World Electr. Veh. J., Volume 13, Issue 10 (October 2022) – 21 articles

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging. Full article

In order to solve the problem that the three-phase current of the interior permanent magnet synchronous motor (IPMSM) contains the 11th and 13th current harmonics, a multiple synchronous rotating frame transformation (MSRFT)-based current harmonic suppression method is established. Firstly, the influencing factors of current harmonics in IPMSM vector control are analyzed, and the influence mechanism of the inverter’s dead-time effect and the permanent magnet flux linkage harmonics on the current harmonics is described. Secondly, a simple current harmonic extraction method is proposed by optimizing the traditional current harmonic extraction method based on MSRFTs. The proposed method achieves the accurate extraction of the current harmonic components. Thirdly, a harmonic voltage generation method is established and is combined with the proposed current harmonic extraction method to form a current harmonic suppression strategy. Finally, the feasibility and effectiveness of the proposed method are verified via MATLAB/Simulink simulations and experiments. The simulation and experimental results show that the proposed current harmonic extraction method can extract the current harmonic components accurately, and the proposed current harmonic suppression strategy can suppress the 11th and 13th current harmonics effectively. Full article

Plug-in hybrid electric vehicles (PHEVs) are equipped with more than one power source, providing additional degrees of freedom to meet the driver’s power demand. Therefore, the reasonable allocation of the power demand of each power source by the energy management strategy (EMS) to keep each power source operating in the efficiency zone is essential for improving fuel economy. This paper proposes a novel model-free EMS based on the soft actor-critic (SAC) algorithm with automatic entropy tuning to balance the optimization of energy efficiency with the adaptability of driving cycles. The maximum entropy framework is introduced into deep reinforcement learning-based energy management to improve the performance of exploring the internal combustion engine (ICE) as well as the electric motor (EM) efficiency interval. Specifically, the automatic entropy adjustment framework improves the adaptability to driving cycles. In addition, the simulation is verified by the data collected from the real vehicle. The results show that the introduction of automatic entropy adjustment can effectively improve vehicle equivalent fuel economy. Compared with traditional EMS, the proposed EMS can save energy by 4.37%. Moreover, it is able to adapt to different driving cycles and can keep the state of charge to the reference value. Full article

Power system control is an important issue with regard to power system safety, flexibility, and reliability. Over the years, various new power system control strategies have been explored, but the main disadvantage of these control strategies is their complexity in structures with respect to industrially applied PID controller. The present paper introduces a novel control strategy based on modified disturbance rejection control, which is a modification of the PID controller that not only preserves the simplicity of control design but also offers an effective control based on state observer-based control law. The proposed control strategy addresses some basic limitations of a PID controller and implements modified control law to remove these limitations. In order to prove the effective control of the proposed control strategy, a standard IEEE-39 bus power system integrated with renewable energy generations is developed, and a comparative analysis of the proposed controller is performed with respect to its ancestor controllers. The comparison is validated based on the system dynamic responses like frequency and tie-line power deviations when the power system is subjected to different disturbances. Furthermore, the power system is integrated with electric vehices (EVs) in vehicle-to-grid (V2G) mode in order to ascertain the effect of EVs when used in V2G mode. A novel study is carried out in which the optimal location of EVs in the power system is determined based on the enhancement in stability of the power system by EVs. The analyses are carried out in MATLAB Simulink software. Simulation reports reflect the optimal control action of the proposed controller with respect to already established strategies projected in the literature. Moreover, the results illustrate that EVs when connected in Area 1 and Area 3 of the power system, the system deviations and steady-state errors are much less as compared to the other cases. Full article

Lane detection is an essential module for the safe navigation of autonomous vehicles (AVs). Estimating the vehicle’s position and trajectory on the road is critical; however, several environmental variables can affect this task. State-of-the-art lane detection methods utilize convolutional neural networks (CNNs) as feature extractors to obtain relevant features through training using multiple kernel layers. It makes them vulnerable to any statistical change in the input data or noise affecting the spatial characteristics. In this paper, we compare six different CNN architectures to analyze the effect of various adverse conditions, including harsh weather, illumination variations, and shadows/occlusions, on lane detection. Among all the aforementioned adverse conditions, harsh weather in general and snowy night conditions particularly affect the performance by a large margin. The average detection accuracy of the networks decreased by 75.2%, and the root mean square error (RMSE) increased by 301.1%. Overall, the results show a noticeable drop in the networks’ accuracy for all adverse conditions because the features’ stochastic distributions change for each state. Full article

