Test Vehicle Research Articles

Experimental analysis of the kinematics in the elastic collision between two bodies during the contact time

The elastic collision between two bodies is a fleeting event challenging to observe due to its infinitesimally short contact time, usually lasting mere hundredths or even thousandths of a second. This brief duration poses significant challenges for accurately measuring velocities and impulsive forces and establishing representative functions. Consequently, this study aims to address these challenges. Experimental measurements of velocity, acceleration, and force changes during the contact period are crucial for validating theoretical models and functions that accurately represent the dynamics of collisions under realistic conditions. These measurements are critical in optimizing the activation response of airbag and restraint systems in vehicles and are fundamental in reconstructing physical scenarios of accidents. The experiments were conducted in a practical computer-assisted laboratory, utilizing wireless sensors embedded within the test vehicles and positioned on a low-friction track. The collision setup was designed to be horizontal and frontal, ensuring that the bodies involved did not undergo permanent deformations. The primary methodology adopted in this analysis integrates both quantitative and qualitative approaches, focusing on collecting and analyzing numerical data to identify patterns and establish mathematical relationships between variables. This integrated approach offers a more comprehensive understanding of the kinematics of colliding vehicles during the contact period.

Energy management strategy of series–parallel hybrid transmission integrating map information and personalized driving characteristics

The integration of multi-source intelligent and connected information during a driving trip, along with its online application to globally optimized energy management strategies, has emerged as a crucial technical approach for enhancing the energy-saving effectiveness of hybrid transmissions. However, the action mode of such information and the optimization calculation efficiency of existing dynamic programming (DP) methods limit the online application of the aforementioned strategies with global optimization capabilities. To address these problems, the present study proposes a hierarchical energy management strategy that follows the reference trajectory of the battery state of charge (SoC) and comprehensively considers the multi-source information on the driving trip. First, a global speed prediction model based on personalized driving characteristics is proposed to obtain an accurate driving cycle input for the space-domain DP method. Second, the aforementioned tasks as well as the working-mode decision of the hybrid transmission and the multi-power-source torque distribution calculation tasks are deployed in the dual-core controller. Finally, the hierarchical energy management strategy is verified via vehicle testing. Compared with the DP strategy, the proposed strategy has an energy-saving potential of 4.17% that is yet to be realized. Furthermore, compared with the charge-depleting and charge-sustaining (CD–CS) strategy, the proposed strategy reduces fuel consumption by 0.38 L per 100 km, and its energy-saving effect is significant. This study is the first to apply the DP method to the vehicle controller, thereby facilitating the online application of energy management strategies with global optimization capabilities.

IoT Architecture for Vehicle Pollutant Gas Emission Monitoring and Validation through Machine Learning

This study proposes an IoT architecture for monitoring emissions of polluting gases in vehicles, in response to the growing concern about air pollution and global warming. The architecture is based on a node equipped with DHT22, MQ9, and MQ135 sensors to capture temperature, humidity, and gas emissions, respectively. This node effectively communicates through the LTE network to send the data to the ThingSpeak platform. An analysis of CO2, CO, and CH4 pollution levels is conducted using the collected data. These data are validated through the technical review of a test vehicle. Subsequently, an Artificial Neural Network (ANN) is trained using a specific database of CO2 emissions from vehicles in Canada. As a result, a high R2 of 99.2% is achieved, along with low values of RMSE and MSE, indicating that the model is making accurate predictions and fits well to the training data. The ANN aims to predict CO2 emissions and verify CO2 data from the IoT network. The architecture demonstrates its capability for real-time monitoring and its potential to contribute to pollution reduction.

The Fabrication and Testing of Unmanned Aerial Vehicle (UAV) Wing Structure Using 3D Printing and Carbon Fibre Skinning Method

This study examines the efficacy of integrating 3D printing with carbon fibre skinning to construct wing structures for Unmanned Aerial Vehicles (UAVs). The primary objective is to enhance the structural integrity and material characteristics. The study performs static load tests, which demonstrate that wings reinforced with carbon fibre lamination can endure a substantially greater maximum stress of 29.43 N, in contrast to 14.391 N for wings produced only by 3D printing. The carbon fibre laminated wings have a greater load-bearing capability, as evidenced by the safety factor study. This analysis reveals that the permitted loads for carbon fibre laminated wings (19.61 N) are higher compared to 3D printed wings (9.594 N). The tensile testing of the specimens reveals that carbon fibre laminated wings demonstrate enhanced ductility, stiffness, and yield strength. Additionally, they withstand greater levels of maximum stress and strain, except for a slightly higher stiffness seen in 3D printed wings. This research highlights that carbon fibre laminated wings are typically more durable and versatile for many uses, although 3D printed wings may be favoured in situations when greater rigidity is necessary. In conclusion, the study effectively showcases the advantages of combining 3D printing with carbon fibre skinning in the construction of UAV wings. This research significantly contributes to the progress of UAV design and provides fresh insights in the fields of aeronautical engineering and materials science. It is particularly valuable for applications that require UAVs with exceptional performance, durability, and reliability.

