On-board Data Acquisition System Research Articles

Hybrid storage system management for hybrid electric vehicles under real operating conditions

This study proposes the use and management of hybrid storage systems to power hybrid electric vehicles with the aim of reducing the negative effects of high current values on battery cycling life. Findings derive from a case study on a commercial plug-in hybrid electric vehicle with battery pack operations supported by a Lithium-ion Capacitor module. Specific experimental laboratory activities focused on the parametrization and validation of an equivalent circuit model representing the behavior of Lithium-ion capacitors for integration into the overall vehicle model (designed calibrated and validated on the basis of data from on-board data acquisition and measurement systems). The simulation platform was used to test various energy management strategies for the hybrid storage system supplying the vehicle during real driving cycles characterized by different operating conditions and driving styles. Results highlight the benefits of using the considered management strategies in terms of battery durability, allowing performance comparison for different driving routes and styles.

Cosimulation of a direct-acting riser-tensioner system – validation with field measurements and sample simulations

In this paper, the cosimulation methodology, presented in earlier works by the authors, is extended to a full scale riser-tensioner system, with the semi-submersible platform and the riser stack being modelled in the ocean engineering software, OrcaFlex, and the hydro-pneumatic tensioner being modelled in the multiphysics software, SimulationX. An interface file coded in Python is used to couple the two models to run the cosimulation. Field measurements from an on-board data acquisition system, mounted on an operational semi-submersible drilling platform, is used to benchmark model performance. The motion time–history of the platform, available in the field data, is used to specify heave motions of the model platform. A planned disconnect sequence is simulated, and the results are compared with field measurements. Subsequently, sample simulations are presented to demonstrate the possibilities of the proposed co-simulation methodology, including a simulation of the whole disconnect procedure, covering the connected, pull-off, disconnected, and soft hang-off phases of the system.

What factors contribute to e-scooter crashes: A first look using a naturalistic riding approach

Introduction: Shared dockless electric scooters (e-scooters) are a popular shared mobility service providing an accessible last-mile transportation option in urban and campus environments. However, city and campus stakeholders may hesitate to introduce these scooters due to safety concerns. While prior e-scooter safety studies have collected injury data from hospitals or riding data under controlled or naturalistic conditions, these datasets are limited and did not identify risk factors associated with e-scooter riding safety. To address this gap in e-scooter safety research, this study collected the largest naturalistic e-scooter dataset to date and quantified the safety risks associated with behavioral, infrastructure, and environmental factors. Method: A fleet of 200 e-scooters was deployed on Virginia Tech’s campus in Blacksburg, VA for a 6-month period. Fifty were equipped with a unique onboard data acquisition system, using sensors and video to capture e-scooter trips in their entirety. The resulting dataset consisted of 3,500 hours of data spanning over 8,500 trips. Algorithms were developed to identify safety critical events (SCEs) in the dataset and analyses were conducted to determine the prevalence of various SCE risk factors and associated odds ratios. Results: Results from this study indicate that infrastructure-related factors, behavior of e-scooter riders and other actors, and environmental factors all contributed to the SCE risk for e-scooter riders in Virginia Tech’s pedestrian-dense campus environment. Conclusions: To help mitigate unsafe rider behavior, educational outreach programs should quantify the significant risks associated with infrastructure, behavioral, and environmental risk factors and provide clear recommendations to riders. Improved infrastructure maintenance and design may also improve safety for e-scooter riders. Practical Applications: The infrastructure, behavioral, and environmental risk factors quantified in this study can be applied by e-scooter service providers, municipalities, and campus administrators to develop mitigation strategies to reduce the safety risks associated with e-scooter deployments in the future.

Data Acquisition System for Preventive Maintenance of Equipment in the Urban Tunnels

Urban Tunnel construction for supporting the Under-ground Transit Solutions (UTS) namely Metros and Subways in highly densely populated locations involves huge power requirements. This power requirement is normally met by the power supplied by the power agencies for the over the ground requirements. However, in the cases of underground work at the drilling site or de-watering sites we need to use the smaller portable generator sets. These generator sets generate a lot of harmful gases and CO2 and in-cases of over loading lead to short circuits and also fire. In the cases of prolonged tunneling times these generators remain inside the tunnel and are not serviced on time leading to gen-sets often going out of order leading to loss of work and may also cause accidents. Regular maintenance enhances the lifespan of the generators. This paper takes the approach to provide a solution to the preventive generator maintenance problems which would take care of reliability issues. In the proposed design we monitor the vital parameters of the gen-sets like fuel levels, temperatures, loads (current and voltage), running time, last servicing time, average fuel consumption, last serviced dates, battery voltages, Air filter life etc. This information is then collected by an on-board Data Acquisition System (DAS) which is then passed on to Cloud via a Wi-Fi connection. Once this information is made available on the cloud, we can apply the Machine Learning Algorithms to predict the failure rates of the generators and calculate the RUL - Remaining Useful Life.

