Road Test Research Articles

Numerical study on the performance, combustion characteristics and energy flow distribution of gasoline-powered vehicle under synthetic actual driving test cycle

In order to meet the requirements of vehicle performance prediction and optimization under actual urban road conditions, a synthetic actual driving test cycle (ADTC) is proposed to accurately predict the performance and energy distribution characteristics of vehicles. Meanwhile, a multi-physical field vehicle energy management simulation model is built and verified carefully. Thus, numerical analysis was conducted on the performance of vehicles under ADTC and the characteristics of energy distribution were revealed. The results show that, the brake thermal efficiency (BTE) of the engine under ADTC varies in the range of 14.88 %–30.54 %, that is lower than worldwide harmonized light vehicles test cycle (WLTC) and new European driving cycle (NEDC). Besides, the proportion of combustion loss is WLTC, ATDC and NEDC from the largest to the smallest, which is mainly related to the complexity of speed variation. Based on this, unlike traditional testing cycles, ADTC synthesized from local actual road test data can more accurately evaluate the actual performance of vehicles under local characteristic road conditions and provide guidance for deep optimization, which is beneficial for energy conservation and emission reduction.

Exploring particle concentrations and inside-to-outside ratios in vehicles: A real-time road test study

In transportation microenvironments, humans exposed to particulate matter (PM) inside vehicles can experience higher levels of daily exposure. To make inside-vehicle PM exposure measurements more feasible and easy under real driving conditions, and to quantify the relationship between the concentrations and influencing factors, we assessed PM1, PM2.5, and PM10. levels. Additionally, we collected key influencing factors to develop predictive models. The measurements of PM1, PM2.5, and PM10 concentrations showed that the ventilation setting was a significant influencing factor. The concentrations decreased significantly under the recirculation setting (RC) compared to the outside air setting (OA). The inside-to-outside (I/O) ratios of PM were 1.69 to 1.93-fold higher than those of RC under OA conditions. However, a substantial reduction in the I/O ratios was observed when RC was employed. Although both the concentrations and I/O ratios exhibited significant differences, they demonstrated strong potential relationships. PM2.5 I/O ratios accounted for over 85 % of the variation in the PM1 and PM10 I/O ratios. The developed models for the I/O ratios of PM accounted for >40 and 60 % of the variation in the measured I/O ratios for RC and OA, respectively. We used the vehicle age, vehicle interior volume, speed, cabin temperature, cabin humidity, and their higher-order terms as predictive variables. It is important to note that the influential predictive feature importance differed under RC and OA, and considering the vehicle characteristics between vehicles of the same type may be necessary when using RC. Overall, these findings indicate that the inside-vehicle PM exposure can be measured more easily under real driving conditions by considering the key influencing factors and utilizing the developed predictive models.

Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan

The advancement of autonomous driving technology has urgently necessitated enhanced safety testing measures. Traditional road testing methods face significant challenges due to their high costs and prolonged durations. In response to the inefficiencies of manual scenario construction and the difficulties in selecting effective scenarios using common scenario generation methods in autonomous driving safety testing, this study proposes an innovative automatic SG system based on PreScan2021.1.0. The SG process is significantly simplified by this system’s capability to swiftly and accurately generate a vast array of specific scenarios through the input of scene parameters. The results indicate that this system achieves SG at a rate 2.5-fold faster than manual methods, alongside substantial improvements in accuracy. This system introduces a novel approach to virtual simulation, which is vital for the progress of autonomous driving safety. It offers a new paradigm for quickly and precisely generating test scenarios for autonomous driving.

Vision based road profile estimation for preview-controlled vehicle suspension systems

In this paper, a vision-based road profile estimation method was studied for the control of semi-active and active suspension systems. For the purpose, a monocular camera was used to collect data from the road tests to develop a logic to convert the camera measurements into the road profile data. For the generation of the road profile, alignment of the different sets of camera measurements and their coherence were expressed. Importance of the sensor and process noise removal were shown in recognition of the high frequency content of the road profile, which was a particular interest of the study. Additionally, a density-based clustering algorithm was taken into account to cluster the measured points vertically, to remove the process and sensor noise. The density-based clustering method reduced the noises and allowed detection of the high and low frequency contents of the road.

