World Electr. Veh. J., Volume 14, Issue 8 (August 2023) – 34 articles

Electric vehicles (EVs) have gained considerable attention in the last decade due to a paradigm shift in the transport sector driven by a higher awareness of environmental issues. While the importance of EVs cannot be overstated in the context of the global climate crisis, it does raise the question of whether certain countries or states are ready for their implementation. It is, therefore, necessary to analyze the impact of EVs in the power grids of these countries and states, considering factors such as line congestion and the eventual degradation of voltage profiles, to determine their hosting capacity and assess eventual expansion options. This paper proposes a representative prototype of Curaçao’s electrical system, which is used for assessing the impacts of EVs, allowing us to determine its hosting capacity. Curaçao is an island in the southern Caribbean Sea that uses fuel generators, wind energy, and solar energy to generate electricity. The idea behind this paper is to analyze the effects caused by an increase in EVs on Curaçao’s power grid and propose preventive measures to deal with such problems. Eight EV charging stations were considered, one DC super fast-charging station, three normal DC fast-charging stations, and four AC fast-charging stations. In 2022, there were an estimated 82,360 vehicles on the island. Using this information, this paper analyzes how many vehicles can be simultaneously connected to the grid before it no longer operates under acceptable values. The results showed that 3.5% of the total vehicles can be hosted by the grid. Nonetheless, this can be increased up to 4.5% with the reinforcement of a transmission line. Full article

Planning for the eventual electrification of transport in sub-Saharan Africa requires accurate simulation of its unique transport systems. The few studies that attempt to model electric minibus taxis—vehicles extensively used in sub-Saharan Africa’s public transport systems—vary greatly in their results. This paper analyses, compares and corrects the only two existing studies that project energy consumption of electric minibus taxis in the region. One of the studies projected an energy consumption of 0.39 kWh/km, while the other projected 0.93 kWh/km. This paper carefully analyses the simulation tools and models and cumulatively applies corrections from the literature and scientific analyses. As a result, the discrepancy between the two simulation tools is eliminated for a given data input and a final energy consumption is estimated in the range of 0.49–0.52 kWh/km, depending on the input data. Full article

In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles. Full article

Conducting excellent thermal management of a new electric vehicle motor drive system may enhance the operational efficiency of the motor drive and minimize its pollutant emissions and energy losses. As an important part of the motor thermal management system, it is necessary to improve the design of the drive motor for the fan. This paper presents the design of a 12s-10p permanent magnet brushless DC motor with a rated speed of 2200 rpm and a rated voltage of 12 V based on finite element analysis. At this rated speed, the maximum torque the motor can output is 1.80 N·m. Then, we calculated the loading capacity of the motor by parameterizing the resistance in the circuit. We have built a prototype based on the design results and built a test bench to test the loading capacity of the prototype. A comparison revealed that the error between the experimental and calculated results was small. Accordingly, it is believed that this work is capable of serving as a theoretical guide for the design and manufacture of automotive cooling fans in the future. Full article

In developed or first-world countries, hybrid cars are widely utilized and essential in technological development and reducing carbon emissions. Despite that, developing or third-world countries such as the Philippines have not yet fully adopted hybrid cars as a means of transportation. Hence, the Sustainability Theory of Planned Behavior (STPB) was developed and integrated with the UTAUT2 framework to predict the factors affecting the purchasing intentions of Filipino drivers toward hybrid cars. The study gathered 1048 valid responses using convenience and snowball sampling to holistically measure user acceptance through twelve latent variables. Machine Learning Algorithm (MLA) tools such as the Decision Tree (DT), Random Forest Classifier (RFC), and Deep Learning Neural Network (DLNN) were utilized to anticipate consumer behavior. The final results from RFC showed an accuracy of 94% and DLNN with an accuracy of 96.60%, which were able to prove the prediction of significant latent factors. Perceived Environmental Concerns (PENCs), Attitude (AT), Perceived Behavioral Control (PBC), and Performance Expectancy (PE) were observed to be the highest factors. This study is one of the first extensive studies utilizing the MLA approach to predict Filipino drivers’ tendency to acquire hybrid vehicles. The study’s results can be adapted by automakers or car companies for devising initiatives, tactics, and advertisements to promote the viability and utility of hybrid vehicles in the Philippines. Since all the factors were proven significant, future investigations can assess not only the behavioral component but also the sustainability aspect of an individual using the STPB framework. Full article