A New CPX Drum Test to Obtain Sound Pressure Levels of Tyre Noise for Type Approval

The primary cause of noise from vehicular traffic while travelling at speeds over 30 km/h is tyre/road interaction. To reduce this noise source, tyre/road sound emissions research has been carried out using different approaches. Most of this research has been centred around track tests, leading to the development of various track and road-based methods for evaluating tyre/road noise emissions. The CPX (Close-Proximity), along with the CPB (Controlled Pass-By), the CB (Coast-By) and the SPB (Statistical Pass-By), methods are the most common ones. Nevertheless, since Reg. (EC) 1222/2009 came into force, only the CB method, defined in Reg. (EC) 117/2007, can be used to obtain tyre/road noise emission type approval values in Europe. However, current track test methods have important limitations, such as the variability of the results depending on the test track or the test vehicle, the repeatability, the influence of environmental variables or, the main aspect, the limitation of the registered magnitude in these tests, which is the sound pressure level. The Alternative Drum test method (A-DR) was developed in 2015 in order to avoid these disadvantages. However, it involves a complex and time-consuming microphone array for each test. With the purpose of improving the A-DR test method, a new methodology based on drum tests, the ISO 11819-2 and the ISO 3744 standards, was developed. This paper describes the new Alternative CPX Drum test method (A-CPX-DR) and validates it by testing several tyres according to the CB, the A-DR and the A-CPX-DR test methods and comparing their results. This research has demonstrated that all three methods have equivalent sound spectra and obtain close equivalent sound pressure levels for type approval of tyres in the EU, while drum tests have shown greater accuracy. For both reasons, the new A-CPX-DR methodology could be used for tyre/road noise emission type approval in a more precise and cheaper way.

Safety and Effectiveness of a Novel Color Corrector Serum for Causing Temporary Changes to Tooth Shade: A Randomized Controlled Clinical Study.

Tooth color is a major driver of facial esthetics. While permanent changes in tooth shade can be achieved by bleaching and restorations, there is a need for cosmetic products that can cause reversible color changes. This randomized controlled clinical study assessed the effectiveness and safety of a novel color-correcting product (Hismile™ V34 Color Corrector Serum™) versus a placebo (vehicle control lacking the color-change dyes). A single-center, randomized, controlled, examiner-blind, two-group, parallel design, single-use study design was followed. The test products were applied on a cotton bud for 30 s, and then, rinsed off. Tooth shade for maxillary central incisors was measured at baseline, immediately, and at 30 and 60 min, using the Vita Bleachedguide 3D-Master® Shade Guide and the EasyShade Advanced 4.0 spectrophotometer (for determining values of L*a*b*). The subjects (N = 60) had a baseline shade of 1M2 (rank 9) or darker. A single application of the test product resulted in an immediate and significant (p < 0.001) three shade improvement (26.2%) according to the shade guide, and the same significant benefits extended to 30 and 60 min. The placebo product did not alter tooth shade (p = 0.326). These changes were accompanied by significant improvements in the L value (whiteness) up to 30 min, and a reduction in b* (yellowness) for up to 60 min. Two-thirds of subjects using the test product stated in a survey that their teeth appeared both whiter and brighter. No safety issues arose from the use of the test product or vehicle control. These results indicate that using a color corrector can achieve worthwhile changes to tooth shade for up to 60 min.