Research on noise reduction of drive axle hypoid gear based on tooth surface mismatch modification

In order to reduce the transmission noise of drive axle hypoid gear, a method of tooth surface mismatch modification was proposed. Firstly, on the basis of establishing the grinding mathematical model of hypoid gear with cutter tilt, the theoretical tooth surface equation was derived, and the numerical tooth surface was calculated by dividing the grid points on tooth surface. Secondly, the pinion tooth surface which was full conjugated with gear tooth surface was constructed, and the tooth surface mismatch topography was established and decomposed into the first order and second order mismatch coefficients. According to the modification requirements, the pinion target surface was constructed by modifying the tooth surface mismatch coefficients, and the pinion modified machine setting parameters were acquired by establishing the pinion processing parameters corrected mathematical model. Finally, the loaded tooth contact analysis with finite element simulation and drive axle NVH (Noise, Vibration, Harshness) simulation were carried out for a pair of hypoid gear, therefore the loaded tooth contact areas, transmission errors curves, and noise simulation curves were obtained, and the simulation results show that after modification the tooth contact stress, loaded transmission errors amplitude, and NVH simulation curves value are all reduced. The road test results indicate that the drive axle hypoid gear howling phenomenon under the high speed operation can be eliminated, so the effectiveness of tooth surface modification method and NVH simulation method were verified.

Wear Behavior of SBR/BR Compounds Including Different ZnO Types

Zinc complexes have considerable impact on human health and environment especially on aquatic wildlife. One of the main sources of zinc release to the environment is worn rubber particles from tires. Environmental footprint of zinc oxide during production, ecological and economical concerns have prompted the researchers to reduce its use in rubber formulations. Rubber compounds used in the tread, which is the part of the tire in contact with road, must have high wear resistance. In order to determine the wear performance, laboratory wear tests need to be carried out prior to road tests. In the present study, a tread compound recipe based on SBR/BR blend is designed using conventional and composite ZnO materials having CaCO3 as core. Wear behavior of the control and trial compounds prepared with these ZnO materials are tested at room temperature by using Lambourn test instrument. Wear rates and wear energies are calculated using the weight loss data at different slip rates. Based on the wear performance of the trial compounds as well as their cure and mechanical properties, it can be concluded that composite ZnO materials are good candidates to replace the conventional activators in rubber compounds to provide environmental and economic gains.

A Robust Position Estimation Method in the Integrated Navigation System via Factor Graph

Achieving higher accuracy and robustness stands as the central objective in the navigation field. In complex urban environments, the integrity of GNSS faces huge challenges and the performance of integrated navigation systems can be significantly affected. As the proportion of faulty measurements rises, it can result in both missed alarms and false positives. In this paper, a robust method based on factor graph is proposed to improve the performance of integrated navigation systems. We propose a detection method based on multi-conditional analysis to determine whether GNSS is anomalous or not. Moreover, the optimal weight of GNSS measurement is estimated under anomalous conditions to mitigate the impact of GNSS outliers. The proposed method is evaluated through real-world road tests, and the results show the positioning accuracy of the proposed method is improved by more than 60% and the missed alarm rate is reduced by 80% compared with the traditional algorithms.

