Real Vehicle Test Research Articles

Influence of trains meeting on the ventilation performance of equipment compartment with independent air duct in high-speed train: numerical and experimental study

AbstractDuring the train meeting events, train equipment compartments are exposed to the worst pressure changes, potentially affecting the ventilation performance of equipment, particularly for electrical facilities equipped with independent air ducts. In this paper, a two-step method is used for numerical computation: (1) obtaining the temporal and spatial transient node data of the flow field sections during the train-passing simulation and (2) using the data as the input data for the equipment compartment simulation. In addition, this paper also compares the difference in equipment ventilation between the single-train and train-passing scenarios in real vehicle tests. The results indicate that the primary factors influencing ventilation effectiveness are the aerodynamic compression and deceleration of airflow induced by the other train’s nose, as well as the instability of the external flow field in the wake of the other train. During train crossing, the air is forced into the air duct, with a maximum ratio of the airflow in-duct to the airflow out-duct reaching 3.2. The average mass flow falls below the rated mass flow for the converter. Compared to the rated air volume of converter, the maximum suppression rates obtained from testing and simulation are – 24.5% and – 16.8%, respectively. Compared to the single-train operation, the maximum suppression rates obtained from testing and simulation are – 15% and – 18%, respectively. These findings provide valuable insights into the design and operation of high-speed trains.

Electric vehicle ramp recognition based on fusion of vehicle mass estimation

Road slope is the necessary environmental information for intelligent electric vehicles. The accuracy of slope recognition directly determines the control quality of vehicles on the hill. Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to To satisfy the application requirements of mass production vehicles, this paper proposes an electric vehicle slope recognition method based on vehicle mass estimation. Firstly, the vehicle longitudinal dynamics model is established, and the signal characteristics of the acceleration sensor under the actual vehicle conditions are analyzed. The least square vehicle mass estimation strategy with forgetting factor is constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms are designed for static parking and dynamic driving scenarios. In the static scenario, the filter latch strategy is used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario, the Kalman filter algorithm based on measurement noise adaptation is designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method is verified by Simulink -CarSim joint simulation. Finally, the real vehicle test is completed on Chery new energy's mass production electric vehicle platform and domain controller. road test results show that the mass estimation error is less than ±10 kg; the static estimation error of the slope is less than 0.001 rad, and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved, which ensures the environmental adaptability of the intelligent electric vehicles.

Integrated Motion Control Strategy Considering Parameter Robustness for Distributed Vehicle by Wire

<div>To address the issues of functional conflicts in execution subsystems and the deterioration of control performance due to model parameter uncertainties in the motion control of distributed vehicle by wire, this article proposes an integrated control strategy considering parameter robustness. This strategy aims to compensate for model mismatch, resolve functional conflicts, and achieve motion coordination. Based on the over-actuation characteristics of distributed vehicle by wire, this article constructs the dynamic model and utilizes the tire cornering properties along with phase portraits to delineate the working regions of the execution subsystems. To deal with model parameter uncertainties and mismatch, tube-based model predictive control (tube-based MPC) is applied to the control strategy design, which compensates for model deviations through state feedback and constructs a robust positively invariant set (RPI) to constrain the system state. Correspondingly, the weights of control inputs are adjusted adaptively, according to the working regions, to optimize the coordination logic of integrated control. In order to verify the effectiveness and feasibility of the strategy, extreme driving condition tests are executed on hardware-in-the-loop (HIL) and real vehicle test platforms. The test results indicate that the strategy proposed in this article is able to reduce the sideslip angle and tracking error of yaw rate, improve driving stability under extreme conditions through integrated control, and especially, it can still maintain precise stability control performance under severe model mismatch, exhibiting strong robustness facing parameter uncertainties.</div>

Clutch Torque Optimization for Heavy-Duty Vehicles Starting Based on Intelligent Identification of Driver’s Starting Intention, Vehicle Mass, and Road Grade