Optimization of embedded cooling for hotspots based on compound plate thermal spreading model

Heat fluxes of GaN-based high electron mobility transistors (HEMTs) can reach dozens of kilowatts per square centimeter, and the heat is generated only within a small area with feature size of micrometer to millimeter length scales, which poses a huge challenge for thermal management. In this study, an embedded microfluidic cooling solution is proposed to dissipate heat from the hotspots, and thermal test vehicles are fabricated using the Micro-Electro-Mechanical System (MEMS) process. Cooling performances of hotspots with sizes ranging from 40 × 40 μm2 to 500 × 500 μm2 and varying locations are demonstrated. Thermal resistances of the test samples are analysed and the heat transfer coefficient can achieve 1.5 × 105 W/(m2∙K) using embedded microchannel cooling. We propose a compound plate spreading thermal resistance model to demonstrate the effect of the dielectric layer and the size of the heat source on heat dissipating capability of the microfluidic cooling system. Based on the thermal spreading model, when the heat source is small, integrating high thermal conductivity materials near the heat source can reduce its total thermal resistance by two orders of magnitude. By balancing the heat spreading resistance with the convective resistance of microchannel cooling, we find that ∼1 mm can be considered as the critical length for distinguishing the primary thermal management approach for different sized hotspots. This paper provides useful design guidelines for embedded microchannel cooling of devices with localized heat generation patterns, such as HEMT devices.

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.

A modeling method for two-dimensional two-wheeler driving behavior during severe conflict interaction at intersections

The safety of two-wheelers is a serious public safety issue nowadays. Two-wheelers usually have severe conflict interaction with vehicles at intersections, such as running red lights, which is very likely to cause traffic accidents. Therefore, a model of two-wheeler driving behavior in conflicting interactions can provide guidance for traffic safety management on one hand, and can be used for the development and testing of autonomous vehicles on the other. However, the existing models perform poorly when interacting with vehicles. To address the problems, this paper proposes a modeling method (an improved social force model, ISFM) for two-dimensional two-wheeler driving simulation for conflict interaction at intersections. Based on analysis of naturalistic driving study data, when two-wheelers encounter with vehicles, their driving intentions and trajectories can be categorized into two groups, which are yielding and overtaking. Therefore, the vehicle-related social forces are designed to be a set of two forces rather than a repulsion force in original SFM, which is a yielding force based on the relative distance between the two-wheeler and the vehicle, and an overtaking force based on the velocity of the two-wheeler itself. This opens up the possibilities for modeling the multi-modal driving intention of two-wheelers encountering with cross traffic. Based on ISFM, a bicycle model, a powered two-wheeler (PTW) model and a model of a group of PTWs, are then constructed. Compared to the original SFM, ISFM increases the precision of driving intention prediction by 19.7 % (yielding situation) and 25.0 % (overtaking situation), and reduces the root mean square error between simulated and actual trajectories by 7.8 % and 14.8 % on the bicycle model and the PTW model, respectively. Meanwhile, the model of a group of PTWs also performs well. Finally, the results of ablation experiments also validate the effectiveness of the social force designed based on velocity.

Allergic Contact Dermatitis to Topical Preparations Containing Minoxidil: A Systematic Review and Individual Participant Data Meta-Analysis.

Topical minoxidil is generally well tolerated, yet there have been a few reports of allergic contact dermatitis (ACD) confirmed through patch testing. This systematic review and individual participant data meta-analysis sought to elucidate the primary allergens in patients exhibiting ACD in response to topical minoxidil formulations and to ascertain the appropriate testing concentrations and vehicles of minoxidil itself. A comprehensive search was conducted across the PubMed, Scopus, and Embase databases utilizing the keywords "minoxidil" and "contact dermatitis," or "contact allergy," or "contact eczema." Studies documenting ACD in patients using topical minoxidil confirmed by patch testing were deemed eligible. Our analysis included 46 studies encompassing 99 patients with patch-test-confirmed ACD to minoxidil-based topical treatments. The majority of these patients (93.9%) were exposed to minoxidil without additional active components. Minoxidil itself was identified as the primary allergen in 74.7% of the patients, with propylene glycol being the next most common allergen at 17.1%. Other allergens identified included estradiol, butylene glycol, methylchloroisothiazolinone/methylisothiazolinone, canrenone, and latanoprost. The most effective concentration was found to be 2% minoxidil in propylene glycol, which yielded a 100% positivity rate. The findings indicate that minoxidil is the predominant allergen in ACD reactions to its topical formulations, followed by propylene glycol. For the accurate diagnosis of ACD related to minoxidil, patch testing with 2% minoxidil in propylene glycol is recommended, as are separate tests for propylene glycol and other potential allergenic ingredients.