Construction and optimization of representative actual driving cycles based on the improved autoencoder

In this study, much work has been performed to accurately and efficiently develop representative actual driving cycles. Electric vehicle road tests were conducted and the associated data were gathered based on the manual driving method, and the Changsha Driving Cycle Construction (CS-DCC) method was proposed to achieve systematical construction of a representative driving cycle from the original data. The results show that the refined data exhibit greater stability and a smoother pattern in contrast to the original data after noise reduction by five-scale wavelet analysis. The Gaussian Kernel Principal Component Analysis (KPCA) algorithm is chosen to reduce the dimensionality of the characteristic matrix, and the number of principal components is selected as 5 with a cumulative contribution rate of 85.99%. The average error of the characteristic parameters between the optimized drive cycle and the total data is further reduced from 13.6 to 6.1%, with a reduction ratio of 55.1%. Meanwhile, the constructed driving cycle has prominent local characteristics compared with four standard driving cycles, demonstrating the necessity of constructing an actual driving cycle that reflects localized driving patterns. The findings present a powerful application of artificial intelligence in advancing engineering technologies.

Using millimeter-wave radar to evaluate the performance of dummy models for advanced driving assistance systems test

With the rapid development of intelligent and connected vehicles, the experimental road test for the advanced driving assistance system (ADAS) is dramatically increasing around the world. Considering its high cost and hazardous situations, simulation test based on a dummy model is becoming a promising way for ADAS road test practice to reduce the experiment expanses. This study proposed a methodology for the evaluation of the performance of human and dummies with distinct designed materials based on the data extracted from the Doppler effect of millimeter-wave radar. Echo data of 8 different angles from 0 to 360 degrees, with the an interval of 45 degrees, at the same distance between the test object and the signal source is collected. Meanwhile, the echo energy is collected for correlation modeling and analysis among groups. By evaluating the performance of humans and dummies via statistical analysis, a close correlation was found which results verified the substitutability of the dummy for the ADAS experiment test. The correlation coefficient between human and dummies ranges from 0.75 to 0.93. The support vector machine (SVM) model was developed and fitted to predict the echo energy in diverse environments. The mean average error (MAE) is 5.42–11.42 in the training and testing datasets while root mean square error (RMSE) is 0.43–0.90. The methods developed in the study can simulate the real ADAS road test environment and support future experimental research.

Optimizing Geotechnical Parameters for Sustainable Road Embankments in Flood Disaster-Prone Areas of Bangladesh

The vulnerability of transportation infrastructure to flood disasters in Bangladesh necessitates strategic interventions to optimize geotechnical parameters for sustainable road embankments. This study focuses on a flood disaster-prone region of Bangladesh, aiming to enhance the resilience and sustainability of road infrastructure in the face of recurring floods. The research is driven by the imperative to develop effective engineering strategies that mitigate the impact of floods on road embankments, ensuring safe and reliable transportation networks. The study area is characterized by a complex interplay of geographic and geological factors that contribute to its susceptibility to flooding. Understanding the local geology is crucial for tailoring geotechnical parameters to the specific conditions of the region. Detailed analyses of soil types, their composition, and formation processes provide the foundation for the subsequent optimization efforts. The methodology employed involves systematic sampling along a Road, incorporating both in-situ and laboratory testing. Geotechnical analyses include the evaluation of critical parameters such as California Bearing Ratio (CBR), shear strength, and soil composition. These analyses are instrumental in identifying key geotechnical characteristics that significantly influence the performance of road embankments during flood events. The optimization process encompasses the application of advanced techniques to enhance the resilience of road embankments. The results and subsequent discussions delve into the effectiveness of the optimization strategies in comparison to existing standards and guidelines for road construction. This involves a comprehensive assessment of soil properties, composition, and the outcomes of the optimization process. Finally, the study sheds light on the critical role of geotechnical parameters in developing sustainable road embankments in flood-prone areas. The findings not only contribute to the understanding of the study area but also offer valuable insights for broader applications in flood disaster mitigation in Bangladesh. The research emphasizes the need for a proactive approach to optimize geotechnical parameters, paving the way for resilient and sustainable road infrastructure that can withstand the challenges posed by recurrent floods.

Simulating whole-body vibration for neonatal patients on a tire-coupled road simulator.

