Test Vehicle Research Articles

Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses

Highways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in the evaluation of road quality and safety, which is a major input source for vehicles. The vehicle responses-based method of identifying road roughness is efficient and convenient. However, the dynamic characteristics of the vehicle have an important impact on the interaction between the vehicle and the road. When the vehicle parameters are not yet clear, the coupling of unknown parameters and unknown road roughness results in the need for mutual iteration when the existing methods simultaneously identify vehicle parameters and road roughness. To address this issue, this study proposes an effective method for the combined identification of vehicle parameters and road roughness using vehicle responses. The test vehicle is modeled as a four-degree-of-freedom half-vehicle model. In view of the coupling effect between tire stiffness and road roughness, the unknown vehicle physical parameters, except for tire stiffness, are first included in the extended state vector. Based on the extended Kalman filter for unknown excitation (EKF-UI), unknown vehicle physical parameters and unknown forces on the axle are identified. Subsequently, based on the property that the front and rear axles of the vehicle pass through the same road roughness area at a fixed time lag, the tire stiffness is identified by combining the identified unknown forces on the axle. Finally, the road roughness is obtained using the identified vehicle parameters and unknown forces. Numerical studies with different levels of roughness, different noise levels, and different vehicle speeds have verified the accuracy of this method in identifying vehicle parameters and road roughness.

Housekeeping System for Suborbital Vehicles: VIRIATO Mock-Up Vehicle Integration and Testing

The work presented in this paper regards the improvement of a housekeeping system for data acquisition of a suborbital vehicle (VIRIATO rocket or launcher). The specifications regarding the vehicle are presented and hardware is chosen accordingly, considering commercial off-the-shelf components. Mechanical and thermal simulations are performed regarding the designed system and a physical prototype is manufactured, assembled and programmed. Functional and field test results resorting to unmanned aerial vehicles, as well as the system’s integration within VIRIATO project’s mock-up vehicle, are presented. These tests demonstrate the viability of this system as an independent data acquisition system, and simulation results show that commercial off-the-shelf components have the capability of surviving expected launch environments.

Correction: Accelerated Testing and Evaluation of Autonomous Vehicles Based on Dual Surrogates

Correction: Accelerated Testing and Evaluation of Autonomous Vehicles Based on Dual Surrogates

A városi önvezetőjármű-felkészültség kulcstényezőinek meghatározása strukturált szöveg- és tartalomelemzéssel = Investigating Autonomous Vehicle Readness of Cities: A Structured Text and Content Analysis

The process of urban integration of autonomous vehicles is posing increasing challenges for many cities today. Based on the growing number of autonomous vehicle tests on urban roads, it seems that the urban presence of these vehicles requires only minimal preparation from cities. However, their widespread adoption, integration, and effective and safe operation at the urban level require substantial preparation. This study aims to synthesize the factors found in the literature using software-supported structured text and content analysis. Based on our results, we identified nine main factors and twenty-seven key elements that could be crucial in supporting the future mass adoption of autonomous vehicles in urban areas. With our work, we hope to contribute to a deeper understanding of urban autonomous vehicle readiness and the identification of measures necessary to achieve it.

Multi-Step Ageing Prediction of NMC Lithium-Ion Batteries Based on Temperature Characteristics

The performance of lithium-ion batteries depends strongly on their ageing state; therefore, the monitoring and the prediction of the battery state of health (SoH) is necessary for an optimized and secured functioning of battery systems. This paper evaluates and compares three artificial neural network architectures for multi-step ageing prediction of lithium-ion cells: Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). These models use the features extracted from the cell’s temperature to predict the cell’s capacity. The features are extracted from experimental measurements of the cell’s surface temperature and selected based on Spearman correlation analysis. The prediction results were evaluated and compared considering three different percentages of the training dataset: 60%, 70%, and 80%. Training and testing data were generated experimentally based on accelerated ageing cycling tests. During these experiments, four Nickel Manganese Cobalt/Graphite (NMC) cells were cycled under a controlled temperature environment based on a dynamic current profile extracted from the Worldwide Harmonized Light Vehicles Test Cycles.