Coupled Vibration of a Vehicle Group–Bridge System and Its Application in the Optimal Strategy for Bridge Health Monitoring

The accuracy of bridge performance monitoring and evaluation is easily affected by unfavorable factors such as vehicle coupling and test noise. In order to accurately evaluate the dynamic response and health monitoring threshold of the bridge under different operating conditions, a time-varying dynamic vehicle group model including the main uniform mass and the coupling mass was established, and the influence of road roughness was considered in the coupling equation. A bridge monitoring strategy considering signal noise ratio and vehicle–bridge interaction was proposed, and the effectiveness of the monitoring strategy was verified by taking a simple supported beam as an example. The results showed that the proposed time-varying dynamic vehicle group model could accurately consider the influence of road roughness and estimate the threshold of health monitoring, and the proposed bridge monitoring strategy could filter out a large amount of low signal-to-noise ratio or meaningless data, thus saving computing resources and realizing the lightweight safety monitoring of bridges.

Performance Analysis of Liquid-to-Air Heat Exchanger of High-Power Density Racks

Abstract The ability of traditional room-conditioning systems to accommodate expanding information technology loads is limited in contemporary data centers (DCs), where the storage, storing, and processing of data have grown quickly as a result of evolving technological trends and rising demand for online services, which has led to an increase in the amount of waste heat generated by IT equipment. Through the implementation of hybrid air and liquid cooling technologies, targeted, on-demand cooling is made possible by employing a variety of techniques, which include but are not limited to in-row, overhead, and rear door heat exchanger (HX) cooling systems. One of the most common liquid cooling techniques will be examined in this study based on different conditions for high-power density racks (+50 kW). This paper investigates the cooling performance of a liquid-to-air in-row coolant distribution unit (CDU) in test racks containing seven thermal test vehicles (TTVs) under various conditions, focusing on cooling capacity and HX effectiveness under different supply air temperatures (SAT). This test rig has the necessary instruments to monitor and analyze the experiments on both the liquid coolant and the air sides. Moreover, another experiment is conducted to assess the performance of the CDU that runs under different control fan schemes, as well as how the change of the control type will affect the supply fluid temperature and the TTV case temperatures at 10%, 50%, and 100% of the total power. Finally, suggestions for the best control fan scheme to use for these systems and units are provided at the conclusion of the study.

Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model’s prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions.

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.

Lightweight environment sensing algorithm for intelligent driving based on improved YOLOv7

AbstractAccurately and quickly detecting obstacles ahead is a prerequisite for intelligent driving. The combined detection scheme of light detection and ranging (LiDAR) and the camera is far more capable of coping with complex road conditions than a single sensor. However, immediately afterward, ensuring the real‐time performance of the sensing algorithms through a significantly increased amount of computation has become a new challenge. For this purpose, the paper introduces an improved dynamic obstacle detection algorithm based on YOLOv7 (You Only Look Once version 7) to overcome the drawbacks of slow and unstable detection of traditional methods. Concretely, Mobilenetv3 supplants the backbone network utilized in the original YOLOv7 architecture, thereby achieving a reduction in computational overhead. It integrates a specialized layer for the detection of small‐scale targets and incorporates a convolutional block attention module to enhance detection efficacy for diminutive obstacles. Furthermore, the framework adopts the Efficient Intersection over Union Loss function, which is specifically designed to mitigate the issue of mutual occlusion among detected objects. On a dataset consisting of 27,362 labelled KITTI data samples, the improved YOLOv7 algorithm achieves 92.6% mean average precision and 82 frames per second, which reduces the Model_size by 85.9% and loses only 1.5% accuracy compared with the traditional YOLOv7 algorithm. In addition, this paper builds a virtual scene to test the improved algorithm and fuses LiDAR and camera data. Experimental results conducted on a test vehicle equipped with a camera and LiDAR sensor demonstrate the effectiveness and significant performance of the method. The improved obstacle detection algorithm proposed in this research can significantly reduce the computational cost of the environment perception task, meet the requirements of real‐world applications, and is crucial for achieving safer and smarter driving.

Study on Discharge Characteristic Performance of New Energy Electric Vehicle Batteries in Teaching Experiments of Safety Simulation under Different Operating Conditions

High-voltage heat release from batteries can cause safety issues for electric vehicles. Relevant scientific research work is carried out in the laboratory. The battery safety of laboratory experiments should not be underestimated. In order to evaluate the safety performance of batteries in the laboratory testing of driving conditions of electric vehicles, this paper simulated and compared the discharge characteristics of two common batteries (lithium iron phosphate (LFP) battery and nickel–cobalt–manganese (NCM) ternary lithium battery) in three different operating conditions. The operating conditions are the NEDC (New European Driving Cycle), WLTP (World Light Vehicle Test Procedure) and CLTC-P (China light vehicle test cycle) for normal driving of electric vehicles. LFP batteries have a higher maximum voltage and lower minimum voltage under the same initial voltage conditions, with a maximum voltage difference variation of 11 V. The maximum current of WLTP is significantly higher than NEDC and CLTC-P operating conditions (&gt;20 A). Low current discharge conditions should be emulated in teaching simulation and experiments for safety reasons. The simulation data showed that the LFP battery had good performance in maintaining the voltage plateau and discharge voltage stability, while the NCM battery had excellent energy density and long-term endurance.