Exposure to excessive whole-body vibration is linked to health issues and may result in increased rates of mortality and morbidity in infants. Newborn infants requiring specialized treatment at neonatal intensive care units often require transportation by road ambulance to specialized care centers, exposing the infants to potentially harmful vibration and noise. A standardized Neonatal Patient Transport System (NPTS) has been deployed in Ontario, Canada, that provides life saving equipment to patients and safe operation for the clinical care staff. However, there is evidence that suggests patients may experience a higher amplitude of vibration at certain frequencies when compared with the vehicle vibration. In a multi-year collaborative project, we seek to create a standardized test procedure to evaluate the levels of vibration and the effectiveness of mitigation strategies. Previous studies have looked at laboratory vibration testing of a transport system or transport incubator and were limited to single degree of freedom excitation, neglecting the combined effects of rotational motion. This study considers laboratory testing of a full vehicle and patient transport system on an MTS Model 320 Tire-Coupled Road Simulator. The simulation of road profiles and discrete events on a tire-coupled road simulator allows for the evaluation of the vibration levels of the transport system and the exploration of mitigation strategies in a controlled setting. The tire-coupled simulator can excite six degrees-of-freedom motion of the transport system for vibration evaluation in three orthogonal directions including the contributions of the three rotational degrees of freedom. The vibration data measured on the transport system during the tire-coupled testing are compared to corresponding road test data to assess the accuracy of the vibration environment replication. Three runs of the same drive file were conducted during the laboratory testing, allowing the identification of anomalies and evaluation of the repeatability. The tire-coupled full vehicle testing revealed a high level of accuracy in re-creating the road sections and synthesized random profiles. The simulation of high amplitude discrete events, such as speed hump traverses, were highly repeatable, yet yielded less accurate results with respect to the peak amplitudes at the patient. The resulting accelerations collected at the input to the manikin (sensor located under the mattress) matched well between the real-world and road simulator. The sensors used during testing included series 3741B uni-axial and series 356A01 tri-axial accelerometers by PCB Piezotronics. These results indicate a tire-coupled road simulator can be used to accurately evaluate vibration levels and assess the benefits of future mitigation strategies in a controlled setting with a high level of repeatability.

Vehicle pitch dynamics control using in-wheel motors

Implementation of in-wheel motors brings numerous advantages: decreased overall vehicle weight and manufacture cost, available accurate torque control for better road handling and powertrain high energy efficiency. Even so, when the motor is placed inside the wheel, the unsprung mass of the vehicle increases, leading to a less comfortable ride. This paper puts forward and studies a control system that takes advantage of the vertical component of the driving force provided by an in-wheel motor to enhance ride comfort and handling dynamics. The proportional integral (PI) controller was proposed to minimize the pitch acceleration response of the vehicle. Controller’s gains were optimized using the gradient descent method with sequential quadratic programing algorithm. A co-simulation approach and a dynamic model of the vehicle were presented. Data regarding the vehicle’s measurements was taken from a real car with in-wheel motors. The dynamic model of the car was validated based on the results derived during the road test experiment. Assessing the controller’s effectiveness, a co-simulation approach between the dynamic model and the control system was established. An acceleration test in a straight line showed a 25% increase in ride comfort as measured by the RMS pitch angular vibrations of the sprung mass. The control is attained with no increase in the tire load and the vehicle’s longitudinal dynamics.

A Deep Learning Approach to Optimize the Performance and Power Demand of Electric Scooters under the Effect of Operating and Structure Parameters

The purpose of this study was to enhance electric scooter performance utilizing a novel method consisting of an artificial neural network (ANN) and genetic algorithm (GA) to predict power demand, battery voltage, and identify the optimal performance range. For training, validation, and testing, a dataset comprising 1000 data points for each parameter was extracted from a MATLAB-Simulink model. The ANN application was used to identify the battery voltage and power demand, reflecting the simulated results under varying key input parameters. Additionally, the GA was used to identify the optimal performance after the ANN had been trained. The results showed that the ES can achieve a speed of 28.2 km/h while using an optimal power of 553 W, at a wind velocity of 0 m/s, a slope ratio of 0%, and a wheel diameter of 0.37 m. The achieved results show that the ANN-GA method is appropriate for determining the operating and structural parameters for maximizing the performance of electric scooters. To support the simulated results, an experimental study was carried out with an actual road test along the Taehwa river.

NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network

NOx is one of the main sources of pollutants for motor vehicles. Nowadays, many diesel vehicle manufacturers may use emission-cheating equipment to make the vehicles meet compliance standards during emission tests, but the emissions will exceed the standards during actual driving. In order to strengthen the supervision of diesel vehicles for emission monitoring, this article intends to establish a model that can predict the transient emission characteristics of heavy-duty diesel vehicles and provide a solution for remote online monitoring of diesel vehicles. This paper refers to the heavy-duty vehicle National VI emission regulations and uses vehicle-mounted portable emission testing equipment (PEMS) to conduct actual road emission tests on a certain country’s VI heavy-duty diesel vehicles. Then, it proposes a new feature engineering processing method that uses gray correlation analysis and principal component analysis to eliminate invalid data and reduce the dimensionality of the aligned data, which facilitates the rapid convergence of the model during the training process. Then, a double-hidden-layer BP (Back propagation) neural network was established, and the improved gray wolf algorithm was used to optimize the threshold and weight of the neural network, and a heavy-duty diesel vehicle NOx emission prediction model was obtained. Through the training of the network, the root mean square error (RMSE) of the improved model on the test set between the predicted value and the true value is 1.9144 (mg/s), and the coefficient of determination (R2) is 0.87024. Compared with single-hidden-layer network and double-hidden-layer BP neural network models, the accuracy of the model has been improved. The model can well predict the actual road NOx emissions of heavy-duty diesel vehicles.

School Bus Lighting Effectiveness and Improvements: Results from a Driving Experiment

In Korea, drivers should come to a complete halt and proceed cautiously when encountering a school bus displaying its red warning lights and other safety features, a requirement that is often disregarded in practice. The reason for this might stem from a lack of awareness about the law, and we set out to investigate whether an innovative lighting system employing road projections or VMS could encourage compliance. We found that while 63% of drivers in surveys indicated they would correctly stop when approaching a stopped school bus, in driving experiments, we found that only 18% of drivers did. Our study also uncovered a knowledge gap, with just 53% to 60% of respondents correctly answering basic about the purpose of existing lighting and laws related to school buses. With on-road experiments, when we introduced road projection systems for enhanced non-connected vehicle-to-vehicle (V2V) communication, we found that understanding would increase and compliance could increase by up to 77% (from surveys) and 93% (in road tests); these findings underscore the potential of road projections or potentially VMS as effective V2V tools for enhancing road safety in proximity to school buses.

Microcracking treatment mechanism of semi-rigid base asphalt pavement with discrete-continuous coupling simulation

The microcracking treatment employs the falling weight to break the semi-rigid base into interlocking blocks to prevent reflective cracks in asphalt pavement. To study the microcracking treatment mechanism, the semi-rigid base microcracking simulation was performed through a discrete-continuous coupling model. After that, the coupling model was verified through a test road in Shaanxi Province, China, and the impact of convex points properties was studied. The results showed that the coupling model exhibited a peak compressive stress calculation error of less than 10% and effectively simulated the semi-rigid base cracking. Besides, the properties of convex points significantly impacted cracking. An appropriate area of convex points could effectively reduce upper layer cracks while inducing a greater number of cracks in the lower layer of the base. The three-convex falling weight produced significant cracks, and the semi-rigid base was microcracked into interlocking blocks. With convex points inner tangent circle radius increasing and length ratio decreasing, the number of upper layer cracks in the base grew while the number of lower layer cracks decreased. However, an increased length ratio could improve the horizontal extension capability of cracks. These findings contribute to improving the microcracking effect in engineering. After trail impact, if there is no apparent cracking on the semi-rigid base surface, it is recommended to use a three-convex point falling weight, slightly reduce the convex points area, and increase the inner tangent circle radius and length ratio. In the case of severe cracking, it is recommended to increase the number of convex points and their area, decrease the inner tangent circle radius and length ratio, or use a flat-bottomed falling weight.

