International Journal of Automotive Technology最新文献

Carbon particles, a primary component of diesel engine emissions, cause persistent erosion in the exhaust piping system, inevitably leading to performance degradation. This erosion can result in reduced fuel economy and increased emissions. The effects of three key parameters including solid particle size, turbine U/C operating conditions and rotational speed on the erosion characteristics of the flow channels of a radial turbine for vehicle diesel engine applications and their impact on performance were investigated through numerical simulations in the study. The findings indicate that larger particle size and higher rotational speed can significantly lead to the higher erosion rate density of the volute channel and casing wall surfaces. Reducing U/C does not substantially affect the distribution of erosion rate density. Centrifugal force will play an important role in the variation of erosion distribution characteristics. Compared to U/C, the other two key parameters are sensitive factors affecting turbine performance degradation. Under the same condition for operating 5000 h, 10 μm particles cause a 7.5-fold increase in efficiency loss change rate compared to 0.5 μm particles. The efficiency loss at 140 krpm is 16 times greater than that at 40 krpm.

A long rigid-flexible coupling transmission shaft (RFCTS) is usually used to connect the dynamometer with the device under test (DUT) in semi-anechoic chamber. However, due to the parameter error and torsional vibration characteristic of RFCTS, load emulation loading accuracy of the test bench is lower. Therefore, the research focused on establishing a control-oriented vehicle powertrain system test bench (VPSTB) model based on RFCTS to further improve the load emulation loading accuracy. First, a 14-degrees of freedom (DOF) high-order lumped mass model of VPSTB is established, and the stiffness and moment of inertia equivalent parameters are obtained. Subsequently, a 7-DOF simplified control-oriented VPSTB model is established based on 14-DOF model, and the model parameters are identified by the recursive least squares estimation (RLSE) method with forgetting factor. Simultaneously, the natural frequency of 14-DOF high-order model and 7-DOF simplified model is calculated, then un-damped vibration response of the two models are compared and analyzed. Finally, simulation and experimental are carried out under open-loop and closed-loop conditions, which show that the effectiveness of 7-DOF simplified model parameters, and it also indicate that the 7-DOF simplified VPSTB model can achieve better loading accuracy of load emulation.

This study aims to investigate the NOx/CO reduction characteristics of harmful gases according to the loading amount of active Ag, which plays the most important role in H₂-SCR, and the type of support. H₂-SCR with Ag loaded on support of TiO₂ was reduced to Ag⁺ at about 140 °C in AgO oxide, and H₂-SCR with Ag loaded on support of Al₂O₃ was reduced to Ag clusters (Ag_n^δ+) at about 260 °C in AgO oxide. Because it is more unstable than AgO oxide, it promoted the chemical reaction of Ag. 0.5Ag/TiO₂ H₂-SCR showed the highest NOx conversion rate of about 18% at 200 °C, and the window width was also widened. 2Ag/Al₂O₃ H₂-SCR improved the low-temperature activity of the catalyst due to its large specific surface area and the loading amount of active catalyst Ag. Although the harmful gas reduction performance has decreased significantly compared to the active material Pt used in the H₂-SCR, research on active materials considering the performance the improvement and economic feasibility must be continued.

A parametric Adaptive Model Predictive Controller (AMPC) based on Particle Swarm Optimization-Back Propagation (PSO-BP) neural network has been developed in this paper, the primary focus is on improving the trajectory tracking performance of autonomous vehicles under varying road conditions. The PSO-BP neural network is employed for real-time adjustment of the controller's predictive horizon and sampling time. A vehicle dynamics model is established and an improved tracking control algorithm involving road curvature feedforward is proposed. In the design of AMPC, the real-time update of tire lateral stiffness is achieved through the adoption of the Recursive Least Squares (RLS) method, ensuring the precision of trajectory tracking for the vehicle under varying operating conditions. The simulation platform, which combines Carsim and Simulink, was employed for validating the proposed approach. The findings reveal that the proposed controller can promptly adjust the predictive horizon and sampling time according to the vehicle's state. Through the employed estimation strategy, real-time adjustments of tire lateral stiffness are achieved, allowing for dynamic alterations of vehicle speed and front wheel angle in response to road curvature. As a result, this approach significantly enhances control accuracy and lateral steering stability, especially in large curvature conditions.

This study proposes an embedded traffic sign detection system, YOLOV5-MCBS, based on an enhanced YOLOv5 algorithm. This system aims to mitigate the impact of traditional target detection algorithms’ high computational complexity and low detection accuracy on traffic sign detection performance, thereby improving accuracy and real-time performance. Our primary objective is to develop a lightweight network that enhances detection accuracy, enabling real-time detection on embedded systems. First, to minimize computation and model size, we replaced the original YOLOv5 algorithm’s backbone feature network with a lightweight MobileNetV3 network. Subsequently, we introduced the convolutional block attention module into the neck network to optimize the feature fusion stage’s attention and enhance model detection accuracy. Concurrently, we employed the bidirectional feature pyramid network in the neck layer for multi-scale feature fusion. Additionally, we incorporated a small target detection layer into the original network output layer to enhance detection performance. What’s more, we transplanted the enhanced algorithm into a Raspberry Pi embedded system to validate its real-time detection performance. Finally, we conducted computer simulations to assess our algorithm’s performance by comparing it with existing target detection algorithms. Experimental results suggest that the enhanced algorithm achieves an average precision mean (mAP @ 0.5) value of 95.3% and frames per second value of 91.1 on the embedded system.

