World Electric Vehicle Journal

Electric vehicles are able to provide immediate power through the vehicle-to-grid function, and they can adjust their charging power level when in the grid-to-vehicle mode. This allows them to provide ancillary services such as frequency control. Their batteries differ from conventional energy storage systems in that the owner’s energy requirement constraint must be met when the vehicles participate in a frequency control system. An optimization problem was defined by considering both the owner satisfaction and frequency control performance. The main contribution of the proposed paper, compared to the literature, are (1) to keep the total available energy stored in the batteries connected to a charging station in an optimal region that favors the frequency regulation capability of the station and the proposed QoS and (2) to consider the optimal region bounded by the efficiency thresholds of the charger to allow for maximum regulation power. The problem is expressed as a multi-criteria optimization problem with time-dependent references. The paper presents an energy management strategy for frequency control, describes a concept of an optimal time-dependent state of charge for electric vehicle charging demands, and considers the power dependence of the electric vehicle charger efficiency. Finally, the simulation results are presented via Matlab/Simulink to prove the effectiveness of the proposed algorithm. Full article

In the case of electric vehicle drives, faults in the drive system or in the traction inverter, which controls the vehicle drive unit, could lead to abrupt and unpredictable motion as well as acceleration of the vehicle. In terms of functional safety, the typically existing, permanent mechanical connection of the drive machine with the drive wheels poses a high safety risk. In particular, unintended motion of the vehicle from a standstill is especially critical due to the high risk of injury to traffic participants. To reduce this risk, appropriate monitoring algorithms can be applied for the rapid detection of faulty operation. A corresponding algorithm for fault detection in the electric drive of a vehicle is presented in this paper. In addition to the description of the algorithms, various driving maneuvers of an electric single-wheel drivetrain are simulated in fault-free and faulty operation on a hardware-in-the-loop test bench. The focus here is on the consideration of driving-off operations. Full article

In this paper, a new collision avoidance switching system is proposed to address the lack of adaptability of intelligent vehicles under different collision avoidance operating conditions. To ensure the rationality of the collision avoidance switching strategy for intelligent vehicles, the NGSIM road dataset is introduced to analyse the driver’s collision avoidance behaviour, and a two-layer fuzzy controller considering the overlap rate is established to design the collision avoidance switching strategy. In order to achieve real-time collision avoidance system activation, a lane change collision avoidance model based on MPC control is also developed. Finally, a simulation environment was created using Matlab/CarSim for simulation verification. The simulation results show that the collision avoidance switching system is more responsive and has a shorter start-up distance and is more adaptable to different driving conditions. Full article

The use of and interest in renewable energy have increased in recent years due to the environmental impact of the technologies currently used to generate electricity. Switched converters play a fundamental role in renewable energy systems. The main goal is to manipulate the output signal of the renewable energy source to meet the requirements of different loads. Therefore, the increase in research on renewable energy sources has resulted in an increase in studies on switched converters. However, many DC–DC converters can be used in a particular application, and there is no clear guidance on which converter to use. The choice of whether to use one converter over another is highly reliant on the expertise of the researcher. Two examples of DC–DC converters are the Sheppard–Taylor converter and the Cuk converter. In this work, a critical comparison is made between these converters. The parameters considered in this comparison are the number of components, gain, stress on parts, and others. The simulation results were obtained to evaluate the performance of the converters in different scenarios. Finally, we conclude that the only application for which the use of the Sheppard–Taylor converter is justified are those that require high specific power and power density. Full article

This paper investigates the trajectory tracking control issue of four-wheel independently actuated electric vehicles (FWIA EVs) with steer-by-wire devices concerning parameter uncertainties, external disturbances, input saturation, and vehicle sideslip angle not easily obtained. A robust $H_{\infty }$ dynamic output feedback control strategy is proposed for the integrated control of the steering motor current and direct yaw moment without using sideslip angle information to ensure the reference trajectory tracking and the improvement of handling performance and yaw stability. In the proposed integration control framework, the steer-by-wire device dynamic is involved in the polyhedral linear parameter-varying (LPV) trajectory tracking error model considering the time-varying longitudinal velocity, and the norm-bounded parameter uncertainties such as road adhesion coefficient, tire cornering stiffness, vehicle mass, and vehicle moment of inertia. With the help of the LPV model of all the states of the steer-by-wire FWIA EV, a dynamic output feedback trajectory tracking controller is designed using the robust $H_{\infty }$ technique. The controller gain matrices are obtained by solving the linear matrix inequalities. Finally, the high-fidelity full-vehicle model based on the CarSim-MATLAB/Simulink joint simulation platform verifies the robustness and advantages of the designed control strategy in the accelerated lane-change scenario. Full article