<div>For heavy-duty vehicles equipped with automated mechanical transmission (AMT), the control of automatic clutch torque is crucial during the start-up process. However, the difficulty of controlling clutch torque is exacerbated by differences in driver’s starting intentions, changes in vehicle mass, and road gradient. Therefore, this article proposes the clutch starting torque optimization strategy based on intelligent recognition of driver’s starting intention, vehicle mass, and road gradient. First, an intelligent recognition strategy is proposed based on the combination of data-driven and onboard transmission control unit (TCU) algorithms, which improves the accuracy of recognizing the driver’s intention to start as well as the vehicle mass and road gradient. Based on the vehicle’s historical state data information, the predictive model is trained offline using a long–short-term memory (LSTM) network to obtain predicted parameter identification results, which are then used to calibrate the computed values of the onboard TCU algorithm. Second, the clutch torque optimization strategy is designed based on the driver’s starting intention, while considering the effects of road gradient and vehicle mass on the clutch starting resistance torque. The weight coefficients of the objective performance function are adjusted according to the driver’s starting intention, and the Pontryagin’s minimum principle (PMP) is used to solve the clutch target torque. Finally, offline data training and real-vehicle testing are performed. The results show that the optimization strategy can effectively reduce the friction work and the degree of impact during the starting process, minimize the clutch slipping time, and improve the smoothness of vehicle starting and driving comfort.</div>

Analysis of hybrid electric vehicle performance and emission applied to LPG fuel system

Global carbon neutrality policies have intensified regulations on automotive emissions. CO2 and particulate number (PN) are major pollutants, with CO2 contributing to global warming and particulates affecting human health. Despite the increase of electric vehicles, internal combustion engines remain prevalent owing to their mature technology, infrastructure and economic efficiency. Liquefied petroleum gas (LPG) is a promising alternative fuel with lower CO2 and PN emissions. LPG direct injection (LPDI) technology improves fuel efficiency and reduces emissions compared to current LPG vehicles. However, vapor-lock issues at high temperatures must be addressed. Hybrid electric vehicles (HEV), combining internal combustion and electric motors, improve fuel efficiency, reduce emissions and serve as a transitional technology prior to electric vehicle (EV). In this study, a conventional 2-liter gasoline hybrid electric vehicle is converted to an LPDI-HEV using LPG. The experimental results showed that the engine output was within 5 % of an equivalent level. In real vehicle tests with the hybrid system applied, CO2 emissions were confirmed to be within a 3% difference, indicating an equivalent level, while PN emissions were significantly reduced. Although the LPDI HEV has lower fuel efficiency compared to the gasoline direct injection hybrid electric vehicle (GDI) HEV, it was found to be 12 % more economical when considering costs. This study also assesses the economic viability and environmental benefits of converting GDI HEV to LPDI HEV in urban taxi applications, providing policy insights for sustainable transportation.

An Automatic Vehicle Navigation System Based on Filters Integrating Inertial Navigation and Global Positioning Systems

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system.

Research on Improved Algorithms for Cone Bucket Detection in Formula Unmanned Competition.

The model network based on YOLOv8 for detecting race cones and buckets in the Formula Unmanned Competition for Chinese university students needs help with problems with complex structure, redundant number of parameters, and computation, significantly affecting detection efficiency. A lightweight detection model based on YOLOv8 is proposed to address these problems. The model includes improving the backbone network, neck network, and detection head, as well as introducing knowledge distillation and other techniques to construct a lightweight model. The specific improvements are as follows: firstly, the backbone network for extracting features is improved by introducing the ADown module in YOLOv9 to replace the convolution module used for downsampling in the YOLOv8 network, and secondly, the FasterBlock in FasterNet network was introduced to replace the fusion module in YOLOv8 C2f, and then the self-developed lightweight detection head was introduced to improve the detection performance while achieving lightweight. Finally, the detection performance was further improved by knowledge distillation. The experimental results on the public dataset FSACOCO show that the improved model's accuracy, recall, and average precision are 92.7%, 84.6%, and 91%, respectively. Compared with the original YOLOv8n detection model, the recall and average precision increase by 2.7 and 1.2 percentage points, the memory is half the original, and the model computation is 51%. The model significantly reduces the misdetection and leakage of conical buckets in real-vehicle tests and, at the same time, ensures the detection speed to satisfy the deployment requirements on tiny devices. Satisfies all the requirements for deployment of tiny devices in the race car of the China University Student Driverless Formula Competition. The improved method in this paper can be applied to conebucket detection in complex scenarios, and the improved idea can be carried over to the detection of other small targets.