Central to the design of a direct current fast charging (DCFC) network is the question of how much energy a DCFC of a given power can supply to vehicles without users being forced to queue to charge. We define ‘utilization factor’ as the ratio of the energy delivered by a DCFC in a multi-day period to the maximum amount of energy it could deliver in period. Three and a half years of data from 12 DCFCs are examined, characterizing each charging event by both the utilization factor and the time lag since the termination of the previous charging event. Short lags between events are inferred to indicate queuing. To keep the fraction of would-be users who have to queue below 10%, the overall utilization of the DCFC must likewise be limited to 10% (or 7–17% in exceptionally heterogeneous or exceptionally homogeneous traffic patterns, respectively). E.g., a 100 kW DCFC should not be expected to deliver more than 240 kWh per day (100 kW × 24 h × 10%). Full article

In the first half of 2020, the coronavirus pandemic led to a drastic slump in the automotive industry, which was replaced by a surprisingly rapid growth in demand in the fall of 2020, and consequently led to the current shortages in microelectronic products. The prospect of an equally rapid economic recovery in the automotive industry is still threatened by supply bottlenecks for raw materials and key components, foremost for semiconductors. The so-called ‘semiconductor crises’ show exemplarily the overlapping of short-term supply chain disruptions with long-term structural features of the semiconductor industry. The combination of both is preventing that the supply situation in the automotive industry will improve quickly. First in this paper, the reasons for and respective effects of the crisis on the automotive industry are investigated by a quantitative market analysis. Second, specific strategic measures and options of automotive Original Equipment Manufacturers (OEM) and suppliers in Germany to cope with the situation and increase resilience in future supply chains are described by the means of qualitative expert interviews. By that, the paper helps in understanding the actual situation in the automotive industry, on the one hand, and contributes to the field of strategic supply chain and risk management with a focus on practical implications on the other hand. The results aim to support political stakeholders as well as small and medium sized enterprises to prepare themselves for future developments in the automotive market and changes in manufacturer–supplier relationships due to the transformation to new powertrain technologies and digitization. Full article

Currently, transitioning from fossil fuels to renewable sources of energy is needed, considering the impact of climate change on the globe. From this point of view, there is a need for development in several stages such as storage, transmission, and conversion of power. In this paper, we demonstrate a simulation of a hybrid energy storage system consisting of a battery and fuel cell in parallel operation. The novelty in the proposed system is the inclusion of an electrolyser along with a switching algorithm. The electrolyser consumes electricity to intrinsically produce hydrogen and store it in a tank. This implies that the system consumes electricity as input energy as opposed to hydrogen being the input fuel. The hydrogen produced by the electrolyser and stored in the tank is later utilised by the fuel cell to produce electricity to power the load when needed. Energy is, therefore, stored in the form of hydrogen. A battery of lower capacity is coupled with the fuel cell to handle transient loads. A parallel control algorithm is developed to switch on/off the charging and discharging cycle of the fuel cell and battery depending upon the connected load. Electrically equivalent circuits of a polymer electrolyte membrane electrolyser, polymer electrolyte membrane fuel cell, necessary hydrogen, oxygen, water tanks, and switching controller for the parallel operation were modelled with their respective mathematical equations in MATLAB^® Simulink^®. In this paper, we mainly focus on the modelling and simulation of the proposed system. The results showcase the simulated system’s mentioned advantages and compare its ability to handle loads to a battery-only system. Full article