Empirical Evaluation of Lane Changing Following Gap Distance on Expressway

Lane changing (LC) is an essential aspect of driving behaviour on expressways, which impacts on traffic efficiency and safety. Therefore, a wrongful execution of LC, especially the wrong choice following distance before the LC manoeuvre. This could lead to road crashes, mostly in the form of rear-end, sideswipe, or angled crash. This research assesses the characteristics of drivers’ following gap distance during LC. The study was performed on a segment along Kuala Lumpur – Seremban expressway. Data were collected along the selected segment using an instrumented test vehicle approach. A passenger car was instrumented with a Video Velocity Box (VBox), which an on-board data acquisition system used for video recording of field traffic events. Hence, VBox attached to the test vehicle was used for recording the following gap distance during LC manoeuvres in the field. After that, the video records were played back on a computer to estimate the gap distance. A total of 174 following incidences were observed in this work. Finding from the study revealed that the following gap distance before LC of the sampled motorists ranges from 4.89 to 81.15 m with a mean and standard deviation of 18.47 and 12.16 m, respectively. Furthermore, out of the 174 sampled drivers, about 86.7% recorded following gap distances in the range of 10 to 30 m, implying that the majority of the motorists accept short gaps while executing LC. It was also found that the space gap is regarded as an essential variable than the time gap because as drivers travel at desired speeds, they tend to be guided by the available space rather than time.

Calibration and Validation of Lane Changing Following Gap Distance in VISSIM

Lane change (LC) is an essential feature of expressway driving behavior that affects traffic efficiency and safety. Thus, a wrongful execution of LC, could be due to the wrong choice of gap distance before the LC manoeuvre. This might lead to road crashes, mostly in form rear-end, sideswipe, or angled collision. This research evaluates the characteristics of drivers’ Following Gap Distance (FGD) during LC using VISSIM traffic simulation program. Data were collected along a representative segment along with Kuala Lumpur – Seremban expressway for two days using an instrumented vehicle approach. A passenger car was instrumented with a Video Velocity Box (VBox), an on-board data acquisition system used for video recording of the road’s traffic event. A total of 75 and 20 following incidences of lane changing were observed in first and second day, respectively. Standstill Distance (CC0), Gap Time (CC1) and Following Distance Oscillation (CC2) were used as calibration parameters in VISSIM model. FGD was used as a measure of performance for the model. The data from second day was used for model validation. The findings indicate that the model was well calibrated and validated. Therefore, it can be used for modelling various scenarios involving LC on the expressway.

Simultaneous, Robot-Compatible γ-Ray Spectroscopy and Imaging of an Operating Nuclear Reactor

The design and test of a robot-compatible, radiation detection instrument providing simultaneous $\gamma $ -ray imaging and $\gamma $ -ray spectroscopy is described. The sensing system comprises a cerium bromide inorganic scintillation detector and a cylindrical, lead slot collimator that is configured with a robot-compatible, on-board data acquisition system. The mount for the sensor is a lightweight, bespoke 3-axis gimbal actuated by servos for pan, tilt and rotation of the collimator for imaging capability. This paper discusses the integration of this relatively low-cost radiation detection apparatus with a commercially available, Robot-Operating-System-controlled robotic platform (a Clearpath Robotics™ Jackal). The detection system is compliant with the power and mass payload constraints of the robot. Its performance has been evaluated by means of two practical examples: a) measurements in a laboratory environment to assess the ability of the system to resolve two caesium-137 point sources, and b) deployment at the Jožef Stefan Institute TRIGA Mark II research reactor to assess the ability of the system to characterise the $\gamma $ -ray emission at 1 kW from a horizontal tangential beam port in the reactor hall and from the reactor sample pool above the core.