Research on Dual-Actuator Shift Control of Dual-Mode Coupling Drive Electric Vehicles

Dual-mode coupling drive system can improve the dynamic performance of electric vehicles through mode switching, and the quality of mode switching directly affects the comfort of drivers and passengers. Mechanical coupling on the left and right sides of the single actuator causes mutual interference during shifting, resulting in prolonged power interruption time and shifting shock. Therefore, this paper analyzes the mutual interference mechanism of single-actuator shifting, designs a new dual actuator, and proposes a staged fuzzy PID controller. Finally, the effectiveness of dual-actuator shifting is proven through simulations and real vehicle testing. Compared with conventional PID control and BP neural network PID control, the shock degree is reduced by 64% and 50%, which improves the mode-switching quality of the dual-mode coupling drive system.

Analysis of Seat Vibration during High-Speed Driving for a Certain Vehicle Model

Abstract To address the seat shaking issue of a certain vehicle model under high-speed conditions, this article utilizes the TPA method, starting from the “source-path-response” perspective. By comparing with benchmark vehicles’ lateral parameters, it confirms the abnormal vibration frequency of the seat as 12Hz in the Z direction. Through real vehicle testing, the validity of this approach is verified, providing clear optimization solutions and improvement suggestions for subsequent vehicle NVH design.

Impact of traffic signs on driving speed at mountain highway tunnel entrances − The role of low-volume intermittent information

Driver’s perception of speed is the basis of driving safety, and installing speed limit signs at tunnel entrances is an intuitive means of controlling a driver’s driving speed. Therefore, tunnel entrance signs should be set up effectively to ensure each signage can fulfil its intended effect. Quantifying the information volume conveyed by traffic signs, discovering the impact of different information volumes on driving speeds, and understanding the effects of typical signs are the prerequisites for the effective use of signage. This study collected the speed variations of 40 drivers driving at nine mountainous highway tunnels with different entrance traffic signs through a naturalistic driving real vehicle test. The effect of low-volume intermittent information at tunnel entrances is identified in terms of the black hole effect on drivers (BHD), white hole effect on drivers (WHD), velocity fluctuation trend (VFT), and effect of speed limit signs (ELS). The study’s results confirm that the effect of speed limit signage with traffic sign information volume (TSIV) in the range of 8.904 to 33.318 bit is significant. In comparison, the sign will lose its effectiveness in guiding drivers to control their speed when TSIV is greater than 58.641 bits. Using low-volume information speed limit signs or tunnel warning signs repeated twice before entering a tunnel can alert drivers and regulate their driving speeds. The speed limit sign (sign ①) for each type of vehicle is set individually, and the combined speed limit and no lane change sign (sign ②) used in two stacks have a strong speed control effect. Moreover, the sign ②, when used alone, has the best timeliness; the effective distance of speed control can reach 1522 m; using it before short tunnels or long tunnels less than 1500 m can achieve better effectiveness.

Energy-saving drive control strategy for electric tractors based on terrain parameter identification

Emissions from agricultural production already have a significant impact on the global environment. New energy, zero-emission electric tractors have great potential for application. How to further reduce energy consumption under the premise of ensuring operational stability is an urgent problem to be solved to realize the broad application of electric tractors. For this purpose, this paper proposed an energy-saving drive control strategy for electric tractors based on terrain parameter identification. At first, the longitudinal dynamics model and load transfer model of the electric tractor were established by considering the terrain information of the area to be operated. Mathematical principles for adjusting wheel slip to reduce energy consumption in electric tractors are presented. Then, a wheel longitudinal force estimation algorithm was designed based on the principle of sliding-mode observer and dynamics compensation. To achieve energy-saving drives for electric tractors, a particle filter based on a wheel-soil model was designed to identify terrain parameters in real time and calculate the optimal slip to control the torque of the drive motor. Lastly, the strategy is validated by hardware-in-the-loop (HIL) simulation tests and real-vehicle tests. HIL tests show that this strategy results in a 25.35% increase in operational speed stability and a 6.79% reduction in operational energy consumption compared to the conventional drive control strategy. The real vehicle test shows a 45.09% increase in operating speed stability and a 9.51% reduction in total energy consumption of the proposed method. The proposed drive control strategy reduces operational energy consumption and operational costs on the premise of ensuring the stability of operational speed, which is conducive to the implementation of cleaner production.