Quick-replacement battery technology has the advantages of eliminating mileage issues, extending battery life and reducing cost. The battery box plays an important role in carrying and protecting the on-board battery pack. However, fatigue life has not been well-established in changeable operating environments and driving conditions; hence, this knowledge gap is the focus of this paper. Here, SolidWorks (SolidWorks, Waltham, MA, USA) was used to establish a three-dimensional model of a quick-replacement battery box for electric vehicles, OptiStruct software (Altair, Detroit, MI, USA) was used for sweep frequency and random vibration analyses, and random vibration fatigue analysis was carried out using Ncode software (ANSYS, Pittsburgh, PA, USA). The quick-replacement battery box structure was then optimized according to the analysis results and lightweight targets. The results of sweep frequency and random vibration analyses showed that the maximum stress of a quick-replacement battery box is 39.058 Mpa. Compared with the allowable stress of the DC01 material at 150 Mpa, a significant margin is still present. The results of random vibration fatigue analysis showed that the minimum service life of a quick-replacement battery box is 5.740 × 10¹⁰, which meets the design requirements. Following optimization design, the maximum stress of a quick-replacement battery box was 71.197 Mpa, still meeting the allowable stress of the DC01 material at 150 Mpa and effectively alleviating the stress concentration. Furthermore, the optimized quick-replacement battery box was approximately 4 kg lighter. Therefore, optimization of the quick-replacement battery box is feasible and necessary. The results provide great theoretical and engineering significance for the design and optimization of quick-replacement battery boxes for electric vehicles. Full article

Accurately determining load model parameters is of the utmost importance for conducting power system simulation analysis and designing effective control strategies. Measurement-based approaches are commonly employed to identify load model parameters that closely reflect the actual operating conditions. However, these methods typically rely on iterative parameter search processes, which can be time-consuming, particularly when dealing with complex models. To address this challenge, this paper introduces a parameter identification method for the generalized synthetic load model (SLM) using the Extreme Learning Machine (ELM) technique, with the aim of enhancing computational efficiency. Furthermore, to achieve better alignment with load response curves, a Quantum Particle Swarm Optimization (QPSO) algorithm is adopted to train the ELM model. The proposed QPSO-ELM-based SLM parameter identification method is subsequently evaluated using a standard test system. To assess its effectiveness, parameter sensitivity analysis is performed, and simulation results are analyzed. The findings demonstrate that the proposed method yields favorable outcomes, offering improved computation efficiency in load model parameter identification tasks. Full article

This paper integrates the Internet of Things (IoT) technology and a smart grid to build an electric power IoT architecture and analyzes the intelligent sensing technology and wireless communication technology in this electric power IoT. Through the multi-channel data collection technology in power IoT technology and an orthogonal discrete multiwavelet transform algorithm of edge computing technology, the high-voltage transmission line status data of the smart grid are collected and processed. Then, the high-voltage transmission line condition monitoring system is designed using the node design of the high-voltage transmission line condition monitoring sensing network and the optimal sensor configuration for droop monitoring. The performance of the monitoring system is simulated and examined. The experimental results show that as the number of burst data nodes increases, the acceptance rate of the ODMT algorithm decreases from 99% to 98%, and the network survival time is over 2000. When the current exceeds 20% of the rated current, the overall measurement error is controlled at approx. 3%. At a height of 4 m, the ratio of the difference between the input voltage and output voltage sensing monitoring is approx. 5%. The error range of temperature sensing monitoring is within ±1 °C. The error rate of communication distance within 200 m is 0, and over 200 m, the error rate is approx. 7%. This system can monitor the transmission status of high-voltage lines very well. Full article