Fabrication of Remotely Controllable Robotic Weed-Ejector Vehicle

Weed is an unwanted plant in an agriculture field that keeps the crop plants deprived of sunlight, fertilisers and water. If not removed, weed reduces the crop output to a larger extent i.e., endangering the farmer's interests. There are three main reasons for leaving weed to grow in fields and substantial loss of crop output 1) non-availability of agricultural labour, 2) fear of snake bites and 3) higher amounts of time required for weed injection. As of there are no successful ROVs (Remotely operating vehicles) to get to the weed and capable of pulling off the weed without the farmer physically entering into the agricultural fields. The Proposed model reduces farmers getting exposed to snake bites during weed ejection and reduces the time and labour requirement for weed ejection. The project involves the study of different mechanisms required for weed pull-off from the agricultural fields and remote-control systems, modelling of components, fabrication of components, assembly and testing of the Remotely Controllable Robotic Weed-Ejector Vehicle.

Whole body vibration and rider comfort determination of an electric two-wheeler test rig

Background Two-wheeled vehicles are the major mode of transportation in India. Such vehicles are exposed to excessive vibration on the road when compared to four-wheeled vehicles. However, the research on the reduction of whole body vibration in the case of two-wheelers is not explored in detail. The present study predicts rider comfort in the case of an electric two-wheeler as per ISO 2631-1, by obtaining the finding the weighted acceleration at the strategic locations of vibration at the test rig. Methods An electric two-wheeler test rig is used in the study. The values of acceleration from the test rig in running conditions are obtained by using NI LabVIEW 2019. The drive cycle of the electric vehicle (EV) test rig is controlled by Sync sols’ EV lab software. Obtaining the weighted root mean square (RMS) acceleration from running the test setup, it is compared with the ISO 2631-1 standard to obtain the rider comfort. Results Loading area, traction motor, base mount, and suspension were found to be the strategic points of vibration. Frequency weighted RMS acceleration of 0.3 to 0.4 m/s2 obtained at these points are prone to cause discomfort for the rider. Vehicle speed, road profile, and duration of exposure were found to be important parameters affecting the rider’s comfort. A maximum of 4.6 m/s2 amplitude was observed. The loading area, which corresponds to a rider’s seat in actual vehicle, is important and reduction of these vibrations make the ride comfortable for the rider. Suspension and base mount of the test rig are found to be uncomfortable observing the weighted RMS acceleration. Conclusions A suitable damping technique design is very much essential in reducing these vibrations and improve the rider comfort, as many more non-deterministic vibrations are prone to cause dis-comfort in case of actual on road riding conditions.

Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios

Autonomous vehicles (AVs) must fulfill adequate safety requirements before formal application, and performing an effective functional evaluation to verify vehicle safety requires extensive testing in different scenarios. However, it is crucial to rationalize the application of different scenarios to support different testing needs; thus, one of the current challenges limiting the development of AVs is the critical evaluation of scenarios, i.e., the lack of quantitative criteria for scenario design. This study introduces a method using the Spherical Fuzzy-Analytical Network Process (SF-ANP) to evaluate these scenarios, addressing their inherent risks and complexities. The method involves constructing a five-layer model to decompose scenario elements and using SF-ANP to calculate weights based on element interactions. The study evaluates 700 scenarios from the China In-depth Traffic Safety Study–Traffic Accident (CIMSS-TA) database, incorporating fuzzy factors and element weights. Virtual simulation of vehicles in the scenarios was performed using Baidu Apollo, and the performance of the scenarios was assessed by collecting the vehicle test results. The correlation between the obtained alternative safety indicators and the quantitative values confirms the validity and scientific validity of this approach. This will provide valuable guidance for categorizing audiovisual test scenarios and selecting corresponding scenarios to challenge different levels of vehicle functionality. At the same time, it can be used as a design basis to generate a large number of effective scenarios to accelerate the construction of scenario libraries and promote commercialization of AVs.

Experimental Outdoor Vehicle Acoustic Testing Based on ISO-362 Pass-by-Noise and Tyre Noise Contribution for Electric Vehicles

This paper focuses on the novel and unique training provision of acoustics relevant for noise, vibration, and harshness (NVH), focused on the ISO-362 standard highlighting important design aspects for electric vehicles. A case study of the practical implementation of off-site vehicle testing supporting an acoustics module is described, detailing a time-constrained test for automotive pass-by-noise and tyre-radiated noise with speed. Industrial test standards are discussed, with education as a primary motivation. The connections between low-cost, accessible equipment and future electric vehicle acoustics are made. The paper contains a full equipment breakdown to demonstrate the ability to link digital data transfer, analogue-to-digital communication, telemetry, and acquisition skills. The benchmark results of novel pass-by-noise and tyre testing are framed around discussion points for assessments. Inexpensive Arduino Uno boards provide data acquisition with class 1 sound pressure meters, XBee radios provide telemetry to a vehicle, and a vehicle datalogger provides GPS position with CANBUS data. Data acquisition is triggered through the implementation of light gate sensors on the test track, with the whole test lasting 90 minutes.