Current source inverters (CSIs) use inductors as the major component to store energy. Compared with voltage source inverters (VSIs), CSIs have two advantages: 1. They can avoid the converter failure caused by capacitor failures, and 2. The load current does not increase with load mutation or even short-circuit failure. Therefore, CSIs can be a promising technology for EV charging. However, the waveforms, parameter design procedure, and power efficiency are still unclear. Therefore, it is unclear if CSIs are suitable for EV chargers. This article derives the closed-loop equations of the critical components, including the inductor current waveforms and the voltage ripple. Especially, the load over-voltage phenomenon is derived and verified to further ensure the reliability of the CSI system. Based on the derived equations and reliability requirements, the parameter design procedure is proposed. The power efficiency of both the Si- and SiC-based converters are derived and compared to remove the barrier of applying CSIs in EV chargers in the industry. Our simulations and experiments verify the correctness of the system modeling, over-voltage phenomenon, and power efficiency. All the simulation files (using PLECS) and calculation files (using MATLAB) are attached for the readers to verify and/or further modify. Full article

With the popularity of new energy vehicles, a large number of cities began to focus on the installation of electric vehicle charging piles. However, the existing intelligent charging piles have faced problems such as short supply, unreasonable distribution areas, and insufficient power supply. In response to these problems, this research proposes a recurrent neural network algorithm with an integrated firefly algorithm. Based on these two algorithms, a charging pile location and capacity model was established, and users’ travel habits were analyzed according to the model. In the simulation experiment, the PR curve analysis of the algorithm was carried out first. The analysis results showed that the AP value of the recurrent neural network algorithm combined with the firefly algorithm was increased from 0.9324 to 0.9972. In addition, it had higher accuracy and stability than before, which also verified the feasibility of the algorithm. Finally, through the model, the user’s travel habits were analyzed in detail. From the perspective of total demand, the charging demand of commercial centers was the highest, with a peak of about 537 kw, followed by 501 kw in office areas and then about 379 kw in parks. The kw charging demand in other areas was below 200 kw. The above results show that the recursive neural network can effectively determine the location and capacity of the charging pile, which is of great value to the development of transportation and new energy. Full article

The Norwegian charging infrastructure ecosystem was investigated from a user perspective by (1) developing knowledge of end-user experiences with public charging, (2) mapping BEV owners and future owner’s user-friendliness needs and the extent to which these needs are met, (3) pointing at potential user-friendliness improvements, (4) mapping the charging infrastructure ecosystem and business models, and (5) developing scenarios for the future system development and the impact on charging infrastructure user-friendliness. The article draws on the literature, a BEV (battery electric vehicle) and ICEV (internal combustion engine vehicle) owner survey, 15 BEV owner interviews, 21 charging infrastructure actor interviews, and open information sources on the charger actors. The unregulated charging system evolved into a complex web of actors that developed their own charging networks following their individually sensible business models, which in sum led to serious user-friendliness issues. To gain access to all chargers, users need to interact with up to 20–30 apps and 13 payment systems, which comes on top of different plug types, power levels, and charger interfaces. Some actors support roaming, while others oppose it. OEMs want users to interface with chargers through the navigation system. In the future, the system will become even more complex and less user friendly as more actors join unless, e.g., consolidation, regulation, or independent network orchestrators reduce the complexity. Full article

Rolling-element bearing fault diagnosis has some problems in the applied environment, such as low signal-to-noise ratio, weak feature extraction, low efficiency of feature learning and the complex structure of diagnosis models. A fault diagnosis method based on the comprehensive index method, complete ensemble empirical mode decomposition with adaptive noise independent component analysis (CEEMDANICA) and two-dimensional convolutional neural network (TDCNN) is proposed. Firstly, the original vibration signal of the bearing is preprocessed by CEEMDANICA, and the ICA components with different frequencies are obtained. Secondly, the ICA components are selected as the sample set by using multiscale permutation entropy, correlation coefficient, kurtosis and box dimension. Finally, the sample set are trained and tested by a DCNN model to realize the fault diagnosis of different bearing fault types. In order to verify the reliability of the method, a bearing fault vibration monitoring platform for an electric vehicle two-speed automatic transmission was built to collect the bearing vibration signals of multiple fault types under different working conditions. The diagnostic accuracy of several deep learning models is compared. The results show that the proposed method can realize the single and compound fault diagnosis of rolling-element bearings in an automatic transmission, with a high degree of accuracy. Full article

