayfly algorithm can considerably decrease the charging device’s total energy consumption and distance traveled while maintaining performance because it can keep the network operational with less complexity than other schemes. Full article

The Levelized Cost of Driving (LCOD) calculates how much it costs to drive a vehicle, per kilometer, over the vehicle lifespan, and it is typically measured in USD/km. LCOD has been widely applied to assess and compare the performance of Electric Vehicles (EVs) and Internal Combustion engine Vehicles (ICVs). Unfortunately, there is no common methodology to establish how to compute LCOD. As authors use different criteria to determine which elements are relevant in LCOD, it is difficult to compare results. To overcome this issue, we re-introduce the LCOD concept, where the most relevant expenditures and incomes are included. This novel LCOD definition also incorporates key elements that are sometimes neglected, such as the second life of batteries in EVs, EV battery degradation models, and stochastic models, to characterize daily operation. The proposed methodology is applied to assess the cost of using EVs as taxis in the city of Quito, Ecuador. Full article

This research study involves designing and optimizing a sandwich structure based on an auxetic structure to protect the pouch battery system for electric vehicles undergoing ground impact load. The core of the sandwich structure is filled with the auxetic structure that has gone through optimization to maximize the specific energy absorbed. Its performance is analyzed with the non-linear finite element method. Five geometrical variables of the auxetic structures are analyzed using the analysis of variance and optimized using Taguchi’s method. The optimum control variables are double-U hierarchal (DUH), the cross-section’s thickness = 2 mm, the length of the cell = 10 mm, the width of the cell = 17 mm, and the bending height = 3 mm. The optimized geometries are then arranged into three different sandwich structure configurations. The core is filled with optimized DUH cells that have been enlarged to 200% in length, arranged in 11 × 11 × 1 cells, resulting in a total dimension and mass of 189 × 189 × 12 mm and 0.75 Kg. The optimized sandwich structure shows that the pouch battery cells can be protected very well from ground impact load with a maximum deformation of 1.92 mm, below the deformation threshold for battery failure. Full article

Against the background of carbon neutrality, the power dispatching operation mode has undergone great changes. It not only gradually realizes the coordinated control of source–grid–load–storage, but also strives to realize the multi-level coordination of the transmission network, distribution network and microgrid. Disorderly charging and discharging of large-scale electric vehicles (EVs) will have a great negative impact on the distribution network, but aggregating EVs and guiding them to charge and discharge in an orderly manner will play a positive role in delaying investment in the distribution network. Therefore, it is urgent to adopt an effective scheduling control strategy for electric vehicle charging and discharging. First, a variety of indexes were set to analyze the influence of EVs access on distribution network and the correlation between the indexes. Then, by defining the EVs penetration rate and the load simultaneous rate, the charging load planning of EVs was calculated. Based on the simultaneous load rate, the regional electricity load plan was calculated, and a configuration model of distribution capacity suitable for charging loads in different regions was constructed. Finally, an optimal dispatch model for electric vehicles considering the safety of distribution network was proposed and the distribution transformer capacity allocation model was used as the optimization target constraint. Compared with most optimized dispatch models used to maximize aggregator revenues and reduce peak-to-valley differences and load fluctuations in distribution networks, this model could effectively reduce unnecessary investment while meeting regional distribution transformer needs and maintaining distribution network security. Taking the improved IEEE 34-bus systems as an example, the simulation analysis was carried out and the investment demand of distribution network under the condition of disordered and orderly charge and discharge was compared. The results show that the proposed optimal scheduling method can effectively reduce the load fluctuation of distribution network, keep the voltage offset within the allowable voltage deviation range, and can effectively delay the investment of distribution network. Full article

To achieve sustainable development in the road sector, the use of Electric Vehicles (EVs) appears as a positive response to transport emissions. Among the available technologies, dynamic charging seems to overcome the main weakness points of EVs, even if it requires that traditional roads (t-roads) be equipped with a system providing electricity for EVs. Thus, so-called electrified roads (e-roads) must be implemented into the urban road networks. Since it is not possible to electrify all roads simultaneously, and also to consider the demand needs of citizens, a selection criterion is essential. This research describes and develops a simple, self-explanatory, repeatable, and adaptable selection criterion aimed at helping city managers in prioritizing the roads of an urban network to be upgraded from t-road to e-road status. This method belongs to the so-called Multicriteria Spatial Decision Support Systems (MC-SDSS)—processes useful for solving spatial problems through the integration of multicriteria analysis (Fuzzy Analytic Hierarchy Process, F-AHP) with a geo-referenced data management and analysis tool (GIS). The developed algorithm is based on several criteria related to the infrastructure/transport, social and environmental areas. The result of the implemented method is a Feasibility Index (FI), able to prioritize the roads most eligible to be upgraded as e-roads, as also verified by its application on the urban area of Milan (Italy). Full article

The emerging popularity of Plug-in Electric Vehicles (PEVs) is creating new connections between the transportation and electric sectors, and PEV charging will bring new opportunities and challenges to a system of growing complexity. The electrification of transport will increase energy security, reduce carbon emissions, and improve local air quality. The actual expansion of electric vehicles (EVs) will depend on several factors: the evolution of autonomy, the acquisition price, the charging process and infrastructure, etc. This paper provides a guide for simulating the accumulative load profile for EV charging on a national level. The importance of all the parameters and variables involved (deterministic or stochastic) is investigated. Detailed tables and references concerning the distribution of values and the composition of the EV fleet are provided. A multivariate probabilistic model is developed considering the EV classes, weekly and seasonal driving patterns, charging strategies, battery capacities, consumption per EV, etc., leading to an accurate estimation of aggregated EV charging demand. Finally, a net-metering scheme is proposed, in which a photovoltaic (PV) system of a certain size will be able to provide the annual energy needs of the first 10,000 EVs in the Greek market. Full article