Secure communication between connected electric vehicles is critical for realizing the full potential of the Internet of Vehicles. However, the authentication and security of the information shared between vehicles remains a major challenge. In this work, we propose a blockchain-based data authentication algorithm to enable secure information sharing between electric vehicles. Our algorithm leverages the distributed ledger and consensus mechanism of blockchain technology to overcome limitations of traditional public key infrastructure schemes for large-scale vehicle networks. Each electric vehicle has a unique key pair and address on the blockchain network. Vehicles generate digital signatures using their private keys to share data, while recipients verify the signatures using corresponding public keys for authentication. Experimental results demonstrate that the proposed algorithm achieves high authentication success rates with acceptable latency and computation overhead. The algorithm provides benefits like decentralization, transparency and non-repudiation compared to existing approaches. Our work indicates the potential of blockchain to enhance security, trust and cooperation in Internet of Vehicles applications. Full article

The tendency towards thin insulation layers in traction battery systems presents new challenges regarding insulation quality and service life. Phase-resolved DC partial discharge diagnostics can help to identify defects. Furthermore, different root causes are characterized by different patterns. However, to industrialize the procedure, there is the need for an automatic pattern recognition system. This paper shows how methods from computer vision can be applied to DC partial discharge diagnostics. The derived system is self-learning, needs no tedious manual calibration, and can identify defects within a matter of seconds. Thus, the combination of computer vision and phase-resolved DC partial discharge diagnostics provides an industrializable system for detecting insulation faults and identifying their root causes. Full article

The significance of employing control strategies on a switched reluctance motor (SRM) is that they can reduce vibration noise and torque ripple. With the rapid development of digital system processors, predictive control (PC), as a modern control approach, is increasingly applied to enhance the dynamic performance and operational efficiency of SRMs. This review provides a comprehensive overview of the current state of research on PC strategies of SRMs and classifies PC technologies, such as generalized predictive control (GPC), hysteresis predictive control (HPC), deadbeat predictive control (DPC), and model predictive control (MPC). It summarizes the PC schemes from the aspects of predictive current control (PCC), predictive torque control (PTC), and other PC, and it discusses the current trends in technology development, as well as potential research directions. The insights presented herein aim to facilitate further investigations into predictive control techniques for SRM. Full article

With the development of the brake-by-wire system, more and more advanced driver assistance systems have been applied to automobiles. The brake-by-wire system can collect the driver’s braking intention through the displacement sensor and thus realize accurate braking by the motor. Based on the brake-by-wire system, we design an algorithm that can realize the vehicle Comfort Stop Technology (CST) in this paper. The CST can control the drop and rise of brake fluid pressure during the braking stop of the vehicle, and therefore reduce the sharp feeling of front and back pitching during the braking stop. Finally, through real car verification, the functional algorithm designed in this paper can improve the nodding feeling of the vehicle by reducing the deceleration of the vehicle during braking. Full article

In this study, the usability evaluation model is constructed for a co-pilot screen, and an analysis of the impact factors and optimization recommendations is made based on the evaluation results. Firstly, based on the usability design principles, interaction ease, interaction efficiency, visual comfort, driving safety, and their corresponding secondary indicators are defined, and the subjective weight of each indicator is determined using the analytic hierarchy process (AHP). Then, usability evaluation is carried out on four vehicles via vehicle driving simulated experiments and driving experiments on the road, and the objective weight of the indicators is determined using the CRITIC method. Finally, the usability evaluation model for co-pilot screens is established by applying the fuzzy comprehensive evaluation method. The results indicate that the overall usability comprehensive score of co-pilot screens is convergent and is mainly concentrated in the range of 50–65 points, with two vehicles having excellent affiliation and two vehicles having average affiliation. However, there is a great distance still to reach when compared to an excellent level. The usability evaluation model of co-pilot screens established in this article can quantify the HMI usability design of co-pilot screens. The results of this study are significant for the four tested vehicles in terms of guiding the usability design of co-pilot screens and in promoting the rapid iteration of co-pilot screen development. And a production vehicle that connects a driving simulation platform and the usability evaluation model can be used to test and evaluate more screen designs, interaction models, tasks, and infotainment applications, thus guiding further user experience designs. Full article