For the transformation of the mobility sector, small and light electric vehicles (LEV) show great promise, owing to their efficiency and low vehicle weight resulting in low energy consumption and lower greenhouse gas emissions per driven kilometer. The presented study focuses on the theoretical potential of substitutability of passenger car trips in Germany by varied LEVs based on the “Mobilität in Deutschland 2017” (“Mobility in Germany 2017”) dataset, for the year 2030. A detailed approach for identifying substitutable car trips was developed, reflecting age, trip purpose, number of passengers, and other decision criteria. By conducting a life cycle assessment of the considered LEVs and passenger cars, potential emission savings were analyzed. In the considered baseline scenario, it is found that emissions could be reduced by 44 % with 50% of passenger car mileage being substituted by LEVs. This study, thereby, gives way to further research on LEVs, and would urge both policy makers and general users to steer towards comprehensive measures that encourage a switch from cars to LEVs. Full article

The uptake of electric vehicles (EVs) may pose a challenge to power distribution networks (PDNs). While smart charging can be deployed to relieve stress on the grid, user-centric smart charging strategies could also exacerbate peak power demand due to synchronization when optimizing charging with regard to different objectives, such as charging costs. In this paper, we assess the charging demand emerging from a large fleet of EVs, with models for the decision to charge and distribution of the steady-state state-of-charge (SoC). These are applied to the municipality of Frederiksberg, Denmark, using data from the Danish national travel survey. Home and workplace charging are mapped to the urban 10 kV medium voltage PDN of Frederiksberg considering different charging behaviors and degrees of synchronization. Results indicate that the likelihood of severe congestion in the power distribution network is low and that it can be attributed to rare scenarios in which high synchronization is observed, particularly when maintaining the normal steady-state demand. Despite the low likelihood, preventive measures should be devised to mitigate such scenarios, especially if additional high-power consumers are connected. Full article

The improvement of both the stability and economy of the four in-wheel motor drive (4IWMD) electric vehicle under complex drive cycles is currently a difficult problem in this field. A torque distribution method with the comprehensive goals of optimal torque distribution and energy efficiency, considering economy through energy efficiency for the 4IWMD electric vehicle, is proposed in this paper. Each component of the 4IWMD electric vehicle is modelled. The dynamic programming (DP) control algorithm is utilized for torque distribution between the front and rear in-wheel motors to obtain optimal torque distribution and energy efficiency in the 4IWMD electric vehicle. The simulation is performed on a co-simulation platform with the software of AVL Cruise and MATLAB/Simulink, considering a straight road. Compared to the fuzzy logic control algorithm, the simulation results are very promising, as the energy consumption of the electric vehicle was reduced by 22.68%, 20.73% and 21.84% under the WLTC, NEDC and customized IM240 driving cycle conditions, respectively, with the proposed DP control algorithm. The hardware-in-the loop (HIL) experimental results also indicate that the effectiveness of the proposed DP algorithm is verified under the NEDC, WLTC and IM240 driving cycles, when a straight road is considered. The proposed DP control algorithm not only reduces the vehicle energy consumption and guarantees the optimization of torque distribution, but also increases the driving range of the vehicle. Full article

The braking system is one of the most important components of a vehicle. In general, the brakes will generate heat due to the braking process. The heat generated must be released into the environment to maintain braking performance at optimal conditions. In extreme conditions, braking will fail. The braking system can be developed as a braking support system is a non-contact braking system. One form of the non-contact braking system is the eddy current brake (ECB). ECB is an electric braking system with the principle of eddy current. In the ECB, overheating will result in decreased performance. The approach that can be taken to determine braking performance during heat generation is the modeling process using FEM. This study uses FEM to analyze the heat generated during braking. In addition to using FEM, research was carried out using experiments as a comparison. Analysis of heat generation in braking is needed to determine whether braking with ECB can be a backup and its potential as a substitute for friction brakes. The results show that the ECB heat generation event that affects the temperature rise reduces the braking torque performance. Research indicates that when overheating occurs, braking performance will decrease by up to 10% when the disk surface temperature rises more than 20 °C. It shows the importance of managing heat that occurs in the ECB. Full article