Consistency and repeatability verification for vehicle in the loop test platform based on digital twin of proving ground

Abstract A high-confidence vehicle-in-the-loop test method based on the digital twin of proving ground (DTPG-VIL) is proposed for the test of autonomous driving vehicles. The overall architectural design of the DTPG-VIL and the corresponding components are introduced, with a focus on describing the scene simulation system based on the digital twin of the proving ground. A King Long intelligent driving sightseeing bus for test verification is introduced, and its positioning performance is verified by the high-definition positioning base station and GPS/INS combined navigation system to verify the accuracy of its positioning precision and meet the requirements of the digital twin test so that the DTPG-VIL can obtain more information from the bus. Taking the automatic emergency avoidance function as an example, the method, process, and experimental results of the control test between the DTPG-VIL test and the real vehicle test in a proving ground are designed. Through the correlation coefficient analysis and transient response analysis, consistency and repeatability comparisons are carried out to verify the high confidence of the digital twin test.

Automobile exhaust flexible thermoelectric harvester enabled by liquid metal-based heatsink

The softening thermoelectric generator based on a deformable heatsink is expected to facilitate achieving remarkable temperature differences and thermal harvesting capabilities while alleviating exhaust back pressure and engine operating perturbations. However, the available soft polymer materials with poor thermal characteristics face challenges in realizing efficient heat transfer. Herein, we reported a highly integrated automobile exhaust flexible thermoelectric generator (IAE-FTEG) enabled by a liquid metal (LM)-based stretchable heatsink for cylindrical thermal sources to achieve efficiently harnessing waste heat energy and continuously powering multiple vehicle-mounted sensors. IAE-FTEG exhibits excellent flexibility (the bending radius at the apex of 0.57 cm) and outstanding heat transfer capability by introducing the porous sandwich-based soft electrode films and LM-based thermal interface material, achieving an output power enhancement of 25.7 %. The flexible heatsink is prepared by embedding LM droplets and copper particles into the elastic matrix forming solid–liquid multiphase composites, which obtain considerable thermal conductivity (2.40 W/mK@200 % stretching deformation) and excellent geometric conformity. We built the bench and the real vehicle test platform of the IAE-FTEG waste heat recovery system. The simulation test results demonstrate that the maximum power density achieved by the IAE-FTEG waste heat recovery system is 244 kW/m3. The real vehicle tests show that the IAE-FTEG waste heat recovery system has a relatively stable output performance with an average power density of 117 kW/m3 under urban conditions and can realize a stable power supply for back-end loads. Our IAE-FTEG can offer new opportunities in enabling efficient curved-surface waste heat recovery, flexible wearable electronics, and personalized thermal management.

Driving Characteristics of Drivers Avoiding Lateral Obstacles in Mountain Highway Tunnels: A Dynamic Lateral Safety Distance Prediction Model