An In-Depth Study of Ring Oscillator Reliability under Accelerated Degradation and Annealing to Unveil Integrated Circuit Usage.

The reliability and durability of integrated circuits (ICs), present in almost every electronic system, from consumer electronics to the automotive or aerospace industries, have been and will continue to be critical concerns for IC chip makers, especially in scaled nanometer technologies. In this context, ICs are expected to deliver optimal performance and reliability throughout their projected lifetime. However, real-time reliability assessment and remaining lifetime projections during in-field IC operation remain unknown due to the absence of trustworthy on-chip reliability monitors. The integration of such on-chip monitors has recently gained significant importance because they can provide real-time IC reliability extraction by exploiting the fundamental physics of two of the major reliability degradation phenomena: bias temperature instability (BTI) and hot carrier degradation (HCD). In this work, we present an extensive study of ring oscillator (RO)-based degradation and annealing monitors designed on our latest 28 nm versatile array chip. This test vehicle, along with a dedicated test setup, enabled the reliable statistical characterization of BTI- and HCD-stressed as well as annealed RO monitor circuits. The versatility of the test vehicle presented in this work permits the execution of accelerated degradation tests together with annealing experiments conducted on RO-based reliability monitor circuits. From these experiments, we have constructed precise annealing maps that provide detailed insights into the annealing behavior of our monitors as a function of temperature and time, ultimately revealing the usage history of the IC.

Emissions from heavy-duty diesel, natural gas, and diesel-hybrid electric vehicles – Part 1. NOx, N2O and NH3 emissions

The current study characterized nitrogen oxide (NOx), nitrous oxide (N2O), and ammonia (NH3) emissions from 14 heavy-duty vehicles from different vocations (school bus, transit bus, refuse hauler, delivery vehicle, and goods movement vehicle) over different drive cycles using a chassis dynamometer. Test vehicles with engines ranging in model year from 2009 to 2018 and including diesel and compressed natural gas (CNG) vehicles. The diesel vehicles included both vehicles without selective catalytic reduction (no-SCR) and with SCR certified to the 0.2 g per brake horsepower-hour (g/bhp-hr) [0.27 g/kilowatt-hour (kW-hr)] NOx emissions standard that were operated with diesel fuel, with a subset tested on hydrotreated vegetable oil (HVO). The CNG vehicles were all three-way catalyst (TWC) equipped and certified to either the 0.2 or 0.02 g/bhp-hr NOx standard [0.27 or 0.027 g/kW-hr]. This study is part of a larger project that included over 200 in-use heavy-duty vehicles, which is one of the most extensive studies of emissions from modern heavy-duty vehicles to date. NOx, NH3, and N2O emissions varied depending on the vocation and the technology. Average NOx emissions for the Urban Dynamometer Driving Schedule (UDDS) cycle over all vehicles ranged from 0.003 to 6.16 g/bhp-hr [0.004 to 8.25 g/kW-hr] and from 0.02 to 17.2 g/mile [0.012 to 10.7 g/km], with the diesel vehicles showing the highest emissions, the 0.2 g/bhp-hr certified CNG vehicles showing lower emissions, and the 0.02 g/bhp-hr certified CNG vehicles showing very low emissions. NOx emissions over other different cycles were generally in the same range as those for the hot start UDDS, with the highway cycles generally showing the lowest emissions. CNG vehicles had relatively high NH3 emissions ranging from 0.13 to 0.34 g/bhp-hr [0.17 to 0.46 g/kW-hr] and from 0.44 to 0.97 g/mile [0.27 to 0.6 g/km] over the hot start UDDS compared to the SCR-equipped diesel vehicles due to NH3 formation across the TWC. N2O emissions did not show consistent trends and were near/at the detection limits for a number of the vehicles.

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.

Battery Modeling for Emulators in Vehicle Test Cell

Battery Modeling for Emulators in Vehicle Test Cell