A three-phase boost-buck inverter (BBI) comprised of three identical boost-buck DC/DC converter modules is presented for an EV traction inverter application. It allows the step-up and/or step-down of the battery pack voltage according to the operating condition of the traction motor so that the overall performance can be optimized, which is essential for EVs with relatively low and varying battery voltages such as a hybrid EV or fuel-cell EV. It also features low switching losses, low harmonic distortion in the output current, and reduced common-mode voltage and/or current. A detailed analysis and performance evaluation of the BBI compared to the conventional technology demonstrate its feasibility in EV traction applications. The functionality and performance of the boost-buck inverter are verified with simulation and experimental results. Full article

For the neutral point clamped (NPC) three-level converter fed permanent magnet synchronous motor (PMSM) system, the performance of the conventional model predictive current control (MPCC) algorithm will be deteriorated if the amplitude of the neutral point potential (NPP) is large. Additionally, the adjustment process of the weighted coefficients of the conventional MPCC algorithm is complex because of numerous control terms in the cost function. To solve the above issues, an improved MPCC algorithm is proposed in this paper. Firstly, Newtonian iteration is used to transfer the stator current into stator voltage in the cost function. Then, the NPP term in the conventional cost function can be eliminated by introducing the partition control of the NP potential, which also eliminates the whole adjustment process of weighting coefficients. Finally, based on the amplitude of the NPP, the amplitude and phase angle of medium and small vectors are modified to improve the control performance of the torque and flux. Experimental results show that the fluctuation of the neutral point potential can be suppressed rapidly. Meanwhile, the performance of the torque, flux and current are also improved compared with the conventional MPCC. Full article

The control techniques of the brushless DC (BLDC) motor have gained a large amount of interest in recent years, with their use being implemented in order to achieve a high-performance drive, including quick transient response and high-quality waveforms at the steady state. This paper provides a comparative study between three control schemes of BLDC motors: the direct power control scheme using a finite control set model predictive control (FCS-MPC) approach, the stator current controlled scheme using an FCS-MPC approach, and the stator current controlled scheme using ON–OFF hysteresis current controllers. The three systems were studied and investigated under the same operating conditions. The comparative study included investigating the performance of the BLDC drive in both steady state and transient operations. Qualitative and quantitative analyses were performed on the results obtained with each control scheme. The obtained results demonstrate the validity and effectiveness of the three investigated schemes in controlling the motor speed to the desired value under sudden load changes and achieving satisfactory quick transient responses. However, the results indicate the superiority of the direct power control scheme using an FCS-MPC approach over the others in terms of its minimum torque ripple, lowest torque and speed pulsations, minimum active and reactive power ripples, and high-quality waveforms of the stator currents drawn by the motor with minimum THD. Full article

The data transmission in the vehicle network is easily interfered with by the outside world, which makes the security of data access difficult to provide in order to meet the actual needs. Therefore, a data security access control algorithm for an electric vehicle network based on blockchain technology is proposed. Using the double-chain architecture of an alliance chain-private chain in a blockchain, the distributed database of data communication for vehicle networking is constructed. In the process of vehicle network communication, the long short-term memory neural network is introduced to analyze the risk of communication behavior. A generator and discriminator are used to avoid communication risk behavior and realize secure access to data. The experimental results show that the success rate of data tampering is lower than 0.09 when this design method is used to deal with external intrusion, and it has high security. Full article

For plug-in hybrid electric vehicle (PHEV), using a hybrid energy storage system (HESS) instead of a single battery system can prolong the battery life and reduce the vehicle cost. To develop a PHEV with HESS, it is a key link to obtain the optimal size of the power supply and energy system that can meet the load requirements of a driving cycle. Since little effort has been dedicated to simultaneously performing the component sizing of PHEV and HESS, this paper proposes an approach based on the particle swarm optimization (PSO) algorithm to simultaneously determine the sizes of the engine, motor, battery and supercapacitor (SC) in a PHEV with HESS. The drivetrain cost is minimized in a different all-electric range (AER)—and depends on the battery type—while ensuring the driving performance requirements. In addition, the effects of the power system and drive cycle on the component sizes were analyzed and compared. The simulation results show that the cost of the PHEV drivetrain with the Ni-MH battery/SC HESS is reduced by up to 12.21% when compared to the drivetrain with the Li-ion battery/SC HESS. The drivetrain cost is reduced by 8.79% when compared to analysis-based optimization. The type of power supply system and drive cycle can significantly affect the optimization results. Full article

In this paper, a new concurrent yaw rate, sideslip angle, and longitudinal-velocity direct yaw moment control (DYC) strategy is proposed to improve the handling and stability of a rear-wheel drive student electric racing vehicle (EV) equipped with two independent motors. In order to control these three parameters concurrently, three control schemes are developed: three fuzzy controllers, three optimized PID controllers, and two fuzzy controllers for the yaw rate and sideslip angle with a PID for longitudinal velocity. The EV dynamic behavior for the different control schemes is compared by using a nonlinear model of the EV. This model consists of three main parts: vehicle dynamics, wheel dynamics, and tire dynamics. Simulations under a circular-path driving scenario show that the proposed fuzzy controllers can effectively reduce the consumed energy by 10%, track the desired speed and path, and enhance the vehicle’s behavior and stability while maneuvering by decreasing both the yaw rate and sideslip angle deviation. Full article