Performance evaluation of a multi-rotor unmanned agricultural aircraft system for chemical application

Unmanned agricultural aircraft system (UAAS) has been widely employed as a low-cost and reliable method to apply agrochemicals to small agricultural fields in China. The performance of battery-powered multirotor UAAS has attracted considerable attention from manufacturers and researchers. The objective of this research was to design a UAAS equipping with a data acquisition system, to characterize its chemical application performance based on droplet deposition data and optimize the operating parameters. Each test was repeated three times to assess the reliability of the spraying system. Various flight parameters were also evaluated. The optimal spray pressure for the XR8001 and XR8002 (TeeJet, Wheaton, IL, USA) nozzles was found to be 300 kPa, and the latter nozzle had a higher droplet deposition rate and spray volume. Spray volume was not significantly affected by the flight speed or droplet density, and was negatively correlated with the nozzle pressure. The results of this study provide a basis for improving the efficiency of UAAS chemical application systems in terms of large-scale application. Keywords: spray characterization, unmanned agricultural aircraft system, aerial sprayer, onboard data acquisition system, effective swath width, flight parameters, chemical application, performance evaluation DOI: 10.25165/j.ijabe.20211404.6194 Citation: Zhu H, Li H Z, Adam A P, Li L J, Tian L. Performance evaluation of a multi-rotor unmanned agricultural aircraft system for chemical application. Int J Agric & Biol Eng, 2021; 14(4): 43–52.

Study on the Energy Parameters of a Vehicle while Driving Using DEWESOFT-101 Minitaur

The aim of the paper is to study the opportunities offered by an on-board data acquisition system (DAQ) in collecting, monitoring and controlling vehicle operating parameters. The testing system is able to obtain information concerning the energetic and dynamic parameters. It also has a built-in GPS system that allows the correlation between the information acquired with the route traveled. The data is collected from the vehicle in real-driving conditions and is processed with the aim of analyze it.

The Onboard Data-Acquisition System of Research Aircraft for Studying Atmospheric Processes

The onboard data-acquisition system for the new-generation Yak-42D Roshydromet research aircraft is described. The research aircraft is intended for investigating the atmosphere and the underlying surface. The large quantity of scientific equipment installed onboard requires that the tasks of acquisition, synchronization, transmission, and storage of aircraft observation data be performed. The onboard measuring and computing system developed for the research aircraft and the local computer network are described. The solution to the problem of synchronizing the data of high-frequency measurements is presented. The possibilities of using the local network for integrating the measurement systems on board the research aircraft are shown. The principles of organization of a unified data-acquisition system can be used to build measuring systems for any class of mobile laboratory.

Online system identification of mini cropped delta UAVs using flight test methods

The current manuscript presents the longitudinal and lateral directional online parameter estimation of two unmanned aerial vehicles (UAVs) using sequential Least Squares formulation in frequency domain. The two fixed wing UAVs share a similar cropped delta planform and differ in their cross sectional geometries, one with a rectangular and the other being a reflex airfoil cross sections respectively. Recursive Fourier Transform algorithm has been used to convert the flight data in time domain to frequency domain which is measured by means of a dedicated on-board data acquisition system capable of on-board logging and telemetry to ground station. The combination of Sequential Least Squares with Recursive Fourier Transform (SLS-RFT) in frequency domain can be used to carry out online parameter estimation. An attempt has been made to check the applicability of the current method to estimate parameters from the generated flight data of the two UAVs using both conventional as well as random control inputs. Results showed that the parameters estimated, using SLS-RFT, from the linear flight data are consistent and in close agreement with the obtained parameters from full scale wind tunnel testing of UAVs. It was also observed that the estimates from the manoeuvres with multistep control inputs converged faster compared to the parameters obtained from the manoeuvres with slow varying control surface deflections. The time varying linear aerodynamic parametric model of SLS-RFT was able to capture the dynamics of the flights with nonlinear aerodynamics. Certain limitations of the current online system identification method were also observed with estimating parameters from the flight data of UAVs performing near stall manoeuvres. The estimated parameters using SLS-RFT are also compared with the results obtained from batch methods namely classical Maximum Likelihood (ML) and neural based Neural–Gauss–Newton (NGN) methods.

A stand-alone 3D muon tracking system based on Silicon Photomultipliers

We present a muon tracking system based on the use of plastic scintillator strips and Silicon Photomultipliers (SiPMs). Originally intended to be used mainly in outreach activities, the detector became an effective instrument to perform muon flux intensity measurements and for muon transmission imaging applications. It consists of two independent orthogonal views, each one made of 10 planes and containing 10 detection channels per plane. A custom electronics has been designed to bias and readout all the 200 SiPMs in the detector, to generate the trigger logic pattern and to control the working parameters. Thanks to an on-board data acquisition system, standalone data taking is possible with the addition of a computer. Commissioning and performance of the detector are presented, along with preliminary results concerning the first data taking on Saadiyat Island, Abu Dhabi, with the aim of measuring the local cosmic ray flux and to assess its muon transmission imaging capability.

