World Electric Vehicle Journal

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

Technologies for automated driving have advanced rapidly in recent years. Autonomous Vehicles (AVs) are one example of these recent technologies that deploy elements such as sensors or processing units to assist the driver. The effective integration of these vehicles into public roads depends on the drivers’ acceptance and how they adjust to this new generation of vehicles. This study investigated the acceptance and willingness of Jordanians to purchase AVs in Jordan. The ordinal logit model was deployed to determine the factors attributed to individual acceptance of AVs, such as the cost, security, privacy, along with the environmental impact, among others. The findings of a national survey conducted on 582 Jordanians to assess their perception about AVs revealed that Jordanians were generally interested in using AVs. However, their decisions about purchasing AVs are influenced by several factors. The results indicated that the cost of AVs greatly influences purchasing decisions, though if the cost is affordable, respondents were more interested in using AVs. The findings also revealed that there is a substantial relationship between the level of security and the likelihood of buying a self-driving car, as respondents are concerned about the level of security and privacy. Furthermore, the results revealed that environmentally friendly AVs are more likely to be owned compared to conventional vehicles. This study helps to enhance the current understanding by highlighting road user perceptions, with practical implications for practitioners. Full article

To overcome the problems existing in the practical application of traditional in-wheel motors used for electric vehicles, an integrated double rotor in-wheel motor was proposed, which can realize three drive modes to meet variable operating condition requirements of the vehicle. The process of switching between different drive modes affects the ride comfort of a vehicle. Taking the mode switching from a single inner motor drive to a dual-motor coupling drive as a research object, a dynamic modeling method of drive mode switching based on the switching system was proposed. According to the critical conditions of each state transition, the switching rules expressed by the segmental constant function were designed. At the engagement stage of electromagnetic clutch II, the torque coordination control strategy based on model predictive control (MPC) and control allocation was proposed. The simulation results show that the proposed strategy can effectively reduce the impact degree of a vehicle and the slipping-friction work of the clutch on the premise of ensuring the fast response of mode switching and the steady increase in vehicle speed. The switching quality of the mode-switching process is effectively improved. In addition, the drive mode switching control of the double rotor in-wheel motor prototype was tested, which proves its ability to operate in multi-drive mode. Full article

The anode recirculation mode is increasingly being adopted in today’s fuel cell systems. The recycling of hydrogen gas can effectively improve fuel utilization and the wider economy. However, using the purge strategy for the recirculation exhaust has a significant impact on the operational performance and economic efficiency of fuel cell systems.Experiments have shown that, when the purge interval increases from 6 s to 10 s, the recirculation pump power increases by about 20%, the nitrogen content in the exhaust gas increases, and the stack voltage shows a 10 V attenuation. The accumulation of nitrogen permeation in the anode circuit leads to the degradation of the fuel cell performance. Therefore, it is necessary to discharge the accumulated nitrogen through the purge valve in a timely manner. However, opening the exhaust valve with excessively high frequency can result in the unreacted hydrogen being discharged, which reduces the economic efficiency of the fuel cell. This paper is based on the principle of mass conservation and models each subsystem of the anode circuit in the recirculation pump mode of the fuel cell separately, including the proportional valve model, the hydrogen consumption model of the fuel cell, the nitrogen permeation model of the fuel cell, the neural network model of the circulating pump, and the purge valve model. These submodels are integrated to construct a nitrogen content observer for the hydrogen circuit, which can estimate the nitrogen content. The accuracy of the model is validated through experimental data. The estimation error is less than 5.5%. The nitrogen content in the anode circuit can be effectively estimated, providing a model reference for purge operations and improving hydrogen utilization. Full article

This paper investigates the loss and thermal characteristics of a three-phase 10 kW flux-switching permanent magnet (FSPM) machine, which is used as an integrated starter generator (ISG) for hybrid electric vehicles (HEVs). In this paper, an improved method considering both DC-bias component and minor hysteresis loops in iron flux-density distribution is proposed to calculate core loss more precisely. Then, a lumped parameter thermal network (LPTN) model is constructed to predict transient thermal behavior of the FSPM machine, which takes into consideration various losses as heat sources determined from predictions and experiments. Meanwhile, a simplified one-dimensional (1D) steady heat conduction (1D-SHC) model with two heat sources in cylindrical coordinates is also proposed to predict the thermal behavior. To verify the two methods above, transient and steady thermal analyses of the FSPM machine were performed by computational fluid dynamics (CFD) based on the losses mentioned above. Finally, the predicted results from both LPTN and 1D-SHC were verified by the experiments on a prototyped FSPM machine. Full article