In order to improve the satisfaction of users during the human–machine interaction with intelligent electric vehicles, this paper presents the human–machine interaction method of intelligent electric vehicles. Firstly, the principle of human–computer interaction of intelligent electric vehicles is analyzed, the application of interaction in big data visualization is expounded, and the cognitive mechanism of big data visualization interaction is designed. According to the above mechanism, the design the of information interface and the HUD interface is completed, and the interaction model is established. So far, the design of a human–computer interaction method of intelligent electric vehicles is completed. The experimental results show that the human–computer interaction response time of the design method is was only 5 ms, and the human-computer interaction satisfaction of the intelligent electric vehicle can reach 99%, which has certain application value. Full article

The need for electrical energy means batteries have a critical role in technological developments in the future. One of the most advanced types of batteries is the lithium-ion battery. The conventional charging system has the disadvantage of taking a relatively long time, so the battery temperature is high. Therefore, a charging method that can shorten the charging time and extend battery life is needed. Some contributions of the paper are the design and prototype of a buck-boost converter for dual-mode lithium-ion battery charging (buck and boost mode) and the implementation of the Multi-Step Constant Current Method (MSCC) algorithm with an optimal charging pattern (OPT) to perform fast charging under voltage, current limit, and temperature monitoring. The test results showed that the proposed charging system prototype has an accuracy of 99.93% for the voltage sensor and 98.86% for the current sensor, whereas the precision of voltage and current sensors are 98.60% and 99.34%, respectively. The proposed method took 45 min to charge the 2-series (2S) and 4-series (4S) batteries. Compared to the CCCV method, the charging time of the MSCC method was 18.18% faster. In terms of battery temperature, MSCC had a lower peak temperature compared to CCCV by 1.5% and 1.25% for 2S and 4S, respectively. Full article

The work presented focuses on a material efficient, modular design of a battery module for vehicle applications. Furthermore, the possibility of disassembly of individual components was considered. The constructive design focused on the combination of cast aluminum components, lightweight composites panels, and aluminum-foam phase-change material (PCM) composites. This led to an innovative battery module, which was finally implemented on a demonstrator level. The required cooling power of the module could be reduced by approx. 20% compared to conventional battery module setups. Furthermore, the constructive design of the module and the use of a “debonding-on-demand” technology enabled significantly faster disassembly. Die to the combination of these advantages and the possibility to give individual parts of the module a second life for new modules, the module shows a high resource efficiency as well as high CO₂ savings potential. Full article

In the context of the COVID-19 pandemic, an increasing number of people prefer individual single-track vehicles for urban transport. Long-range super-lightweight small electric vehicles are preferred due to the rising cost of electricity. It is difficult for new researchers and experts to obtain information on the current state of solutions in addressing the issues described within the Smart Cities platform. The research on the current state of the development of long-range super-lightweight small electric vehicles for intergenerational urban E-mobility using intelligent infrastructure within Smart Cities was carried out with the prospect of using the information learned in a pilot study. The study will be applied to resolving the traffic service of the Poruba city district within the statutory city of Ostrava in the Czech Republic. The main reason for choosing this urban district is the fact that it has the largest concentration of secondary schools and is the seat of the VŠB-Technical University of Ostrava. The project investigators see secondary and university students as the main target group of users of micro-mobility devices based on super-lightweight and small electric vehicles. Full article

The quantification and effective representation of safety risks for scenarios in structured road traffic environments of autonomous driving are currently being investigated in an active way. Based on artificial potential fields, a risk-field model for the traffic environment that considers the motion state of an obstacle vehicle is established, and an adaptive-grid risk-field method is proposed for autonomous vehicles. In this method, the traffic environment is meshed initially, and adaptive-grid division is performed using a quadtree grid-dividing strategy for root grids where the grid risk values are within the division interval, which allows for a more accurate quantification of traffic environment risk values. Adding adaptive-grid risk-field parameters to the cost function of the path-planning algorithm improves the accuracy of path safety risk assessment and completes the evaluation and selection of the optimal lane-change path. Simulation results show that the adaptive-grid risk-field established in this paper can effectively express the safety risks of the traffic environment, and the path-planning algorithm incorporating the adaptive-grid risk-field can obtain better paths for lane change compared with the traditional path-planning algorithm, while ensuring the safety of lane change. Full article