When vehicles with traditional passive suspension systems are driving on complex pavement, the large vibration of the body will result in relatively negative effects on ride comfort, vehicle handling, and stability of passengers and goods. Body attitude tracking control based on tandem active–passive suspension can improve vehicle attitude stability and passability by enabling the body attitude to track an ideal position. In addition, the performance limitations of the actuator are considered in the design of the attitude tracking control algorithms. The attitude tracking performances are investigated in both simulations and real car tests. Two control algorithms which adopt linear quadratic regulator (LQR) and model predictive control (MPC) algorithms, are compared and analyzed in terms of theory and control performance. The simulations and real car tests results show that both attitude tracking control algorithms can effectively track the ideal body attitude with acceptable errors under different pavements, and the control effect of MPC is slightly better than that of LQR. In this way, attitude tracking of car body shows a lot of potential when a vehicle is in harsh environments.

To address the issues of curvature discontinuity and terminal tire non-return in the parallel parking of autonomous vehicles, a novel parallel parking path planning method based on the combination of the quintic polynomial curve and an improved sigmoid function was proposed. First, a vehicle kinematic model was established. Second, considering the position, front wheel angle, and yaw angle constraints during the parking process, a hybrid superimposed curve was designed. The parking path planning problem was converted into an optimal control problem, with the maximum curvature and curvature at both ends as objective functions, and the parameters were optimized using the simulated annealing algorithm. Subsequently, for path tracking control, the simulated annealing algorithm was used to optimize the prediction time horizon of the model predictive control algorithm. Finally, a series of actual vehicle experiments were conducted based on the Apollo Autonomous Driving Developer Suite, and the effectiveness of the proposed path planning method was validated. Therefore, this method can provide a certain reference for automatic parking path planning technology.

The electric drive thermal management system of electric vehicles is an important part of electric vehicles. To improve the cooling effect of the electric drive system and reduce energy consumption, a cooling scheme combining the spray oil cooling of the motor end winding with the cooling water jacket of the motor shell and the water cooling in the motor shaft is proposed. Based on the electric drive cooling system framework of electric vehicles, a fuzzy logic control strategy is established. The flow rate and heat dissipation in the system are optimized by controlling the rotation speed of the oil/water cooling pump and the radiator fan, which further reduces the energy consumption of the system. In addition, through the co-simulation of AMESim and Simulink, under the same simulation time, the simulation comparison and analysis are carried out under SFTP-US06 and WLTC conditions, respectively. The power loss of fuzzy logic control is 9% and 28% lower than that of switching control, and 21% and 25% lower than that of PID control, respectively.

Recently, hub motor driven vehicles (HMDV) have been recognized as the ideal configuration of electric vehicles in the future. However, the adverse effect of its special structure configuration seriously affects the vehicles dynamic performance, and becomes the bottleneck of its further development and industrial application. In this paper, the characteristics of inertial suspension adjusting vertical motion inertia are utilized to suppress the HMDV adverse effect. Firstly, the coupling model of vehicle suspension and hub motor is established, and the coupling excitation of road surface and unbalanced electromagnetic force on vehicle suspension is studied. Aiming at the influence mechanism of the adverse effect, an inertial suspension model of the skyhook and groundhook (SH-GH) algorithm is established. Moreover, with the primary goal of improving the ride comfort, an inertial suspension and the mixed skyhook and power-driven-damper (SH-PDD) algorithm are combined. Particle swarm optimization (PSO) is utilized to optimize suspension performance. The results show that the RMS value of body acceleration, unbalanced radial force and dynamic tire load of inertial suspension decreased by 10.91%, 50% and 8.53%. It is proved that its characteristics can effectively suppress the HMDV adverse effects. The SH-GH inertial suspension also further reduces the vehicle performance index. The SH-PDD inertial suspension reduces the RMS value of body acceleration by 27.45%, significantly improves the vehicle ride comfort. In general, the inertial suspension proposed in this paper can effectively suppress the adverse effect of HMDV vertical vibration, and improve the HMDV ride comfort and road friendliness. It provides a new direction for the research of suppressing the adverse effect of HMDV vertical vibration.

The proton exchange membrane fuel cell (PEMFC) in a fuel cell electric bus (FCEB) converts hydrogen's chemical energy into electrical energy. The fuel cell system comprises a fuel cell stack and a balance of plant (BOP) system, which efficiently controls the stack. Fuel cell and battery are sensitive to operational temperature, which directly impacts performance, lifespan, and safety. Therefore, a thermal management system (TMS) is necessary to maintain an appropriate temperature by dissipating the heat generated by the fuel cell and battery. In this study, the exponential or quadratic relationships between the power consumption of the major components of an FCEB and various factors, such as temperature and flow rate influencing the operational behavior and control of the components, were analyzed based on the results of a dynamometer vehicle test. Additionally, the vehicle's energy flow was calculated under different fuel cell load conditions. When the fuel cell operated at 56.3 kW, TMS power was 6.6 times higher than at 20 kW. At full load, under 90 kW, it increased to 17.4 times higher. The rise in fuel cell load correlated with higher heat generation, resulting in a significant increase in power consumption for both the radiator fan and coolant pump.