World Electric Vehicle Journal

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users. Full article

Based on an investigation of how vehicle structural characteristics and system parameters influence the motion stability of high-speed steering intelligent vehicle platooning, a control strategy for ensuring motion stability is proposed. This strategy is based on a virtual articulated concept and is validated using both characteristic equation analysis and time domain analysis methods. To create a system, any two adjacent front and rear vehicles in the intelligent vehicle platooning are connected using a virtual articulated model constructed through the virtual structure method. A ten-degrees-of-freedom model of the intelligent vehicle platooning system is established, taking into account the nonlinearities of the tire and steering systems, utilizing the principles of the second Lagrange equation theory. The system damping ratio is determined through characteristic equation analysis, and the system’s dynamic critical speed is assessed by examining the relationship between the damping ratio and the motion stability of the intelligent vehicle platooning, serving as an indicator of system stability. By applying sensitivity analysis, control variable analysis, and time domain analysis methods, the influence of vehicle structural characteristics and system parameters on the system’s dynamic critical speed and motion stability under lateral disturbances within the intelligent vehicle platooning is thoroughly investigated, thereby validating the soundness of the proposed control strategy. Full article

This paper focuses on the obstacle avoidance trajectory planning problem for autonomous vehicles on structured roads. The objective is to design a trajectory planning algorithm that can ensure vehicle safety and comfort and satisfy the rationality of traffic regulations. This paper proposes a path and speed decoupled planning method for non-split vehicle trajectory planning on structured roads. Firstly, the path planning layer adopts the improved artificial potential field method. The obstacle-repulsive potential field, gravitational potential field, and fitting method of the traditional artificial potential field are improved. Secondly, the speed planning aspect is performed in the Frenet coordinate system. Speed planning is accomplished based on S-T graph construction and solving convex optimization problems. Finally, simulation and experimental verification are performed. The results show that the method proposed in this paper can significantly improve the safety and comfortable riding of the vehicle. Full article

Aiming to address the tracking accuracy and anti-rollover problem of the unmanned mining truck path tracking process under the complex unstructured road conditions in mining areas, a coordinated control strategy for path tracking and anti-rollover based on topology theory is proposed. Moreover, optimal equilibrium weights are assigned to path tracking control and anti-rollover control to ensure that the path tracking accuracy of the mining vehicle can be effectively improved in a safe and stable driving state. Regarding the path tracking problem, a lateral preview error model is established, and a path tracking controller is designed using LQR (linear quadratic regulator) control theory. In the design of the anti-rollover controller, the effects of understeer and trip-type rollover on the stability of the vehicle are taken into account, and the ideal transverse swing angular velocity and trip-type rollover evaluation index are introduced for controller design, which reduce the effects of the curves and roadway excitation on the mining truck and improve the rollover motion. Based on a joint simulation using Trucksim and Simulink and the construction of a hardware-in-the-loop simulation platform for verification, the single control strategy and coordinated control strategy are compared and analyzed. The final simulation results show that the tracking error, yaw velocity, and center of mass side deviation angle are optimized by 45%, 32.5%, and 20%, respectively. Therefore, the Extension theory-based coordinated controller satisfies the complex road conditions in the mining area and improves the tracking accuracy to the maximum extent while ensuring the safety and smoothness of vehicle driving and exhibiting good adaptability and robustness. Full article

To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new population initialization method is proposed. This method adopts a higher-quality random point generation strategy to generate random points centrally near the start and end of connecting lines. It combines the improved ACO algorithm to connect these random points quickly, thus greatly improving the quality of the initial population. Secondly, path smoothness constraints are proposed in the adaptive function. These constraints reduce the large-angle turns and non-essential turns, improving the smoothness of the generated path. The algorithm integrates the roulette and tournament methods in the selection stage to enhance the searching ability and prevent premature convergence. Additionally, the crossover stage introduces the edit distance and a two-layer crossover operation based on it to avoid ineffective crossover and improve convergence speed. In the mutation stage, we propose a new mutation method and introduce a three-stage mutation operation based on the idea of simulated annealing. This makes the mutation operation more effective and efficient. The three-stage mutation operation ensures that the mutated paths also have high weights, increases the diversity of the population, and avoids local optimality. Additionally, we added a deletion operation to eliminate redundant nodes in the paths and shorten them. The simulation software and experimental platform of ROS (Robot Operating System) demonstrate that the improved algorithm has better path search quality and faster convergence speed. This effectively prevents the algorithm from maturing prematurely and proves its effectiveness in solving the path planning problem of AGV (automated guided vehicle). Full article

