World Electr. Veh. J., Volume 13, Issue 6 (June 2022) – 15 articles

The standardized driving cycles, which are used around the globe for the development and homologation of automobiles, consist of a series of speed points versus time, to represent typical driving conditions and to exclude the influence of a human driver. However, with respect to autonomous vehicles (AVs), the driving style is defined in driving algorithms as a characteristic of the vehicle. Therefore, driving style should be considered in driving cycles. In this research, using MATLAB/Simulink^® we developed the AVDC (Autonomous Vehicle Driving Cycle) Tool, which is capable of generating driving cycles based on driving style characteristics. The autonomous vehicles being investigated drive in a simulated environment along a straight road amongst other traffic vehicles, applying standard cycles to ensure the representativeness of generated autonomous cycles. The autonomous vehicle is piloted by adaptive cruise control (ACC) for car-following and free driving. Overtake logic decides whether passing will be attempted. Driving style is defined by four aspects—comfort, safety, swiftness, and economy—and determines the control parameters in the driving algorithm. The driving cycles generated by the AVDC Tool for a variety of driving styles show diverse characteristics, thus indicating the effective representation of various driving styles. Full article

This paper discusses the process of developing a novel and robust algorithm for an interior permanent magnet synchronous motor controller. This is necessary for the simplification of the setting of control parameters and maintaining the proper operation of the motor. A 3D torque lookup table was used in which two inputs were considered, i.e., accelerator movements and the motor rotational speed. These two inputs allowed the lookup table to generate a specified torque at any motor rotation, which was then fed-forward to the field-oriented control and space vector pulse width modulation algorithm. Modeling, simulation, and experimental tests were performed to design and validate the proposed controller. The experimental validation shows that the proposed controller worked as intended. This was indicated by its ability to control the motor to obtain a 7% higher torque output than in the simulation in the constant torque region. In the field-weakening region, the controller could make the motor reach a maximum speed of 5500 RPM. There was only an 8% difference compared to the simulation (6500 RPM). In terms of maximum power generated, the controller was able to match the simulation output with only a 5% difference. Full article

In view of the poor robustness and low accuracy in lane line identification based on digital image processing, this paper proposes a Markov random field intelligent algorithm based on machine learning to identify lane lines. The complete lane line identification steps are as follows: First, high-quality traffic scenario images are created by means of image preprocessing, which includes image graying, grayscale transformation, and the extraction of regions of interest (ROIs). Then, the images are modeled according to Markov random field theory, and model reasoning is performed based on the binary graph cut method. In the reasoning process, to achieve accurate lane line segmentation, i.e., the optimal solution of the model, the energy potential function is introduced to optimize the binary graph cut method. Finally, the lane line pixel label is marked according to the segmentation result. The experiments showed that the algorithm could accurately segment the lane line pixels after only 10 iterations, indicating that the identification method has good performance in both reasoning speed and identification accuracy, which takes account of both accuracy and real-time processing, and can meet the requirements of lane recognition for lightweight automatic driving systems. Full article

Electric vehicles (EVs) are an attractive alternative to conventional vehicles powered by internal combustion engines due to their low carbon footprint, low running cost, and higher energy efficiency. However, currently, they suffer from a lower range than conventional vehicles, which induces range anxiety for consumers. This work explores the EV parameters that strongly impact range using data-driven techniques. A detailed dataset of the technical specifications of commercial EV models manufactured from 2008 to 2021 was collected through web mining. Strong correlations were observed between range and battery capacity, top speed, curb weight, and acceleration (with Pearson coefficients of 0.90, 0.79, 0.70, and −0.84, respectively). Furthermore, regression algorithms were trained and tested on this dataset, with the lowest root-mean-squared error (RMSE) of 31.4 km obtained from support vector machine regression. With a mean EV range in the test set of 364.5 km, an RMSE of 31.4 km equates to around 8.6% accuracy. Additionally, simple linear relationships between EV range and EV model, battery, and performance parameters were determined that may be useful to EV consumers in calculating range. Full article

The real-time monitoring of the vertical force of the tire is the basis for ensuring the driving safety, handling stability, fuel economy and the riding comfort of the vehicle. An algorithm for the vertical force of the tire estimation based on the combination of intelligent tire technology and neural network theory is herein proposed. Firstly, the finite element model of the 205/55/R16 radial tire was established by ABAQUS and the validity of the finite element model was verified through static experiment and dynamic experiment. Secondly, the effects of inflation pressure, speed, load and tread wear on the tire contact patch length and the radial displacement at the virtual acceleration sensor were analyzed based on the finite element analysis method and control variable method. Finally, three kinds of the vertical force prediction algorithms based on the GA-BP neural network algorithm were established, and the network performance of each prediction model was tested. The results show that the vertical force prediction model with inflation pressure and the peak value of radial displacement as the characteristic input parameters has the highest prediction accuracy and the shortest calculation time. At the same time, the mathematical formula of the Model 3 was built. Full article