Because of emissions of exhaust gases, global warming is proceeding, and air pollution has increased. Thus, many countries are manufacturing eco-friendly vehicles, including electric vehicles. However, the range of electric vehicles is less than the range of internal combustion engine vehicles, so electric vehicle production is being disrupted. Thus, it is necessary to analyze the energy flow of electric vehicles. Therefore, to analyze energy flow of electric vehicles, this study suggested an energy flow structure first, then modeled the energy flow of the vehicle, dividing it into battery, inverter and motor, reduction gear and differential, and wheel parts. This study selected a test vehicle, drove in urban driving conditions and measured data. Then, this study calculated energy flow using MATLAB/SIMULINK in real time, and calculated and analyzed energy loss of each of the vehicle’s parts using the calculated data. Full article

This paper offers a novel dual-mode double stator wound rotor synchronous machine for variable speed applications. The proposed motor integrates the benefits of both the traditional wound rotor synchronous machine (WRSM) and brushless wound rotor synchronous machine (BL-WRSM). A constant torque can be attained in the maximum torque per ampere region by operating the proposed machine as a traditional WRSM in Mode I, and a constant power can be attained in the field-weakening region by operating it as a BL-WRSM in Mode II. Moreover, due to the dual-stator structure, the proposed machine exhibits improved performance in terms of high torque density as compared to the existing single stator BL-WRSM. By using a special stator winding arrangement to achieve the sub-harmonic component of the stator magnetomotive force, the brushless operation of the proposed machine is achieved. The additional sub-harmonic component induces a voltage in the harmonic winding placed on the rotor, which is then rectified and provided a DC current to field winding for brushless excitation. In order to validate the effectiveness of the proposed machine, a two-dimensional finite element analysis (FEA) is carried out. Full article

Aerodynamic characteristics are of great significance to the fuel economy and handling the stability of electric vehicles. The battery pack of electric vehicles has a huge structure and is usually arranged in the chassis area of the vehicle, which inevitably occupies the space at the bottom of the vehicle and affects the aerodynamic characteristics of the vehicle. To study the effect of the power battery pack installed in the chassis on the aerodynamics characteristics of the electric vehicle, the Computational Fluid Dynamics (CFD) method is used to study the flow and pressure fields of the SAE (Society of Automotive Engineers) hierarchical car model with battery packs mounted on chassis. The influence of the structure parameters of the battery pack on the automobile’s aerodynamics are also analyzed in detail. Based on the simulation results, it can be seen that the battery pack installed on the chassis has a great impact on the flow and pressure field at the bottom and tail of the vehicle, causing the drag coefficient and lift coefficient to increase. The structural parameters of the battery pack have contradictory effects on the drag and lift coefficients. As the length of the battery pack increases, the drag coefficient decreases, and the lift coefficient increases. As the battery pack width and height increase, the drag coefficient increases, and the lift coefficient decreases. The research results provide a reference for the optimization of the aerodynamic characteristics of electric vehicles with battery packs mounted on chassis. Full article