This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications. Full article

Due to their reliability, design and analysis models with PID and PID fuzzy controllers for six-phase drive are being applied in new areas in various industries, including transportation. First, the development of any system with multiphase motors requires an elaborate model to define the control mode and controllers. The modeling of a control system for six-phase drive is based on its conventional d-q mathematical model and indirect field-oriented control. In this study, a Simulink six-phase drive model is designed with indirect field-oriented control and simulated with two types of fuzzy controller, PID and PID fuzzy. The simulation results are presented and analyzed; these results reflect the step response and performance at the provided speed reference law while keeping the load application at a constant speed. A fuzzy controller with 49 rules is considered and applied. With field-oriented control and a well-tuned PID controller, the six-phase electric drive has good step response specifications: a short settling time when starting without a load, no overshoot in the step response, small size, and a slight decrease in speed when loaded. The system employing a PID fuzzy controller shows slightly better results in response to the application of torque: the decrease in speed is eliminated more quickly. The simulation results were tabulated with the PID and with the results of previous research that rearranged some models to only operate in the classical controller mode. The simulation results indicate the robustness to disturbance of both the systems with six-phase drive and provide high-quality transient specifications at the provided reference speed. Full article

This article aims to realize the brushless operation of a wound rotor vernier machine (WRVM) by a third-harmonic field produced through stator auxiliary winding (X). In the conventional model, a third-harmonic current is generated by connecting a 4-pole armature and 12-pole excitation windings serially with a three-phase diode rectifier to develop a pulsating field in the airgap of a machine. However, in the proposed model, the ABC winding is supplied by a three-phase current source inverter, whereas the auxiliary winding (X) carries no current due to an open circuit. The fundamental MMF component developed in the machine airgap creates a four-pole stator field, while the third-harmonic MMF induces the harmonic current in the specialized rotor harmonic winding. The rotor on the other side contains the harmonic and the field windings connected through a full-bridge rectifier. The electromagnetic interaction of the stator and rotor fields generates torque. Due to the open-circuited winding pattern, the proposed machine results in a low torque ripple. A 2D model is designed using JMAG-Designer, and 2D field element analysis (FEA) is carried out to determine the output torque and machine’s efficiency. A comparative performance analysis of both the conventional and proposed topologies is discussed graphically. The quantitative analysis of the proposed topology shows better performance as compared to the recently developed third-harmonic-based brushless WRVM topology in terms of output torque and torque ripples. Full article

As a newly conceived vibration isolation element, an inerter can be implemented in different forms, and it is easily introduced in different vibration isolation scenarios. This paper focuses on a novel inerter device called an electromagnetic inerter (EMI), which combines a linear generator with a fluid inerter. Firstly, the structure and the working principle of the EMI is stated. Then, the parameter sensitivity of the fluid inerter is analyzed, and two parameters that have great influence on the inertance coefficient are obtained. Subsequently, the influence of the change of the external circuit on the output characteristics of the device is also discussed. This proves that the introduction of external circuits can simplify complex mechanical topologies. Finally, the topological structures of vehicle suspension are changed in the form of an EMI (including external circuit), and the dynamic performance of these structures in the typical vibration isolation system of a vehicle’s suspension is obtained. It is found that an L4 layout should be considered as the best suspension structure. Compared with traditional passive suspension, it not only ensures that its handling stability is not weakened, but also reduces the root mean square value of body acceleration and the peak of suspension work space by 4.56% and 11.62%, respectively. Full article

Incorporating battery electric buses into bus fleets faces three primary challenges: a BEB’s extended refuel time, the cost of charging, both by the consumer and the power provider, and large compute demands for planning methods. When BEBs charge, the additional demands on the grid may exceed hardware limitations, so power providers divide a consumer’s energy needs into separate meters even though doing so is expensive for both power providers and consumers. Prior work has developed a number of strategies for computing charge schedules for bus fleets; however, prior work has not worked to reduce costs by aggregating meters. Additionally, because many works use mixed integer linear programs, their compute needs make planning for commercial-sized bus fleets intractable. This work presents a multi-program approach to computing charge plans for electric bus fleets. The proposed method solves a series of subproblems where the solution to the charge problem becomes more refined with each problem, moving closer to the optimal schedule. The results demonstrate how runtimes are reduced by using intermediate subproblems to refine the bus charge solution so that the proposed method can be applied to large bus fleets of 100+ buses. Not only will we demonstrate that runtimes scale linearly with the number of buses but we will also show how the proposed method scales to large bus fleets of over 100 buses while managing the monthly cost of energy. Full article