The exponential increase in the number of daily traveling vehicles has exacerbated global warming and environmental pollution issues. These problems directly threaten the continuity and quality of life on the planet. Several techniques and technologies have been used and developed to reduce fuel consumption and gas emissions of traveling vehicles over the road network. Here, we investigate some solutions that assist drivers to follow efficient driving tips during their trips. Advanced technologies of communications or vehicle manufacturing have enhanced traffic efficiency over road networks. In addition, several advisory systems have been proposed to recommend to drivers the most efficient speed, route, or other decisions to follow towards their targeted destinations. These recommendations are selected according to the real-time traffic distribution and the context of the road network. In this paper, different high fuel consumption scenarios are investigated over the road networks. Next, the details of efficient driving techniques that were proposed to tackle each case accordingly are reviewed and categorized for downtown and highway driving. Finally, a set of remarks and existing gaps are reported to researchers in this field. Full article

Electric powertrains in terms of battery electric vehicles (BEV) are considered to be very interesting for heavy truck transportations. The challenge is the need for very large onboard energy and batteries. Long-term fuel cells (FCs) are considered an interesting support system for heavy-duty BEV, but in the short term, a range extender (REX) is also interesting. A heavy-duty BEV with 970 kWh batteries installed can handle 27% of all possible missions for the Scania fleet considering daily recharging. The back-up range extender (BUREX) can expand this figure to 55% utilized 20 days per year. If a customer has a few very energy-demanding use cases each year and does not want to pay for all the batteries needed, the BUREX may be an especially good option. The BUREX reduces life-cycle CO₂ emissions, irrespective of the generation mix of the grid supplying the electricity used in vehicle manufacturing and battery charging. The BUREX reuse of the existing electric components of the BEV powertrain enables the installation of a 10% larger battery pack while being 80% less costly. The BUREX also adds redundancy to the BEV concept while recharging infrastructure improves, especially in rural places. These results indicate that the BUREX concept is a powerful short-term solution that could enable greater use of HD FC and BEV trucks while charging infrastructure and FC technologies gradually become more mature. Full article

In recent years, the throughput of cargo ports has increased rapidly. It is urgent to improve the operating efficiency of ports for its increasing demands. Both industry and academia have shown great interest in adopting 5G and Virtual Reality (VR) technologies to improve the efficiency and safety of industrial operations. However, such technologies have not been well explored in port operations. This paper proposes a 5G-based VR smart port to support intelligent management for five typical port operations. The overall architecture of the smart port and its key processes, major advantages, and limitations are discussed in this paper. An application of the proposed smart port system is demonstrated. The performance study based on network Quality of Service (QoS) and Quality of user Experience (QoE) proves the feasibility of the proposed architecture. The architecture supports efficient interaction in real-time, making comprehensive decisions, and staff training. The smart port architecture is suitable for tasks of high working intensity and can dramatically increase operational efficiency. Full article

Autonomous driving has been a topic of great interest in several areas, of which motor racing is no exception. The aim of this work is the autonomous control of the future Formula Student Lisboa vehicle, by implementing different strategies for control and path planning, with the purpose of minimising race lap times. These strategies are tested in simulation, using a realistic model of the prototype. The approach followed involves the decoupling of the lateral and longitudinal subsystems and obtaining the reference path using artificial potential fields, combined with a two-pass algorithm developed to generate a speed profile. In this way, a sub-optimal solution is reached that adequately portrays the expected behaviour of a human driver while respecting traction conditions. The process of generating the speed reference requires prior knowledge of the track layout. This assumption is then eased for obstacle avoidance, i.e., for a scenario where, in addition to the track limits, unknown static obstacles are present. A decoupled control approach is followed controlling each of the two subsystems individually. Full article

Electricity from renewable energy sources represents the most promising way to decarbonize energy systems. A grid connection of car Electricity Storage Systems (ESSs) represents an opportunity to tackle issues regarding electricity production non-programmability, only if sufficiently smart bi-directional Vehicle to Grid technologies (V2G) are widely implemented. Fully Bi-directional grid capabilities are still poor and must be increased, both physically and in terms of management and billing possibilities (in the so-called smart-grid paradigm). However, some V2G technologies may be already implemented in smaller individual contexts: so-called Vehicle to Home, V2H technologies. Starting from these considerations, within the frame of an Italian publicly funded research project, the authors categorized and described many possible application contexts and developed an open-source dynamic simulation (fully available under request for the scientific community) to identify most promising conditions. To this aim, they also synthetized and tested an effective energy optimization algorithm which will soon be implemented on a prototypal wireless V2H device, built by ENEA in cooperation with Cassino University, in Italy. The performances of the system were assessed evaluating electricity auto-consumption and home auto-feeding ratios. Simulations show that very relevant performances can be obtained, up to the values 69% for electricity auto-consumption and 82% of home auto-feeding. Full article