This study determines the location of the minimum fast charging infrastructure for electric vehicles in the interurban route Riobamba–Quito in Ecuador using the methodology of the maximum distance between fast charges (MDFC). From the application of the method, a MDFC of 60 km and a basic highway charging infrastructure (BHCI) of six stations are obtained. The location is calculated by measuring the MDFC on the road using the desktop application Google Earth Pro. The proposal is validated by means of a mathematical model in Simulink, and two simulation scenarios are proposed. In the first one, the initial state of charge (SOC) is 95% and represents an EV with complete charging patterns, while in the second one, the initial SOC is 65% and represents incomplete charging patterns. The results indicate that for both simulation scenarios, the EV KIA SOUL 2016 can perform the specified round-trip routes using the proposed BHCI performing two fast charges of 20 min each way. In all cases, SOC values during operation remain above 20%. The results obtained allow us to establish that the proposed BHCI and its location are sufficient to allow the studied EV to complete the route. Full article

In order to solve the design problem of electric vehicle charging station distribution, based on the consideration of user and investor costs, this paper establishes a mixed integer model for charging station site selection based on game theory ideas. Among them, the user cost is determined by two indicators, namely, the cost of time for users to reach the charging station and the cost of time for users to wait in line, while the cost of the charging station is determined by the construction cost and the daily operation and maintenance cost. In the established model, the hierarchical analysis is used to minimize the combined cost of users and charging stations as the objective. In addition, an improved artificial bee colony algorithm is designed to solve the model. The improved algorithm adds a neighborhood search method and a feasible decoding scheme to the honey bee harvesting and tracking process, thus solving the problems of low search accuracy, poor convergence, and inability to directly calculate the mixed integer model of the original algorithm. Simulation results show that the improved artificial bee colony algorithm can effectively solve the mixed integer model and has higher search accuracy and convergence speed compared with the traditional method. By applying the algorithm to solve the siting model, the location and number of charging stations can be clearly planned, thus improving charging efficiency and reliability. Full article

This paper presents a proposed global optimal energy management strategy based on dynamic programming to enhance the energy consumption efficiency of an agricultural hybrid tractor that is equipped with a continuously variable transmission (CVT). Firstly, using a diesel-electric parallel agricultural hybrid tractor as the research object, a tractor-rotary tillage coupling dynamics model is constructed. Secondly, with the torque and speed of the motor, the torque and speed of the diesel engine, and the CVT speed ratio as the control variables, the state of charge (SOC) of the power battery as the state variable, and the goal of minimizing the total energy consumption of the whole machine, a global optimal energy management model based on dynamic programming is established. Finally, the field operation measured data is injected into the MATLAB simulation model, and experiments are carried out to verify the effectiveness of the energy management strategy. The results show that compared with the power-following energy management strategy, the proposed energy management strategy can make the diesel engine and electric motor work in the optimal area, and effectively reduce the total cost of energy consumption of the tractor during field operations. Under the condition of rotary tillage, the total cost of energy consumption is decreased by 16.89%. Full article

This paper analyzes methods of heating battery-driven electrical buses. The examined buses were two identical airport transport buses and two buses used in local transportation. To heat the first buses, an electrical water heater with a heating capacity of 20 kW, and for air conditioning, a rooftop air conditioner with a cooling capacity of 20.6 kW was installed. Climate control in the city buses was achieved using an R744 heat pump with a cooling capacity of 25 kW and a heating capacity between 14 and 21 kW, along with an electrical water heater with a capacity of 32 kW. During the project, the measurement data of the buses described above were taken for a full year and evaluated. The analysis of the measurement data brought insights into the specific electrical energy consumption of climate control in the buses in real operating conditions at outdoor temperatures between 2 °C and 36 °C. The results of this project additionally provide information on the optimization potential for the climate control of buses. Full article

Since electric vehicle (ELV) deployment can contribute to overall renewable energy sources, exploration of the heterogeneous influence factors (HIFs) affecting the willingness to accept ELVs can assist in the realization of sustainable development goals, particularly universal access to affordable energy for all. In this research, we explored the HIFs that influence the willingness of individuals to accept ELVs (WAELV) within an integrated decision-making (IDM) framework. We established the IDM conceptual framework through the incorporation of HIFs, notably including the environmental and health benefits of ELVs, knowledge about innovation, and the benefits regarding the built environment and creating a comprehensive structure. We analyzed data gathered through questionnaires from urban and peri-urban areas of the Shandong province (China) by employing the partial least square structural equation modeling technique, which is an appropriate tool for analyzing data measured on a Likert scale. The key findings were as follows. Firstly, the capital cost of ELVs was found to be a significant barrier to the WAELV of individuals. Secondly, among other factors, the societal aspect of ELVs and the environmental awareness aspect were drivers of the WAELV of individuals across all the data samples. However, benefits for the built environment, knowledge about innovation, and the environmental and health benefits of ELVs only positively drove the WAELV of individuals in the urban setting and for the overall sample. Thirdly, these three HIFs were identified as neutral factors in the peri-urban areas. Thus, a clear disparity was detected between the urban and peri-urban areas in terms of factors influencing the WAELV of individuals. Finally, the social aspect of ELVs was revealed as the strongest driver, while benefits for the built environment turned out to be the weakest factor. Based on these findings, some crucial policies are here extracted. Full article