Theoretical basis for conducting road tests for emissions of harmful substances in real driving conditions with vehicles of category N3

The article discusses the theoretical basis for testing vehicles of categories N3 in real driving conditions using a portable (portable) system for measuring emissions of harmful substances from exhaust gases. Bench and road tests were analyzed. Based on a study carried out in accordance with regulatory documents to confirm the type of vehicle and for vehicles in operation, a procedure for selecting engines and an acceptance card, requirements and test conditions are proposed. Keywords internal combustion engine, vehicle, exhaust gases, portable emission measurement system, test

A new method for assessing the reliability of multi-purpose vehicles based on test results in extreme conditions

The article presents materials on methods and means of reducing testing time and improving their quality in terms of accuracy, reliability and reproducibility of results, regardless of changing road, soil and climatic conditions using a new reliability indicator: instead of average time between failures — time to 1st failure with probability of failure-free operation is 0.95. A method is presented for assessing the reliability of test results with different volumes of mileage in extreme road conditions by comparing normal test data with a reference scientific and methodological platform. Keywords multi-purpose vehicles, road tests, reliability, failure, method, reliability assessment

A test bench for the assessment of flow meters accuracy for fuel consumption measurement in highly dynamic drive cycle tests

The transport sector is one of the most significant contributors in terms of NOx, particulate matter and net CO2 emission. In order to mitigate this environmental impact, severe homologation limits have been introduced by the different regional Authorities. Both pollutant emissions and CO2 production of vehicles are measured during standardized laboratory test cycles and road tests, both characterized by relatively abrupt powertrain load and fuel rate transients. This dynamic approach results in severe issues in terms of fuel flow measurement capabilities as flow meters are typically calibrated in steady fuel flow conditions. In the present activity, an experimental test bench was developed with the aim of offering a flexible and affordable test system to evaluate the performances of different flow meters in dynamic conditions. The test bench consists of a complete fuel injection system managed to reproduce an assigned cycle; by this approach the highly-dynamic engine-like fuel flow rate is replicated, offering the opportunity to compare the accuracy of the flow meter under test with both a master Coriolis-type flow meter and the nominal fuel flow time-profile. This bench allows the execution of flow meter accuracy tests in controlled operating conditions with reduced efforts with respect to conventional vehicle test benches and with a high reproducibility level.

MODERN PRACTICE OF USING NON-DESTRUCTIVE TESTING METHODS IN TIRE PRODUCTION

Improving the competitiveness of Russian tires requires improving the organization of the quality control system in production. The purpose of the article is to analyze the current state of the application of tire quality control methods in production. A common way to diagnose tires in the Russian industry is random control by road tests and bench tests, leading to the destruction of the product. At the same time, non-destructive testing is widely used in the global tire industry. Tire structural strength, material homogeneity, stiffness and geometric characteristics of tires are checked on test benches, which are the result of defects that can be diagnosed in a “non-invasive” way, i.e. non-destructive testing methods. Visual-optical, eddy current, ultrasonic and radiation methods of non-destructive testing are the most optimal for diagnosing continuity and obtaining the physical and mechanical characteristics of rubber and cord. Continuous flaw detection control of tires allows for the lowest cost diagnostics of hidden defects and timely improvement of the technological process. The interferometric method of non-destructive testing can be considered as an intelligent predictive diagnostics of hidden defects, bubbles, delamination, foreign inclusions in tires with nanometer accuracy. Patents and practical developments of Russian inventors in the field of interferometry testify to the high potential of mass production of domestic flaw detectors for their use in tire factories.