A natural driving real-vehicle test was conducted in nine tunnels in Chongqing, China. The driving trajectories and speeds of 40 drivers were recorded to better understand drivers’ driving characteristics under the mutual influence of various tunnel lengths and numerous lateral obstacles on mountainous highways. According to the driver’s need for lateral safety distance in different scenarios, the conditions and scope of the occurrence of the shy away effect are defined, and a dynamic avoidance prediction model is established in multiple scenarios. It was demonstrated that, when there were no lateral dynamic obstacles, drivers were influenced by the tunnel sidewalls to drift away from them. However, when lateral dynamic obstacles were present, drivers’ driving trajectories were corrected to be closer to the inside of the lane. The influence of vehicles of the same type resulted in the trajectory offset to the left by 0.2 m (extra-long tunnel), 0.7 m (long tunnel), 0.5 m (medium tunnel), and 0.7 m (short tunnel) compared with the driving trajectory without the influence of the obstacle. The influence of trucks causes the trajectory offset to the left compared with the driving trajectory without obstacles by 0.2 m (extra-long tunnel), 0.5 m (long tunnel), 1 m (medium tunnel), and 1.75 m (short tunnel). There is some variation in the degree of risk to the driver from different obstacles under different tunnels. The lateral distance between the driver and the dynamic obstacle is related to the nature of the available driving distance ahead. A sufficiently long driving maneuver distance enables the subject vehicle to gradually overtake the lateral dynamic obstacle in a more stable state. Dynamic avoidance models with fitting accuracies of 0.6–0.99 were implemented to predict vehicle trajectories in different tunnels and under the influence of various lateral dynamic obstacles. The study of the correlation of vehicle trajectories under the influence of various factors in tunnels is completed by the research findings, which can serve as a foundation for the design of driving behavior regulation, an improvement in traffic safety facilities, traffic management, and monitoring of lateral safety distances of intelligent vehicles in tunnels.

Adaptive Multi-Sensor Fusion Localization Method Based on Filtering

High-precision positioning is a fundamental requirement for autonomous vehicles. However, the accuracy of single-sensor positioning technology can be compromised in complex scenarios due to inherent limitations. To address this issue, we propose an adaptive multi-sensor fusion localization method based on the error-state Kalman filter. By incorporating a tightly coupled laser inertial odometer that utilizes the Normal Distribution Transform (NDT), we constructed a multi-level fuzzy evaluation model for posture transformation states. This model assesses the reliability of Global Navigation Satellite System (GNSS) data and the laser inertial odometer when GNSS signals are disrupted, prioritizing data with higher reliability for posture updates. Real vehicle tests demonstrate that our proposed positioning method satisfactorily meets the positioning accuracy and robustness requirements for autonomous driving vehicles in complex environments.

A comprehensive evaluation of the frog effect on drivers in mountain highway tunnels – The effect of low-volume intermittent information

Objective The driver’s inability to fully absorb and react to operational cues while driving is like boiling a frog in warm water. With intermittent, low-volume information, drivers can underreact by ignoring these minor but continuous changes. This paper aims to provide an opportunity to test the effects of intermittently occurring low-volume information on drivers. Methods A real vehicle test with naturalistic driving was used to collect driving speed data from 40 drivers on a highway tunnel section in Chongqing, China, where nine tunnels are located. Drivers were classified into three categories according to the degree of compliance of their driving speed with the speed limit required by traffic signs, and drivers were analyzed in terms of their sensitivity to traffic signs and their reaction to driving maneuvers. Results Conservative drivers are the most absorbent of low-volume intermittent information, and the cumulative effect of the frog effect does not exceed 2.00 km; eager drivers tend to ignore this information, and the cumulative effect of the frog effect reaches 2.91 km; and the general type of driver is in the middle of these two types of drivers, and the frog effect gradually penetrates the driving speed in a weakly increasing manner, up to a maximum of 9.8 km. Conclusion At the beginning of a journey, drivers are most sensitive to traffic signs, and low-volume intermittent information can also play a role in guiding driving operations effectively at this time. However, as the driving distance increases, the effect of the frog effect on different types of drivers gradually increases, even exceeding the effect caused by the black-and-white hole effect, especially when driving in tunnel groups. Considering the driving characteristics of different types of drivers to improve the deployment of low-volume intermittent information and reduce the distance of the frog effect can effectively improve driving safety.