This study aims to explore an intelligent vehicle trajectory tracking control method based on optimal control theory. Considering the limitations of existing control strategies in dealing with signal delays and communication lags, a control strategy combining an anthropomorphic forward-looking reference path and longitudinal velocity closure is proposed to improve the accuracy and stability of intelligent vehicle trajectory tracking. Firstly, according to the vehicle dynamic error tracking model, a linear quadratic regulator (LQR) transverse controller is designed based on the optimal control principle, and a feedforward control strategy is added to reduce the system steady-state error. Secondly, an anthropomorphic look-ahead prediction model is established to mimic human driving behavior to compensate for the signal lag. The double proportional–integral–derivative (DPID) control algorithm is used to track the longitudinal speed reference value. Finally, a joint simulation is conducted based on MatLab/Simulink2021b and CarSim2019.0 software, and the effectiveness of the control strategy proposed in this paper is verified by constructing a semi-physical experimental platform and carrying out a hardware-in-the-loop test. The simulation and test results show that the control strategy can significantly improve the accuracy and stability of vehicle path tracking, which provides a new idea for future intelligent vehicle control system design. Full article

Brushless DC machines have demonstrated significant advantages in electrical engineering by eliminating commutators and brushes. Every year, these machines increase their presence in transportation applications. In this sense, early fault identification in these systems, specifically in the electronic speed controllers, is relevant for correct device operation. In this context, the techniques reported in the literature for fault identification based on the Hilbert–Huang transform have shown efficiency in electrical systems. This manuscript proposes a novel technique for early fault identification in electronic speed controllers based on the Hilbert–Huang transform algorithm. Initially, currents from the device are captured with non-invasive sensors in a time window during motor operation. Subsequently, the signals are processed to obtain pertinent information about amplitudes and frequencies using the Hilbert–Huang transform, focusing on fundamental components. Then, estimated parameters are evaluated by computing the error between signals. The existing electrical norms of a balanced system are used to identify a healthy or damaged driver. Through amplitude and frequency error analysis between three-phase signals, early faults caused by system imbalances such as current increasing, torque reduction, and speed reduction are detected. The proposed technique is implemented through data acquisition devices at different voltage conditions and then physical signals are evaluated offline through several simulations in the Matlab environment. The method’s robustness against signal variations is highlighted, as each intrinsic mode function serves as a component representation of the signal and instantaneous frequency computation provides resilience against these variations. Two study cases are conducted in different conditions to validate this technique. The experimental results demonstrate the effectiveness of the proposed method in identifying early faults in brushless DC motor drivers. This study provides data from each power line within the electronic speed controller to detect early faults and extend different approaches, contributing to addressing early failures in speed controllers while expanding beyond the conventional focus on motor failure analysis. Full article

This paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like ADAS, edge computing, and cloud services to enhance road safety. Upon detection of misbehaviour, the MBR system utilizes data from interconnected parking facilities to identify the nearest safe location and provides navigation guidance to authorities and nearby vehicles. The paper presents a prototype of the MBR system, demonstrating its efficiency in detecting misbehaviours and coordinating swift responses. It also discusses the system’s limitations and societal implications, outlining future research directions, including integration with autonomous vehicle systems and variable speed limit technologies, to further improve road safety through proactive and context-aware response mechanisms. Full article

In order to reduce costs, compress space, and improve system stability under harsh operating conditions, the current vehicle motor drive systems often use position sensorless control methods. However, due to the introduction of filters and the hysteresis of position observers, the position sensorless control has the problem of deteriorating dynamic performance when vehicles start from zero and low speeds or their loads change. Therefore, this article focuses on the problem of position sensorless control applied by permanent magnet synchronous motors when vehicles start and operate at zero and low speed. Combined with high-frequency pulse vibration injection method, the relationship between the types of position observers, parameter selection, and position tracking performance is analyzed and compared. The short-pulse injection method is proposed to locate the initial position of the motor, overcoming the inherent 180° position deviation of pulse vibration high-frequency injections. Subsequently, the impact of the amplitude and frequency of the injected high-frequency signal on the performance of position estimation is focused on. Considering the adverse effects caused by the phase delay of the filter, a design method for filter parameters is proposed to achieve a smooth start and operation of the permanent magnet synchronous motor under position sensorless control. Finally, the rationality of the theoretical analysis and the effectiveness of the adopted methods are fully verified through simulation and experiments. Full article

