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The operation management of electric Taxi fleets requires cooperative optimization of Charging and Dispatching. The challenge is to make real-time decisions about which is the optimal charging station or passenger for each vehicle in the fleet. With the rapid advancement of Vehicle Internet of Things (VIOT) technologies, the aforementioned challenge can be readily addressed by leveraging big data analytics and machine learning algorithms, thereby contributing to smarter transportation systems. This study focuses on optimizing real-time decision-making for charging and dispatching in large-scale electric taxi fleets to improve their long-term benefits. To achieve this goal, a spatiotemporal decision framework using Bi-level optimization is proposed. Initially, a deep reinforcement learning-based model is built to estimate the value of charging and order dispatching under uncertainty.

The noise tests of electric motor systems serve as the foundation for studying their acoustic issues. However, there is currently a lack of corresponding method for identifying key noise characteristics, such as the noise frequency range that needs to be considered, the significant noise order, and the resonance band worth paying attention to, based on experimental test data under actual operating conditions. This article proposes a method for identifying the noise characteristics of an electric motor system based on tests conducted under distinct operating conditions, which consists of three parts: identifying the primary frequency range, the significant order, and the important resonance band. Firstly, in order to extract noise with the primary energy, the concept of noise contribution is introduced. The primary frequency range and the significant order under a specific operating condition can be obtained after extraction.

With the extension of intelligent vehicles from individual intelligence to group intelligence, intelligent vehicle platoons on intercity highways are important for saving transportation costs, improving transportation efficiency and road utilization, ensuring traffic safety, and utilizing local traffic intelligence [1]. However, there are several problems associated with vehicle platoons including complicated vehicle driving conditions in or between platoon columns, a high degree of mutual influence, dynamic optimization of the platoon, and difficulty in the cooperative control of lane change. Aiming at the dual-column intelligent vehicle platoon control (where “dual-column” refers to the vehicle platoon driving mode formed by multiple vehicles traveling in parallel on two adjacent lanes), a multi-agent model as well as a cooperative control method for lane change based on null space behavior (NSB) for unmanned platoon vehicles are established in this paper.

With the increase of motor speed and the deterioration of operating environment, it is more difficult to predict the transient temperature field (TTF). Meanwhile, it is difficult to obtain the temperature test dataset of key nodes under various complete road conditions, so the cost of bench test or real vehicle test is high. Therefore, it is of great significance to establish a high fidelity, lightweight temperature prediction model which can be applied to real vehicle thermal management for ensuring the safe and stable operation of motor. In this paper, a physical model simulating electromagnetic-heat-flow multi-physical coupling of permanent magnet synchronous motor (PMSM) in electric drive gearbox (EDG) is established, and the correctness of the model is verified by the actual EDG bench test.

Alternating current (AC) heating is an efficient and homogeneous manner to warm Lithium-ion batteries (LIBs) up. The integrated design of AC heating combined with the motor drive circuit has been studied by many scholars. However, the problems of excessive heating frequency (>1kHz) and zeros torque output of the motor during the heating process have not been solved. High-frequency AC excitation may be detrimental to the battery because the effect of high-frequency AC excitation on the state of health of the battery is unknown. In addition, although the zero-torque output can be realized by controlling the q-axis current to zero, the torque ripple is still difficult to eliminate in a real-world application. To further solve the above problems, the motor’s neutral conductor is pulled out and connected to a large capacitor to increase the current amplitude of the AC heating at low frequencies.

With the growing prosperity of the long-distance freight and urban logistics industry, the demand for cargo trucks is gradually increasing. The connecting bracket is the critical connecting part of the truck chassis, which bears the load transmitted by the road excitation and reduces the damage to the frame caused by the load. However, the occurrence of rough road conditions is inevitable in heavy-duty transportation. In this paper, road durability tests and fatigue life analysis are carried out on the original structure to ensure the safety of the vehicle. Based on the known boundary and load constraints, a lightweight and high-performance structure is obtained through size optimization, as the original structure cannot meet the performance requirements. Firstly, the road test was conducted on the truck where the original bracket structure is located.