A study on active road noise control based on operational transfer path analysis and selective subband adaptive filtering

In the feedforward active road noise control (ARNC) system, the noise reduction performance relies on the correlation between the reference signals and the target noise signals. To achieve better noise reduction performance, the operational transfer path analysis (OTPA) model with singular value decomposition was proposed to separate the road noise component from the target noise. Combined with OTPA and multi-coherence sorting method (MCSM), simulation analysis and real vehicle tests under various speeds both showed that the system with optimized reference sensor set had better road noise reduction performance, especially at the peak frequencies. To further improve noise reduction performance and reduce computational load, a selective subband adaptive filtering (SSAF) algorithm was proposed to build an active road noise control model with optimized reference sensor set. Simulation results, employing real vehicle test data, showed that the proposed method further improved the noise reduction performance and effectively reduced the amount of computational load.

Research and Experiment on Cruise Control of a Self-Propelled Electric Sprayer Chassis

In order to address the issues of poor stability in vehicle speed and deteriorated spraying quality caused by changes in road slope and the decrease in overall mass due to liquid spraying, this study focuses on analyzing the structure and longitudinal dynamic characteristics of a 4WID high ground clearance self-propelled electric sprayer. By utilizing MATLAB/Simulink software, three subsystems, namely, the inverse longitudinal dynamics model, torque distribution model, and motor model, are established. The model takes into account the effects of longitudinal driving resistance, slope, and vehicle roll angle on the distribution of loads among the four wheels during slope driving. A seven-degrees-of-freedom dynamic model is developed. A hierarchical control structure is designed, incorporating an upper-level PID controller and a lower-level fuzzy PID controller, to control the overall system. The control algorithms are tailored to the specific characteristics of the sprayer’s operation, and simulation experiments are conducted under the corresponding operating conditions. Building upon this, a sensor-equipped experimental platform is set up in the self-propelled sprayer manufactured by the team in the preliminary stage. Real vehicle tests are conducted in two scenarios: transition transportation and field operations, with the evaluation of the overall vehicle speed serving as the performance metric to validate the correctness of the model and the control theory.

Optimization of energy flow in thermal management of electric vehicles based on real vehicle testing and digital twin simulation

The thermal system is one of the most critical parts of electric vehicles since its performance directly affects the vehicle's energy consumption and emissions. The current digital twin technology in the auto industry lacks a thermodynamic model focusing on energy consumption, and the control strategy lacks an adversary model, which does not allow for an in-depth discussion of energy consumption without sufficient use of data. This paper aims to study the optimization of the thermal system coupled energy flow in electric vehicles with digital twin technologies. The vehicle thermal management digital twin technology discussed in this paper establishes the digital twin model of the vehicle thermodynamic system that focuses on energy consumption. Meanwhile, vehicle energy flow experiments are carried out to analyze the energy consumption of key components under typical high energy consumption conditions. Accordingly, this research optimizes the control logic to optimize energy consumption and enhance vehicle ranges without changing the hardware architecture. The results show that after the optimization of the control strategy, the electricity consumption under high-temperature, low-temperature, and dehumidification conditions is reduced by 890 W, 456 W, and 1594.9 W, respectively. The vehicle driving mileages are increased by 12.51 km, 6.16 km, and 22.42 km, respectively.

Vibration response analysis of boneless wiper-windshield system based on multi-body dynamics model

The vibration and noise generated by the boneless wiper-windshield system during its actual operation bother the occupants. Exploring the dynamic characteristics of the boneless wiper-windshield system is a prerequisite for understanding vibration and noise. In this paper, the dynamic characteristics of the boneless wiper-windshield system are investigated, and a rigid-flexible coupled multi-body dynamic model matching the real boneless wiper-windshield system is constructed. The friction characteristic of the dynamic model is studied through the wiper test bench experiment. The nonlinear dynamic response of the dynamic model is calculated with the Adams and results of the frictional vibration are well reproduced in real motions. The parallel analysis of the simulation calculation results and the real vehicle test results confirms the validity of the model. The results show that the vibration generation mechanism at the moments before and after the reversal can be explained by the instability of the wiper blades due to the negative friction characteristic. There are differences in the magnitude of the reversal impact force and the moment of reversal at each position of the wiper blade. The effect of the speed-gears on the system vibration is analyzed through the simulation results. The analysis method and simulation results in this article can provide reference for the research on vibration and noise control of boneless wiper systems.