The demand for electric two-wheelers as an efficient and environmentally friendly means of transportation has increased due to the rapid expansion in urbanization and growing environmental sustainability concerns. The electric two-wheeler ecosystem requires an ideal design solution that strikes a balance between numerous features, technologies, and specifications to meet these changing needs. In this study, we present an evaluation framework to find the best design for electric two-wheelers using novel Pareto optimality and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approaches. These solutions are then thoroughly assessed against predetermined criteria, such as energy efficiency, manufacturing viability, and market viability. Additionally, we incorporate the TOPSIS approach to order the non-dominated options according to how closely they resemble the best design solution. The design solution that best meets the required objectives while minimizing departures from the ideal state is identified using this procedure. Combining these approaches, our framework provides a more dependable and rigorous tool for evaluating the electric two-wheeler ecosystem, empowering producers and policymakers to choose the best design options. The findings show that the Pareto optimality and TOPSIS approaches efficiently identify the non-dominated options and make it easier to choose an ideal design solution that is in line with customer preferences and environmental sustainability. The results of this study support the development of electric two-wheeler technology and promote the use of environmentally friendly transportation options, thereby promoting a more sustainable future. Full article

For terrain recognition needs during vehicle driving, this paper carries out terrain classification research based on vibration and image information. Twenty time-domain features and eight frequency-domain features of vibration signals that are highly correlated with terrain are selected, and principal component analysis (PCA) is used to reduce the dimensionality of the time-domain and frequency-domain features and retain the main information. Meanwhile, the texture features of the terrain images are extracted using the gray-level co-occurrence matrix (GLCM) technique, and the feature information of the vibration and images are fused in the feature layer. Then, the improved weighted K-nearest neighbor (WKNN) algorithm is used to achieve the terrain classification during the travel process of tracked robots. Finally, the experimental results verify that the proposed method improves the terrain classification accuracy of the tracked robot and provides a basis for improving the stable autonomous driving of tracked vehicles. Full article

The study of path-planning algorithms is crucial for an electric logistics vehicle to reach its target point quickly and safely. In light of this, this work suggests a novel path-planning technique based on the improved A-star (A*) fusion dynamic window approach (DWA). First, compared to the A* algorithm, the upgraded A* algorithm not only avoids the obstruction border but also removes unnecessary nodes and minimizes turning angles. Then, the DWA algorithm is fused with the enhanced A* algorithm to achieve dynamic obstacle avoidance. In addition to RVIZ of ROS, MATLAB simulates and verifies the upgraded A* algorithm and the A* fused DWA. The MATLAB simulation results demonstrate that the approach based on the enhanced A* algorithm combined with DWA not only shortens the path by 4.56% when compared to the A* algorithm but also smooths the path and has dynamic obstacle-avoidance capabilities. The path length is cut by 8.99% and the search time is cut by 16.26% when compared to the DWA. The findings demonstrate that the enhanced method in this study successfully addresses the issues that the A* algorithm’s path is not smooth, dynamic obstacle avoidance cannot be performed, and DWA cannot be both globally optimal. Full article

In recent years, permanent magnet synchronous motors (PMSMs) have been widely used in industry. Position-sensorless control has the advantages of reducing costs and improving reliability, and is becoming one of the most promising technologies for permanent magnet synchronous motors. This article reviews the main position-sensorless technologies. The advantages and disadvantages of model-based and saliency-based techniques were summarized and compared. Finally, the developmental trends and research directions of position-sensorless technology were discussed. Full article

Battery charging mode (CM) is a prevalent method of trans-shipping power to new energy vehicles (NEVs). Unfortunately, due to the limited capacity of batteries, typical NEVs can only travel for approximately 350 miles on a single charge and require hours to be recharged. Battery swapping mode (SM), as a novel alternative, can offer an ideal solution by exchanging depleted batteries for recharged ones at swapping stations in the middle of long trips, inevitably influencing potential consumers’ purchase behaviors. To examine the impact of SM and CM on consumers’ purchase intention, this paper examines a duopolistic market consisting of two new energy vehicle manufacturers (i.e., a NEV-SM manufacturer and a NEV-CM manufacturer), who adopt SM and CM to service consumers, respectively. Considering SM is characterized by low initial investment and ease of use for consumers, NEV-CM manufacturers capitalize on extended battery warranty services in response to rivals’ utilization of SM. Thereby, non-cooperative game models are formulated, in which government subsidies are taken into account. The optimal production decision for both the NEV-SM manufacturer and the NEV-CM manufacturer are analyzed under three scenarios: without extended warranty service, with extended warranty service, and with extended warranty service and subsidy. The results show that the two manufacturers’ market dominance relies on the ratio of the swapping station’s convenience to the extended warranty service and the valuation incremental rate. Additionally, we also find that the government subsidy can dramatically improve the NEV-SM manufacturer’s performance at the initial stage, but if the subsidy is insufficient in size at the subsequent stage, this will lead to policy failure and inefficiency in propelling the diffusion of swapping mode. Full article