Transitioning the automotive industry from internal combustion engines (ICE) to electric vehicles (EV) has arisen as a critical challenge for global countries in achieving human resource development, owing to the need of new labor skills and replacement of automation systems. By applying a case study of Thailand’s automotive industry in response to this challenge, we aimed to plot out the trajectory of changes involved in the industry’s workforce during its transition with a central focus on Small and Medium-sized Enterprises (SMEs), in order to make sound suggestions to the government on how to build an effective policy for industry growth. An exploratory research design was adopted for the investigation. Both primary and secondary sources were collected. Interviews with key stakeholders, including official agencies, organizations in the business sector, and academic institutions, were conducted in a semi-structured format to collect as primary data. Meanwhile, secondary data were gathered from reports and other scholarly contributions that are relevant. All of the data that were collected were subjected to qualitative methods of analysis, including content and theme analysis. We found that the advances in technology and associated skills have posed challenges to the SMEs for the workforce relocations in terms of occupational shifts and skill development, with engineering demand potentially growing 10% while low labor skills declined by nearly 70%. We emphasized that without effective policies for establishing EV roadmap and coordination practices between public and private stakeholders, this transition would have a detrimental effect on the workforce development of SMEs, which would ultimately have a harmful impact on the automotive industry and the economy. Full article

In the study of autonomous obstacle avoidance of intelligent vehicles, the traditional artificial potential field method has the problem that the vehicle may fall into the local minima and lead to obstacle avoidance failure. Therefore, this paper improves the traditional potential field function. Based on the vehicle dynamics model, a strategy of jumping out of local minima based on smaller steering angles is proposed. By finding a smaller steering angle and setting a suitable jump out step length, the intelligent vehicle is enabled to jump out of the local minima. Simulation experiments by MATLAB show that the improved method can jump out of the local minima. By comparing the planned trajectories of the traditional method and the improved method in static and dynamic obstacles situations, the trajectory planned by the improved method is smooth and the curvature is smaller. The planned trajectory is tracked by the Carsim platform, and the test results show that the improved method reduces the front steering wheel angle while the intelligent vehicle satisfies the vehicle dynamics constraints during active obstacle avoidance, which verifies the stability and rationality of the improved method. Full article

With the dynamic development of renewable energies, energy storage devices, and electric vehicles, microgrids have been playing an increasingly vital role in smart power grids. Under the recent development of carbon neutralisation, microgrid systems containing multiple clean energy sources have become significant modules for energy conservation and emission reduction. Considering technological and environmental elements, we investigated the economic operation of microgrids with the integration of electric vehicles. In this paper, carbon trading mechanisms and operation scheduling strategies are analysed in the simulation models. Then, transaction costs and power balance are discussed. Industrial applications and policy implications are also presented. Full article

To evaluate the electromagnetic exposure safety of multiple wireless charging vehicles charging simultaneously and explore simple, effective electromagnetic protection methods, in this paper, car model, wireless charger model, and human body models were established by using COMSOL Multiphysics software. The influence of different relative positions of two wireless chargers on magnetic induction strength (B) in simulation space when two cars are charging simultaneously and the influence of different car body materials on a driver’s body surface B and internal induced electric field strength (E) were analyzed. The simulation results showed that the maximum value of B in the car when two wireless chargers work simultaneously was 1.30 times that when a single wireless charger works. The maximum value of simulation space B could be reduced by 24.23% by staggering the front and rear positions of the two wireless chargers. The use of aluminum alloy car body could reduce the maximum value of human body surface B of the driver in the car by 99.76%. The B of the driver’s body surface in the aluminum alloy car body combined with the staggering method of two chargers was the smallest, which was 42.86% of that when the two chargers were not staggered, and its electromagnetic exposure level was 1.33% of the International Commission on Non-Ionizing Radiation Protection public exposure limit. The results showed that the protection method is effective. Full article

The current increase of electric vehicles in Germany requires an adequately developed charging infrastructure. Large numbers of public and semi-public charging stations are necessary to ensure sufficient coverage. To make the installation worthwhile for the mostly private operators as well as public ones, a sufficient utilization is decisive. An essential factor for the degree of utilization is the placement of a charging station. Therefore, the initial site selection plays a critical role in the planning process. This paper proposes a charging station placement procedure based on real-world data on charging station utilization and places of common interest. In the first step, we correlate utilization rates of existing charging infrastructure with places of common interest such as restaurants, shops, bars and sports facilities. This allows us to estimate the untapped potential of unexploited areas across Germany in a second step. In the last step, we employ the resulting geographical extrapolation to derive two optimized expansion strategies based on the attractiveness of locations for electric vehicle charging. Full article