The intersection is recognized as the most dangerous area because of the restricted road structures and indeterminate traffic regulations. Therefore, according to the Vehicle-to-everything (V2X) communication, Intelligent Transportation Systems (ITS), and Digital Twin data, we present a potential energy field method to establish the general characteristics of intersection traffic safety, evaluate the safety situation of intersection and assist intersection traffic participants in passing through the intersection safer and more efficient. The resulting potential energy field method is established by the contour line of traffic participants' potential energy, which is constructed as a superposition of disparate energies, such as boundary potential energy, body potential energy, and velocity potential energy. The intersection traffic safety is evaluated by the potential energy field characteristic of simultaneous intersection traffic participants.

Electric vehicle sharing (EVS) can alleviate traffic congestion and reduce emissions. However, the poor user experience and lack of word-of-mouth effect lead to the low utilization rate of EVS in China. Based on the demand and pain points of EVS, this paper concentrates on travel mode choice behavior of consumers under social networks and establishes an agent-based model for EVS diffusion. The results show that: (1) Social networks can promote the diffusion of EVS and the number of opinion leaders and the number of fans of opinion leaders play an important role. (2) Consumers are more sensitive to travel costs than non-travel time now, but with the improvement of demand for travel experience, consumers are more concerned with non-travel time. (3) The non-travel time of EVS needs to be reduced to 9, 8 and 7 minutes respectively to retain users when the travel cost increases to 0.7, 0.8 and 0.9 Yuan/minute.

Aiming at the problems of ineffective collision avoidance and vehicle instability in the process of vehicle emergency braking in road conditions with low adhesion and sudden change in adhesion coefficient, a stability-coordinated emergency braking and collision avoidance control system SEBCACS) is proposed. First, according to the motion of the ego vehicle and the target vehicle as well as the road adhesion conditions, a collision time model is proposed for evaluating the vehicle collision risk, and the expected deceleration required to avoid the collision is calculated. Then, the MPC method is used to calculate the yaw moment generated by the four-wheel braking force required to maintain vehicle stability according to the actual and reference yaw rate and side slip angle deviation. Then it is decided whether to implement additional yaw moment control according to the body stability evaluation results.

The driving conditions of commercial logistics vehicles have the characteristics of combined urban and suburban roads with relatively fixed mileage and cargo load alteration, which affect the vehicular fuel economy. To this end, an adaptive equivalent consumption minimization strategy (A-ECMS) with vehicle speed and weight recognition is proposed to improve the fuel economy for a range-extender electric van for logistics in this work. The driving conditions are divided into nine representative groups with different vehicle speed and weight statuses, and the driving patterns are recognized with the use of the bagged trees algorithm through vehicle simulations. In order to generate the reference SOC near the optimal values, the optimal SOC trajectories under the typical driving cycles with different loads are solved by the shooting method and the optimal slopes for these nine patterns are obtained.

The parameter setting has a great influence on the noise reduction performance of the road noise active control (RNC) system. This paper analyzes and optimizes the parameters of the RNC system. Firstly, the model of the RNC system is established based on the FxLMS algorithm. Based on this model, taking the maximum noise reduction as the evaluation index, the sensitivity analysis of convergence coefficient, filter order, and reference signal gain was carried out using the Sobol method with the data measured by a real vehicle on asphalt pavement at 40km/h. The results show that there is no significant interaction between the three parameters. Then, using the idea of orthogonal experiment, the simulation results of the control model are analyzed by taking the maximum noise reduction as the evaluation index. It is found that the convergence coefficient has the greatest effect on the maximum noise reduction, followed by the filter order, and the reference signal gain has the least effect.

Aiming at the test safety problems in the early stage of self-driving cars development, firstly the virtual vehicle on-board CAN data acquisition module of the present project was designed based on virtual LabVIEW. Then a wireless remote control system for the self-driving car was constructed, which integrated the built virtual vehicle on-board CAN data acquisition system, the remote real-time image monitoring module and the remote upper computer control module based on ZigBee wireless transmission. It can execute the environmental awareness training and continuous and complex motion manipulation testing of the vehicle without relying on the driver, which can solve the safety problems in the tests of initial development of self-driving cars. Finally, the four-wheel independent steering electric vehicle was used as the self-driving test vehicle, and the wireless remote control system was tested on the double lane change type path and S-type path.