The suspension system’s performance has a direct impact on the ride comfort, handling stability, and driving safety of heavy vehicles. The active air suspension of heavy vehicles can adjust the stiffness, damping parameters, and body height in real-time based on different road conditions. This adjustment ensures that the entire vehicle experiences a smooth ride while also making vehicle loading and unloading more labor efficient. Additionally, the active air suspension system enables the vehicle to achieve automation and intelligence. This study focused on a particular 6 × 4 heavy vehicle and designed an active air suspension system that aligns with the vehicle parameters of the heavy truck. Through the use of a fuzzy PID active control strategy, this study investigated and analyzed the height adjustment of the air spring. The results indicate that at a vehicle speed of 60 km/h on a class A road surface, the vehicle body’s vertical acceleration was reduced by 22.1%, and the dynamic travel of the suspension was reduced by 20.1%. This indicates that the fuzzy PID active air suspension system effectively reduces the vehicle’s vibrations and improves ride comfort. Full article

The aging process of lithium-ion batteries is an extremely complex process, and the prediction of the calendar life of the lithium-ion battery is important to further guide battery maintenance, extend the battery life and reduce the risk of battery use. In the uninterruptible power supply (UPS) system, the battery is in a floating state for a long time, so the aging of the battery is approximated by calendar aging, and its decay rate is slow and difficult to estimate accurately. This paper proposes a particle filtering-based algorithm for battery state-of-health (SOH) and remaining useful life (RUL) predictions. First, the calendar aging modeling for the batteries used in the UPS system for the Shanghai rail transportation energy storage power station is presented. Then, the particle filtering algorithm is employed for the SOH estimation and RUL prediction for the single-cell battery calendar aging model. Finally, the single-cell SOH and RUL estimation algorithm is expanded to the pack and group scales estimation. The experimental results indicate that the proposed method can achieve accurate SOH estimation and RUL prediction results. Full article

Naturalistic driving data (NDD) are valuable for testing autonomous driving systems under various driving conditions. Automatically identifying scenes from high-dimensional and unlabeled NDD remains a challenging task. This paper presents a novel approach for automatically identifying test scenarios for autonomous driving through deep unsupervised learning. Firstly, US DAS2 NDD are leveraged, and the selection of data variables representing the vehicle state and surrounding environment is conducted to formulate the segmentation criterion. The isolation forest (IF) algorithm is then employed to segment the data, yielding two distinct types of datasets: typical scenarios and extreme scenarios. Secondly, a one-dimensional residual convolutional autoencoder (1D-RCAE) is developed to extract scenario features from the two datasets. Compared to four other autoencoders, the 1D-RCAE can effectively extract crucial information from high-dimensional data with optimal feature extraction capability. Next, considering the varying importance of different features, an information entropy (IE)-optimized K-means algorithm is employed to cluster the features extracted using 1D-RCAE. Finally, statistical analysis is performed on the parameters of each cluster of scenarios to explore their distribution characteristics within each class, and four typical scenarios are identified along with five extreme scenarios. The proposed unsupervised framework, combining IF, 1D-RCAE, and IE-improved K-means algorithms, can automatically identify typical and extreme scenarios from NDD. These identified scenarios can then be applied to test the performance of autonomous driving systems, enriching the library of automated driving test scenarios. Full article