In response to the issues of hybrid tractors’ energy management strategies, such as reliance on experience, difficulty in achieving optimal control, and incomplete analysis of typical operating conditions of tractors, an energy management strategy based on dynamic programming is proposed in combination with various typical operating conditions of tractors. This is aimed at providing a reference for the modeling and energy management strategies of series hybrid tractors. Taking the series hybrid tractor as the research object, the tractor dynamics models under three typical working conditions of plowing, rotary tillage, and transportation were established. With the minimum total fuel consumption of the tractor as the optimization target, the engine power as the control variable, and the state of charge of the power battery as the state variable, an energy management strategy based on a dynamic programming algorithm was established and simulation experiments were conducted. The simulation results show that, compared with the power-following energy management strategy, the energy management strategy based on the dynamic programming algorithm can reasonably control the operating state of the engine. Under the three typical working conditions of plowing, rotary tillage, and transportation, the battery SOC consumption increased by approximately 8.37%, 7.24%, and 0.77%, respectively, while the total fuel consumption decreased by approximately 25.28%, 21.54%, and 13.24%, respectively. Full article

This article examines a new powertrain system for electric vehicles based on the dual-star induction machine, presented as a promising option due to its significant advantages in terms of performance, energy efficiency, and reliability. This system could play a key role in the evolution of electro-mobility technology. The dual-star induction machine reduces electromagnetic torque fluctuations, limits current harmonics, improves power factor, and enables half-speed operation. Our study focuses on the control strategy and operation of the traction chain for electric vehicles propelled by the dual-star induction machine (DSIM) using Matlab software with version 2017. We integrate the battery as the main energy source, along with three-level static converters for energy conversion in the vehicle’s four operating quadrants. We have opted for sliding mode control, which has proven to be feasible and robust against external disturbances. Although we have modeled driver behavior, we consider it as an aspect of control, to which we add the driving profile to guide our evaluation of the control to be used for vehicle operation. The results of our study demonstrate the reliability and robustness of DSIM for electric vehicle motorization and speed control. Promoting this technology is essential to improve the overall performance and efficiency of electric vehicles, especially in traction and braking modes for energy recovery. This underscores the importance of DSIM in the sustainable development of the electric transportation system. Full article

In order to reduce energy consumption, improve driving mileage, and make vehicles adopt driver styles, research on automatic optimization of control strategy based on driver style is conducted in this paper. According to the structure of the powertrain, the vehicle control strategy is designed and a driver-style recognition model based on fuzzy recognition is added to the rule-based control strategy to improve the driver adaptation of the vehicle. In order to further improve the energy-saving effect of the strategy, the control strategy based on driver style is automatically optimized by the Isight optimization platform to make the strategy reach optimum. The test results show that the strategy based on driver style is able to adapt to different styles of drivers and the economy of the vehicle is improved by 2.06% compared with pre-optimization, which validates the effectiveness of the strategy. Full article

With more electric vehicles introduced in society, there is a need for the further implementation of charging infrastructure. Innovation in electromobility may result in new charging and discharging strategies, including concepts such as smart charging and vehicle-to-grid. This article provides an overview of vehicle charging and discharging innovations with a cable connection. A MATLAB/Simulink model is developed to show the difference between an electric vehicle with and without the vehicle-to-grid capabilities for electricity grid prices estimated for Sweden for three different electric vehicle user profiles and four different electric vehicle models. The result includes the state-of-charge values and price estimations for the different vehicles charged with or without a bidirectional power flow to and from the electric grid. The results show that there is a greater difference in state-of-charge values over the day investigated for the electric vehicles with vehicle-to-grid capabilities than for vehicles without vehicle-to-grid capabilities. The results indicate potential economic revenues from using vehicle-to-grid if there is a significant variation in electricity prices during different hours. Therefore, the vehicle owner can potentially receive money from selling electricity to the grid while also supporting the electric grid. The study provides insights into utilizing vehicle-to-grid in society and taking steps towards its implementation. Full article

The estimation of the state of charge (SOC) in lithium-ion batteries is a crucial aspect of battery management systems, serving as a key indicator of the remaining available capacity. However, the inherent process and measurement noises created during battery operation pose significant challenges to the accuracy of SOC estimation. These noises can lead to inaccuracies and uncertainties in assessing the battery’s condition, potentially affecting its overall performance and lifespan. To address this problem, we propose a second-order central difference particle filter (SCDPF) method. This method leverages the latest observation data to enhance the accuracy and noise adaptability of SOC estimation. By employing an improved importance density function, we generate optimized particles that better represent the battery’s dynamic behavior. To validate the effectiveness of our proposed algorithm, we conducted comprehensive comparisons at both 25 °C and 0 °C under the new European driving cycle condition. The results demonstrate that the SCDPF algorithm exhibits a high accuracy and rapid convergence speed, with a maximum error which never exceeds 1.30%. Additionally, we compared the SOC estimations with both Gaussian and non-Gaussian noise to assess the robustness of our proposed algorithm. Overall, this study presents a novel approach to enhancing SOC estimation in lithium-ion batteries, addressing the challenges posed by the process itself and measurement noises. Full article