Three-dimensional object detection plays a vital role in the field of environment perception in autonomous driving, and its results are crucial for the subsequent processes. Pillar-based 3D object detection is a method to detect objects in 3D by dividing point cloud data into pillars and extracting features from each pillar. However, the current pillar-based 3D object-detection methods suffer from problems such as “under-segmentation” and false detections in overlapping and occluded scenes. To address these challenges, we propose an improved pillar-based 3D object-detection network with a two-stage pillar feature-encoding (Ts-PFE) module that considers both inter- and intra-relational features among and in the pillars. This novel approach enhances the model’s ability to identify the local structure and global distribution of the data, which improves the distinction between objects in occluded and overlapping scenes and ultimately reduces under-segmentation and false detection problems. Furthermore, we use the attention mechanism to improve the backbone and make it focus on important features. The proposed approach is evaluated on the KITTI dataset. The experimental results show that the detection accuracy of the proposed approach are significantly improved on the benchmarks of BEV and 3D. The improvement of AP for car, pedestrian, and cyclist 3D detection are 1.1%, 3.78%, and 2.23% over PointPillars. Full article

Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods. Full article

The outbreak of the COVID-19 pandemic has raised concerns about the use of public transport, with a surge in people considering personal car usage. However, owning private cars is costly and wasteful of resources. Electric car-sharing (ECS) is considered a safer and more private mode of transportation compared with public transportation. The COVID-19 pandemic has affected transport on transportation policies and travel willingness. What is the effect of the COVID-19 pandemic on CS travel, especially considering the safety issues during the COVID-19 pandemic? This study analyses the differences in the travel characteristics of private car owners and nonowners while using CS under the influence of the COVID-19 pandemic. Quantitative analysis during four months before and four months after the outbreak of the COVID-19 pandemic is conducted based on CS order data in Lanzhou, China. It was found that the number of CS orders fell by 55.8% during the COVID-19 pandemic. Travel behavior during the pandemic is different from that before the outbreak of the pandemic. Additionally, both private car owners and nonowners use CS while having differences in travel characteristics. Based on the results, business suggestions are introduced on the distribution of vehicles to help improve the profit of CS operators. Full article

The construction of community charging facilities and supporting distribution networks based on the predicted results of electric vehicle (EV) charging power in saturation year has resulted in a large initial idleness of the distribution network and a serious waste of assets. To solve this problem, this paper proposes a collaborative planning method for urban community charging facilities and distribution networks. First, based on the load density method and occupancy rate to predict the base electricity load in the community, the Bass model and charging probability are used to predict the community’s electric vehicle charging load. Taking the minimum annual construction and operation costs of the community distribution network as the objective function, the power supply topology of the distribution network for a new community is optimized by using Prim and single-parent genetic algorithms. Finally, the proposed scheme is verified by using the actual community data of a certain city in China as an analysis example, and the scheme of one-time planning of the distribution network and yearly construction of charging facilities is given. Full article

The growing use of DC/DC power converters has resulted in the requirement that their complex controllers be cheaper and smaller, thus using cost-effective implementations. For this purpose, it is necessary to decrease the computational burden in controller implementation to minimize the hardware requirements. This manuscript presents two methods for tuning an adaptive linear–quadratic–Gaussian voltage controller for a battery charger/discharger, implemented with a Sepic/Zeta converter, to work at any operating point. The first method is based on a lookup table to select, using the nearest method, both the state feedback vector and the observer gain vector, solving the Riccati’s differential equation offline for each practical operating point. The second method defines a polynomial function for each controller element that is based on the previous data corresponding to the system operating points. The adaptability of the two controllers to fixed voltage regulation and reference tracking was validated using simulations and experimental tests. The overshoot and settling time results were lower than 11% and 3.7 ms, which are in the same orders of magnitude of a control approach in which the equations are solved online. Likewise, three indices were evaluated: central processing unit capacity, cost, and performance. This evaluation confirms that the controller based on polynomial interpolation is the best option of the two examined methods due to the satisfactory balance between dynamic performance and cost. Despite the advantages of the controllers in being based on a lookup table and polynomial interpolation, the adaptive linear–quadratic–Gaussian has the benefit of not requiring an offline training campaign; however, the cost saving obtained with the lookup table controllers and polynomial interpolation controllers, due to the possible implementation on small-size microcontrollers with development tool simple and easy maintenance, will surely be desirable for a large number of deployed units, ensuring that those solutions are highly cost-effective. Full article