Mini-electric trucks have been widely used because of their high efficiency and zero emission with the rapid development of electronic commerce and express industry. So, improvement of dynamic performance and economy becomes crucial. The research in this field mainly focuses on passenger vehicles at present. However, most passenger vehicles are front−engine, front-drive vehicles; for mini trucks of front−engine and rear-drive, if the dynamics model of passenger vehicles is applied to mini−electric trucks, the dynamic parameters calculated will not be accurate. To enhance the accuracy of the dynamic parameters of mini-electric trucks, by combining the characteristics of mini trucks, the dynamic parameters are designed, and the types of drive motors and power batteries are selected, the dynamic model of mini−electric trucks is established. To improve the economy, control strategies, with five working modes switching, were established. On this basis, the simulation model is established, and the dynamic and economy simulation analysis and performance test were carried out. In applying the method, the error rate of maximum speed, acceleration time, and maximum gradient between simulation results and test results are 0.641% and 5.63% (15.328%), respectively, proving that the dynamic index has reached the expected value and endurance mileage is up to 295 Km under UDC conditions, increased by 5% after the vehicle control strategy was adopted. The results show that the parameter matching is reasonable and the vehicle control strategy is suitable for mini-electric trucks. The research method and conclusions can provide valuable references for the development of power systems for mini−electric trucks. Full article

Electric vehicles (EVs) are a promising solution to reduce carbon dioxide (CO₂) emissions, but this reduction depends on the fraction of renewable sources used to generate electricity. Wind energy is thus a vital candidate and has experienced a remarkable surge recently, establishing itself as a leading renewable power source worldwide. The research on Direct-Driven Permanent Magnet Synchronous Generator (PMSG)-based type 4 wind farms has indicated that the Phase-locked Loop (PLL) bandwidth significantly impacts Sub-Synchronous Resonance (SSR). However, the influence of PLL architecture on SSR remains unexplored and warrants investigation. Therefore, this paper investigates PLL architectural variations in PLL Loop Filter (LF) to understand their impact on SSR in type 4 wind farms. Specifically, an in-depth analysis of the Notch Filter (NF)-based enhanced PLL is conducted using eigenvalue analysis of the admittance model of a PMSG-based type 4 wind farm. The findings demonstrate that the NF-based enhanced PLL exhibits superior performance and improved passivity in the sub-synchronous frequency range, limiting the risk of SSR below 20 Hz. Additionally, Nyquist plots are employed to assess the impact on system stability resulting in increased stability margins. In the future, it is recommended to further investigate and optimize the PLL to mitigate SSR in wind farms. Full article

The majority of freight in Germany is carried out by trucks, resulting in emitting approximately 9% of Germany’s carbon dioxide equivalent emissions. In particular, long-distance truck journeys contribute significantly to these emissions. This paper aims to explore the conditions and impacts of introducing E-Trucks in Germany by utilizing a microscopic traffic simulation approach. Therefore, five different electrification levels of the long-distance truck traffic are evaluated. The demand-oriented charging network dimensioning aims for a realistic and implementable design and is based on an average charging power of 720 $\mathrm{k}$$\mathrm{W}$. Additionaly, it considers the necessary infrastructure requirements at service and rest areas next to the motorway. The results of this research provide valuable insights in terms of usage, requirements and demand. For an electrification level of 1%, 177 chargers at 173 charging sites must be implemented, while 1296 chargers and 457 charging sites must be built for an electrification level of 20%. The increase in the electrification level leads to more efficient occupancy of the charging facilities; i.e., an increase from 1% to 5% improves the average occupation time ratio per charger by approximately 130%. Of the total energy consumed, 65% is recharged en-route at public chargers. Between Monday and Thursday, each 1% electrification level increase requires 2.68 $\mathrm{G}$$\mathrm{W}$ $\mathrm{h}$ more energy for the public recharging network. Full article