The technology of large surface thermal management of pouch cells was studied and discussed by means of simulation. With thermal management, two cells are managed by a single cold plate. First, the pressure drop of the cold plate was simulated and the velocity distribution of the flow field was observed. The cooling performance of the cold plate to the cell was then studied, and the low-temperature heating ability of the cold plate to the cell was analyzed. Through analysis, it can be concluded that large surface thermal management technology can effectively and quickly control the temperature rise of the cell when the cell is charging and discharging and the temperature difference is less than 5 °C. Finally, a control strategy for low-temperature heating of the cell by the cold plate was proposed. Full article

This paper presents a novel scheme for fast fault detection and isolation (FDI) of position sensors based on signal processing and fault-tolerant control (FTC) for speed tracking of an electric vehicle (EV) propelled by a permanent magnet synchronous motor (PMSM). The fault is detected using a comparison algorithm between the measured and delayed rotor speed signals. The proposed scheme is more practical for diagnosing faults over a wide speed range since it does not use estimated speed value. In addition, to ensure continuous vehicle propulsion and to retain effective field-oriented control of the EV-PMSM in the event of a fault, a reconfiguration mechanism with back-EMF based position observer is employed. Rapid detection of position sensor failure is necessary for a seamless transition from sensored to sensorless control. Furthermore, a comparative analysis between sliding mode observer and flux observer for motor speed control is also presented in the context of EVs. The effectiveness of the position sensors for FDI and FTC is validated in the presence of typical vehicular disturbances, such as uneven road conditions and wind disturbance force. Finally, to validate the proposed approach experimentally in a real-world EV environment, this paper utilizes a scaled-down testbed with a TMS320F28379D DSP for the motor control of the EV. Full article

The state of health (SOH) of lithium-ion batteries (LIBs) needs to be accurately estimated to ensure the safety and stability of electric vehicles (EVs) while in operation. In this paper, we proposed a SOH estimation method based on Improved Aquila Optimizer (IAO) and Support Vector Regression (SVR) to achieve an accurate estimation of SOH. During the charging and discharging phases of the battery, we analyzed the trends in current, voltage, and energy, then extracted four features. We used the Kendall coefficient and gray relational grade to prove that features and SOH were highly correlated. On the other hand, IAO was used to optimize the penalty factor and kernel function parameters of the SVR to further improve the generalization and mapping ability. The proposed method was verified under different operating conditions using the CACLE battery data set; the results show that high accuracy can be achieved in SOH estimation via IAO–SVR, and the estimation error of mean MAE is remaining within 2%. Full article

The urban logistic sector is struggling with the following two problems: a particularly high level of traffic and air pollution. For those reasons, small electric vehicles are the perfect technical solution to cope sustainably with the increasing parcel volume in urban areas. To compensate the weight disadvantage of electric drive, a textile lightweight solution for the body of logistics vehicles is examined in this study. Therefore, various textiles and mounting solutions are examined and developed, as well as tested by multiple testing procedures. The result of this work is an ideal combination of a textile and suitable mounting method for a textile lightweight body for last—mile logistic vehicles. Full article

Electric vehicles are an important part of governments’ environmental policies, and therefore understanding the factors affecting their market share is very important. So, this research is designed to investigate the factors affecting electric vehicle adoption, considering the effects of the COVID-19 pandemic and sustainable development level. Effective factors have been investigated in three categories. One is the characteristics of electric vehicles; the other is the impact of the COVID-19 pandemic on demand for these vehicles; and finally, the impact of the level of sustainable development of countries on adopting electric vehicles. Our analysis method is based on grey econometric and grey regression methods. The results show that vehicle dimensions, battery warranty conditions, battery life, and charging facilities are effective factors in the field of vehicle characteristics that can increase the adoption of electric vehicles. Also, the analysis shows that the COVID-19 pandemic has reduced the adoption of electric vehicles. Finally, we have shown that the market share of electric vehicles is higher in countries with a higher sustainable development level because of better economic, social, and cultural infrastructures. Full article