Experimental evaluation on dynamic performance of medium–low-speed maglev vehicle running on low-positioned subgrade

ABSTRACT As a novel urban rail transit system with relatively low construction costs, medium–low-speed maglev transportation is currently experiencing rapid development in China. However, there have been rare studies on the experiments of medium–low-speed maglev vehicle passing curves and low-positioned subgrade. Therefore, this paper systematically conducts on-site dynamic tests on the Qingyuan Maglev Tourism Line. The experiment provides a detailed analysis of the dynamic characteristics of the vehicle system and subgrade under varying speeds up to 132 km/h. The study explores the impacts of operating speed, track layout, and type of girder structure. The results indicate that a significant increase in operating speed and the application of emergency braking intensify the dynamic response of the vehicle system. When passing through the R700m curve and low-positioned subgrade sections at different speeds, the primary vibration frequency of the sprung structure is below 2.5 hz, while the unsprung structure shows a wide frequency distribution. The peak frequencies of the vibration for the low-positioned subgrade are mainly distributed in the 60–140 hz range, and the vibration response of the girder remains at a low level. The Sperling index values, levitation gap, and lateral offset of the levitation electromagnet of the testing vehicle do not exceed 2.5, 4 mm, and 10 mm, respectively, indicating excellent ride comfort and stable levitation performance. The results of this test study provide a reliable assessment of the safety of the vehicle and subgrade system and would help precise modelling of vehicle and subgrade system in the future.

Cellular Vehicle-to-Everything Automated Large-Scale Testing: A Software Architecture for Combined Scenarios

As the commercialisation of Intelligent Connected Vehicles (ICVs) accelerates, Vehicle-to-Everything (V2X)-based general testing and assessment systems have emerged at the forefront of the research. Current field testing schemes mostly follow the norms of traditional vehicle tests. In contrast, Original Equipment Manufacturers (OEMs) have increasingly focused on the potential influence of V2X communication performance on the application response characteristics. Our previous work resulted in a C-V2X (Cellular-V2X) large-scale testing system (LSTS) for communication performance testing. However, when addressing the need to combine application and communication, the system software faces confronts heightened technical challenges. This paper proposes a layered software architecture for the automated C-V2X LSTS, which is tailored to combined scenarios. This architecture integrates scenario encapsulation technology with a large-scale node array deployment strategy, enabling communication and application testing under diversified scenarios. The experimental results demonstrate the scalability of the system, and a case study of Forward Collision Warning (FCW) validates the effectiveness and reliability of the system.

Empirical Evaluation of the Replacement of Conventionally Powered Vehicles with Hybrid and Electric Vehicles on the Example of the Poznań Agglomeration

From the collected data of the Central Register of Vehicles, an average of 15,000 vehicles were registered per month in the Greater Poland region in 2020–2022. It should be borne in mind that most of them were conventionally powered cars-spark-ignition or compression-ignition engines, the daily operation of which negatively affects the environment. Institutions responsible for regulating the homologation of passenger vehicles are introducing increasingly stringent emission standards to reduce the harmful effects of vehicles on the environment. In addition, more and more public campaigns are being conducted on the use of cars equipped with alternative propulsion systems, e.g., electric, full-hybrid or increasingly popular hydrogen-powered vehicles. The publication presents forecasts for the replacement of vehicles equipped with spark-ignition and compression-ignition engines with cars equipped with hybrid propulsion systems or electric systems. Tests of vehicles in real operating conditions were carried out in the Poznań agglomeration in an urban area, where the number of vehicles per area is higher than in the case of a nonurban zone. The research objects were both cars equipped with a conventional drivetrain and a hybrid system. The research made it possible to draw up a hybridization index, which was then applied to the number of vehicles registered over the past few years.

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.