In the context of achieving carbon peaking and carbon neutrality goals, focusing on coordinated efficiency in loss and carbon reduction, and promoting comprehensive green transformation of economic and social development are critical strategies. Line loss is an economic and technical indicator for measuring losses in a power system, and loss reduction is one of the important ways to achieve the carbon peaking and carbon neutrality goals in the power system. However, with the continuous increase in the power grid scale and the increasingly complex operation mode of the system, it is difficult to clearly quantify the carbon reduction benefits brought by system loss reduction. In order to synergize grid loss reduction and system carbon reduction, and generate economic and environmental benefits at the same time, this paper proposes a carbon market trading model that considers multi-layer reactive power compensation strategies. Based on the carbon emission flow model, a node carbon cost pricing is formed, and multi-layer reactive power compensation measures are set in the distribution network nodes to obtain an optimal loss reduction strategy, with the carbon market trading cost minimization as the objective. The effectiveness of the model is verified by simulating and analyzing four scenarios. Compared with the original system that does not consider carbon trading and reactive compensation, the model proposed in this paper can reduce losses by 20% and reduce carbon emissions by 5.7%. This paper is of great value for reactive power loss reduction management in distribution networks of a low-carbon background. Full article

With the increasing number of new energy sources connected to the grid, the unbalanced output of three-phase grid-connected inverters and the lack of no inertia and damping characteristics in the traditional microgrid control system will seriously affect the stability of voltage, frequency, and power angle for microgrids. This paper proposes a novel cascaded three-phase bridge inverter topology for the battery system used for the electric vehicle. Compared with traditional cascaded H-bridge inverters, the proposed multilevel inverter can achieve self-adaptive balance for three phases. The mathematical model of a cascaded three-phase bridge inverter is established in this paper. Based on the voltage and current equations of a multilevel inverter, a new modulation strategy named carrier phase-shifted-distributed pulse width modulation (CPSD-PWM) was developed, which is more suitable for cascaded three-phase bridge inverters. The harmonic analytic equations of carrier phase-shifted pulse width modulation (CPS-PWM) and CPSD-PWM are constructed by the double Fourier analysis method. Compared with the traditional PWM modulation strategy, the CPSD-PWM can reduce the output harmonics and improve the balance of the three-phase output, which can realize the three-phase adaptive balance in the cascaded three-phase bridge inverter. This paper develops a cascaded three-phase bridge multilevel power converter system based on the virtual synchronous generator (VSG) control strategy. The voltage and frequency of inverter output can be accurately controlled in both island mode and grid-connected mode through active power-frequency regulation and reactive power–voltage regulation, and the stability of primary frequency regulation for the multilevel microgrid inverter can be improved by collaborative optimization of virtual inertia and virtual damping. The CPSD-PWM modulation strategy and VSG control strategy are verified by the simulation results and experimental data for the cascaded three-phase bridge inverter. Full article

Battery state of charge prediction is one of the most essential state quantities of a battery management system. It is a prerequisite for the operation of a battery management system, but it becomes difficult to make an exact prediction of its state due to its characteristics, which cannot be measured directly. For the exact assessment of the Li-ion battery state of charge, the research proposes an extreme learning machine algorithm based on the alternating factor multiplier method with improved regularization. This method constructs a suitable online Li-ion battery state of charge prediction model using the alternating factor multiplier method in gradient form. The experiment demonstrates that the algorithm in the study has a reduction in the number of nodes in the implicit layer relative to the traditional extreme learning machine algorithm. The error fluctuations of the algorithm under two different excitation functions range from [−0.005, 0.005] and [0.082, 0.265]; The root mean square error of the data set in which the algorithm performs well is 1.9516 and 0.6157, respectively. The real simulation scenario created the predicted values of the state of charge in the realistic simulation scenario that fit the real value curve by 99.99%. The average and maximum errors of the proposed state of charge prediction model are the smallest compared to the long and short-term memory networks and gated cyclic units, 0.58% and 2.97%, respectively. The experiment demonstrates that the presented algorithm can reduce the computational burden while guaranteeing the state of charge model prediction. Full article