A hybrid heat-pipe cooling structure for the permanent magnet synchronous motor for electric vehicles was analyzed in this paper to effectively equalize the axial temperature rise and reduce the average temperature of each heat-generating component in the motor. A temperature field calculation method for the hybrid heat-pipe cooling permanent magnet synchronous motor based on an equivalent thermal network method was proposed in this paper to save computing resources in temperature field analyses for hybrid heat-pipe cooling motors. The results were verified against the simulation results of the computational fluid dynamics method under three common operating conditions for electric vehicles. The error was proven to be within 5%. The calculation time of the proposed method was compared with the computational fluid dynamics method, which demonstrated that the calculation time of the proposed method was within 194 s. Full article

The torque ripple of a switched reluctance motor (SRM) limits its application in electric vehicles. This paper proposes a DITC system for SRMs based on a novel multilevel converter (MLC), which aims at the problem that the torque ripple cannot be effectively suppressed for the conventional direct instantaneous torque control (DITC) of an SRM due to the limitation of the DC bus voltage in the asymmetric half-bridge converter (AHBC) and the single control strategy formulated in the commutation region. Based on the advantages of fast excitation and fast demagnetization for the proposed MLC and the torque distribution characteristics for each phase winding in the commutation region, a novel torque hysteresis control strategy is developed to improve the torque-following ability of the DITC and achieve the purpose of minimizing the torque ripple in the commutation region. In addition, multiobjective optimization control of the motor is carried out to improve the efficiency of the DITC system while suppressing the torque ripple. The effectiveness of the proposed SRM drive scheme is verified by experiment, which is of great significance for the application of SRMs in electric vehicles. Full article

A three-phase interior permanent magnet (IPM) machine with 18-stator-slots/12-rotor-poles and concentrated armature winding is commercially employed as a 10 kW integrated-starter-generator in a commercial hybrid electric vehicle. For comprehensive and fair evaluation, a pair of flux-switching permanent magnet (FSPM) brushless machines, namely one stator permanent magnet flux-switching (SPM-FS) machine, and one rotor permanent magnet flux-switching (RPM-FS) machine, are designed and compared under the same DC-link voltage and armature current density. Firstly, a SPM-FS machine is designed and compared with an IPM machine under the same torque requirement, and the performance indicates that they exhibit similar torque density; however, the former suffers from magnetic saturation and low utilization of permanent magnets (PMs). Thus, to eliminate significant stator iron saturation and improve the ratio of torque per PM mass, an RPM-machine is designed with the same overall volume of the IPM machine, where the PMs are moved from stator to rotor and a multi-objective optimization algorithm is applied in the machine optimization. Then, the electromagnetic performance of the three machines, considering end-effect, is compared, including air-gap flux density, torque ripple, overload capacity and flux-weakening ability. The predicted results indicate that the RPM-FS machine exhibits the best performance as a promising candidate for hybrid electric vehicles. Experimental results of both the IPM and SPM-FS machines are provided for validation. Full article