Pedestrian safety behavior modeling method in human-vehicle-intelligence shared road space

In the context of the development of autonomous vehicles sharing road resources with traditional traffic participants in the future, modeling the safe behavior of pedestrians in the shared road space between people, vehicles and intelligence is crucial for the simulation development and reliability and safety testing of autonomous vehicles. To meet this demand, the safe movement behavior of pedestrians is first analyzed, and on this basis, the traditional pedestrian social force model is improved to construct a momentum model for the interactive movement of pedestrians with other pedestrians, traditional vehicles and autonomous vehicles in the shared road space; then, the safety parameters of the model are calibrated using a data set of human-vehicle interactive movement and a genetic algorithm, and a typical shared road space scenario is built to verify the effectiveness of the model. The simulation results show that the model can simulate the real safe movement behavior of pedestrians, and has better simulation effects when used in tasks such as human-vehicle-intelligence shared road space simulation.

Identification of multiple bridge frequencies using a movable test vehicle by approximating axle responses to contact-point responses: theory and experiment

Identification of multiple bridge frequencies using a movable test vehicle by approximating axle responses to contact-point responses: theory and experiment

A nonlinear model predictive control for air suspension of hub motor electric vehicle based on hybrid H2/H∞ state estimation

To alleviate the vibration from the road irregularities and the unbalanced electromagnetic forces (UEFs), deteriorating the dynamic performance of hub motor electric vehicles (HM-EVs), a novel nonlinear model predictive control (NMPC) is proposed based on a hybrid H2/H ∞ filtering algorithm for the HM-EVs with air suspension (HM-AS). The dynamic HM-AS model involves the electric vehicle’s longitudinal, lateral, and vertical motion. The air suspension system between the body and the hub motor enhances ride quality and insulates the oscillation delivered to the vehicle body. Then, the dynamic HM-AS model is validated by the actual vehicle test, and the proposed filter is developed to obtain the vehicle states. Besides, the NMPC controller guarantees the dynamic performance of HM-AS by intervening in vehicle attitude, road-holding stability, motor safety, and ride comfort. Finally, compared with the application of PID and MPC, the efficiency of the proposed method is demonstrated based on several random road scenarios. The results indicate the proposed algorithm can alleviate the dynamic performance of HM-AS and reduce motor vibration.

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>

Simulation Study on Factors Affecting the Output Voltage of Extended-Range Electric Vehicle Power Batteries

The power battery configuration of an extended-range electric vehicle directly affects the overall performance of the vehicle. Optimization of the output voltage of the power battery can improve the overall power and economy of the vehicle to ensure its safe operation. Factors affecting the output voltage of power batteries under different operating conditions, such as nominal voltage and the number of series and parallel connections of the battery cells, have been studied. This study uses AVL Cruise to establish an overall model of an extended-range electric vehicle to simulate the output voltage characteristics under the different operating conditions of the NEDC (New European Driving Cycle), WLTC (World Light Vehicle Test Cycle) and CLTC (China Light Duty Vehicle Test Cycle). The influence of the output voltage of the power battery under different operating conditions is studied to ensure that the power battery can output energy with high efficiency. The operating conditions have an impact on the output voltage with an idle voltage fluctuation of the operating conditions. The nominal voltage variation and the number of series and parallel connections of the battery cells affect the frequency and time of breakdown.

Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios

Before being deployed on roads, Autonomous Vehicles (AVs) must undergo comprehensive testing. Safety-critical situations, however, are infrequent in usual driving conditions, so simulated scenarios are used to create them. A test scenario comprises static and dynamic features related to the AV and the test environment; the representation of these features is complex and makes testing a heavy process. A test scenario is effective if it identifies incorrect behaviors of the AV. In this article, we present a technique for identifying key features of test scenarios associated with their effectiveness using Instance Space Analysis (ISA). ISA generates a ( \(2D\) ) representation of test scenarios and their features. This visualization helps to identify combinations of features that make a test scenario effective. We present a graphical representation of each feature that helps identify how well each testing technique explores the search space. While identifying key features is a primary goal, this study specifically seeks to determine the critical features that differentiate the performance of algorithms. Finally, we present metrics to assess the robustness of testing algorithms and the scenarios generated. Collecting essential features in combination with their values associated with effectiveness can be used for selection and prioritization of effective test cases.