The past decade has witnessed the rapid development of autonomous parking technology, since it has promising capacity to improve traffic efficiency and reduce the burden on drivers. However, it is prone to the trap of self-centeredness when each vehicle is automated parking in isolation. And it is easy to cause traffic congestion and even chaos when multiple autonomous vehicles require of parking into the same lot. In order to address the multiple vehicle parking problem, we propose a parking planning method with genetic algorithm. Firstly, an optimal mathematic model is established to describe the multiple autonomous vehicle parking problem. Secondly, a genetic algorithm is designed to solve the optimization problem. Thirdly, illustrative examples are developed to verify the parking planner. The performance of the present method indicates its competence in addressing parking multiple autonomous vehicles problem.

The separation and analysis of the torque of the permanent magnet synchronous motor is of great significance for optimizing the torque output of the motor. Based on the frozen permeability method, the virtual work principle (VWP) or the Maxwell stress tensor method (MSTM) is often used to separate the torque for torque analysis. However, considering the influence of non-ideal factors such as motor saturation, cross-coupling and flux harmonics, there are differences in torque separation between the VWP and the MSTM, which has been researched and analyzed in this paper. Based on this, for the assisted airspace barrier design of a surface-inserted permanent magnet synchronous motor, to conduct theoretical research on the torque optimization design, this paper uses the VWP to separate the average torque and the MSTM to separate the torque ripple.

An accurate finite element (FE) model is the basis for the numerical prediction of vibration and noise of permanent magnet synchronous motors (PMSMs). This paper provides an equivalent modeling method of PMSMs considering the orthotropic material parameters of the stator system. First, a theoretical analysis of the influence of orthotropic material parameters on modal characteristics is implemented. Subsequently, the influence of orthotropic material parameters on the modal frequency of the stator is analyzed through the FE method. Then, the modal parameters of the stator core and the stator assembly are obtained by modal tests. According to the equivalent FE model and modal parameters, the orthotropic material parameters of the stator system are acquired. Moreover, to save the calculation time and simplify the modal identification process, the influence of windings is taken into account through additional mass and additional stiffness during the modeling process.

With the strict requirements of harmful emission regulations, carbon peaking and neutralization goal, the internal combustion engine (ICE) industry is facing great challenges. Compared with pure ICE powertrain, hybrid powertrain has the advantages on fuel consumption and harmful emissions, which is more suitable for the market today. In series hybrid powertrain, because of the direct mechanical connection between ICE and motor, the motor can be used as an assistant in optimizing the performance of ICE. In order to realize the cycle-based or crank angle-based control of ICE, a high-frequency motor control system need to be built. Field Programmable Gate Array (FPGA) has the characteristics of high calculation frequency and high reliability to meet the demand. At the same time, the ICE control based on LabVIEW and FPGA has been realized.

In this work, the influences of various real-timely available energy management strategies on vehicle fuel consumption (VFC) and energy flow of a range-extender electric vehicle were studied The strategies include single-point, multi-point, speed-following, and equivalent consumption minimization strategy. In addition, the dynamic programming method which cannot be used in real time, but can provide the optimal solution for a known drive situation was used for comparison. VFCs and energy flow characteristics with different strategies under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) were obtained through computer modeling, and the results were verified experimentally on a range-extender test bench. The experimental results are consistent with the modeled ones in general with a maximum deviation of 4.11%, which verifies the accuracy of the simulation models.

The accelerated global progress in the research and development of automobile products, and the use of new technologies, such as the Internet, cloud computing and big data, to coordinate development platforms in different regions and fields, can reduce the duration and cost of development and testing. Specifically, in the context of the current coronavirus disease (COVID-19) pandemic, which has caused great obstacles to normal logistics and transportation, personnel exchanges and information communication, platforms that can support global operation are significant for product testing and validation, because they eliminate the need for the transportation of personnel and equipment. Therefore, the establishment of a distributed test and validation platform for automotive powertrain systems, which can integrate software and hardware testing, is important in terms of both scientific research and industrialization.

Lane marking detection plays a crucial role in Autonomous Driving Systems or Advanced Driving Assistance System. Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. Then, based on the OTSU method, we propose an approach for extracting lane marking points using intensity information.