Hybrid electric vehicle technology (HEVT) is emerging as a reliable alternative to reduce the constraints of battery-only driven pure electric vehicles (EVs). HVET utilizes an electric motor as well as an internal combustion engine for its operation. These components would work on battery power and fossil fuels, respectively, as a source of energy for vehicle mobility. The power is delivered either from battery or fuel or both sources based on user requirements, road conditions, etc. HEVT uses three major propelling systems, namely, front-wheel drive (FWD), rear-wheel drive (RWD), and four-wheel drive (4WD). In these propelling systems, the 4WD model provides torque to all four wheels at the same time. It uses all four wheels to propel thereby offering better driving capability, better traction, and a strong grip on the surface. The 4WD-based HEVs comprise four architectures, namely, series, parallel, series-parallel, and complex. The literature focuses primarily on any one type of architecture for analysis in the context of component optimization, fuel reduction, and energy management. However, a focus on dynamic analysis that gives a real performance insight was not conducted, which is the main motivation for this paper. The proposed work provides an extensive critical performance analysis of all four 4WD architectures subjected to various dynamic operating conditions (continuous, pulse, and step-up accelerations). Under these conditions, various performance parameters such as speed (of vehicle, engine, and motor), power (of engine and battery), battery electrical losses, charge patterns, and fuel consumption are measured and compared. Further, the 4WD architecture performance is validated with FWD and RWD architectures. From MATLAB/Simulink-based evaluation, 4WD HEV architectures have shown superior performance in most of the cases when compared to FWD type and RWD type HEVs. Moreover, 4WD parallel HEV architecture has shown superior performance compared to 4WD series, 4WD series-parallel, and 4WD complex architectures. Full article

The performance of electric drive propulsion systems is often degraded by the high torque and flux ripples of an electric drive. Traditional control methods, such as proportional plus integral (PI) controllers and classical sliding mode controllers (SMCs), have shown good response and reduced torque ripple, but even lower ripple content at low voltage levels is required for its effective use in electric vehicle (EV) applications. In this paper, a new direct torque control (DTC) technique with space vector pulse width modulation (SVPWM) using fractional-order sliding mode control (FOSMC) for a two-level inverter (2LI) at constant switching frequency is proposed. The effectiveness of this proposed controller is compared with a conventional proportional-integral controller and a conventional sliding mode controller (SMC). Simulink models are developed using MATLAB version R2018a to analyze the robustness of the proposed control strategy. Simulation results demonstrate the advantage of the proposed controller in reducing the torque ripples at steady state with less settling time during sudden load change conditions. The proposed control technique also demonstrates better utilization of the stator flux through flux trajectory waveforms. Full article

Hybrid electric vehicles (HEVs) are used as a bridge during the transition to battery electric vehicles (BEVs) and to make energy consumption more efficient. The main problem in improving the efficiency of HEV energy consumption is torque management. In this study, a novel approach based on a nonlinear model predictive controller to solve the reference tracking and torque distribution problem is proposed. That is to say, in order to increase the efficiency of torque distribution, the weights of nonlinear model predictive control (NMPC) are trained with a Deep Q Network (DQN), and an event-triggered mechanism is designed with DQN to reduce the computational cost of MPC. The considered torque distribution problem varies according to the type and structure of the HEV. In this study, a parallel type 2 hybrid electric vehicle (P2 HEV) is considered and modeled via publicly shared passenger vehicle data of the engine, motor, high-voltage battery, transmission, clutch, differential, and wheel characteristics. NMPC is formulated so that the torque values remain within the physical limits of the engine, and the battery also operates at its physical limits. Namely, it is guaranteed that the battery works according to a certain state of charge (SOC) window and current limits. The state of health (SOH) of the battery is also considered in the optimization. The motor and engine efficiencies increase by 3.61% and 2.86%, respectively, with the proposed control structure, while the computational cost is reduced by 52.01% when utilizing the proposed event-triggering mechanism in the NMPC controller. Full article

This research investigated an alternate method for establishing the complex coefficients used in an electric vehicle’s mathematical energy consumption model. While other methods for creating electric vehicle energy models exist, it would be beneficial to have a rapid and inexpensive technique that remains accurate. Producing a mathematical energy model for such a vehicle has the challenge of determining its aerodynamic drag and rolling resistance coefficients. Currently and most often, expensive and tedious (time-consuming) methods are used to find these coefficients. Computational fluid dynamics (CFD), wind tunnel testing, and extensive mathematics make this objective challenging. For this work, a solar-powered electric vehicle provided the source data to derive its coefficients cost-effectively and efficiently. Data were collected during a road test of the solar electric vehicle from South Africa to Namibia stretching over 2000 km, in which all required energy variables were recorded. The collected data were used in an optimisation routine to establish the two coefficients by minimising the actual and modelled energy consumption error and controlling the driving speed. The outcome of the optimisation routine produced accurate coefficients with a final error value of less than 5% when applied to a validation data set not used during optimisation. With minor modifications, this method may be integrated into any electric vehicle computer system to autonomously identify its two hard-to-find coefficients while driving, which can be used to provide an accurate and realistic driving range estimation to the driver. Full article