Energy extraction from ocean currents using straight bladed cross-flow hydrokinetic turbine

Harvesting marine renewable energy remains to be a prime focus of researchers across the globe both in environmental and in commercial perspectives. India is blessed with a long coastline, and the seas around Indian peninsula offer ample potential to tap various ocean energy forms. National Institute of Ocean Technology carries out research and various ocean energy technologies, out of which harnessing kinetic energy in seawater currents is one. This article presents the open sea trials recently carried out on National Institute of Ocean Technology’s cross-flow hydrokinetic ocean current turbine in South Andaman. The turbine was designed to generate 100 W electricity at 1.2 m/s current speed and was built in-house. The turbine was initially tested in a seawater channel and then was deployed in Macpherson Strait in Andaman. It was fitted below a floating platform designed especially for this purpose, and the performance of the turbine was continuously logged inside an on-board data acquisition system. The trials were successful and in line with computations.

Obstanovka: Exploring Nearby Space

The exploration and examination of near-Earth space helps us better understand our solar system and deeply affects our everyday life in areas such as telecommunications, Earth observation, and weather forecasting. Toward that end, scientists developed the onboard data acquisition and control system of the Obstanovka (Russian for environment) experiment in the Russian segment of the International Space Station. Obstanovka, developed by nine teams from different countries, employs 11 sensors to study how the Sun, magnetosphere, and ionosphere influence Earth's weather.

Wheel reprofiling interval optimization based on dynamic behavior evolution for high speed trains

There has been a ballooning of demand on the Chinese railway system which has brought about a need to maximize the availability of vehicles. This has, in turn, led to a need to optimize reprofiling intervals for wheels. Ideally, these intervals should be based on operational benefits and effects of worn profiles on the running behavior of vehicles. Chinese high speed railway system started to develop since 2008. Less operating and maintenance experience have been accumulated. Long-term tracking tests that continually monitoring the evolution of vehicle dynamic behavior with respect to wheel wear process were taken. The object was to optimize wheel reprofiling and vehicle maintenance intervals. This article introduces the tests results of CRH380B series high speed trains running on the Beijing-Shanghai, Ha’erbin-Dalian, and Wuhan-Guangzhou lines. The long-term tracking tests concentrate on the natural wear process of wheels and other accidental damages such as flats, indentations are out of tests scope.The comfort of the passenger is affected by their exposure to vibration and noise in the car body. Lateral stability of the bogies is a key factor of running safety. Correlation evolution of vehicle dynamic behavior and profile worn shapes is a foundation to determine wheel reprofiling intervals. Accelerometers were mounted on the bogie and car body. Vibration data was recorded daily using on-board data acquisition system. Wheel profiles were also measured periodically to get wear evolution. It is found that vibration levels increased at late reprofiling intervals sharply due to high conicity as wear. Lateral stability of the bogie and out of roundness of the wheels was also deteriorated accompanied with wear process. Through quantified evolution relationship between wheel profiles and vibrations, an optimized reprofiling interval was determined to avoid stability and noise issues.

Marine Engine Centered Localized Models for Sensor Fault Detection under Ship Performance Monitoring

Abstract: Sensor fault detection under marine engine centered localized models of an engine propeller combinator diagram is presented in this study. The proposed approach consists of two detection levels to identify of sensor fault situations in an onboard data acquisition system of a vessel. Each parameter in ship performance and navigation data can have a realistic data range (i.e. a threshold relates to the variance), where the parameter can vary. If the sensor reads a value beyond this parameter range, then that data point is categorized as a sensor fault situation by the first fault detection level. However, some sensor faults are located within this data range and that cannot identify by this detection level. Such complex sensor fault situations are detected by the second fault detection level by considering the proposed localized models. These localized models are derived with respect to the operating regions of an engine-propeller combinator diagram, where the respective data points are clustered by Gaussian mixture models with an expectation maximization algorithm. Each data cluster is examined through principal component analysis and projected into the bottom principal component to identify such complex sensor fault situations. A data set of ship performance and navigation information of a selected vessel is used through these sensor fault detection levels and the successful results on identifying such sensor fault situations are also presented in this study.

Characterization of the road surfaces in real time

The main factor in the propagation of traffic noise is the road surface, where the vehicles generate noise due to the contact between tire and pavement, in addition to the noise produced by the engine.The aim of this work is to obtain the parameters of ground surfaces in real time at different speeds by using an on-board data acquisition system. The system is based on two small microphones with flat response frequency, a small directional speaker, audio interface and a laptop for signal processing.The contribution of this system is to know, in real time, the physical parameters of ground surface measured at different speeds, minimizing the influence of aerodynamic noise. The low cost and its portability make the system to be a very useful tool for noise predictions in outdoors propagation and could be used as a complement instrument in noise mapping.The acoustic impedance was measured by the method recommended by the ANSI S1.18 standard, but with some adjustments. This method consists of measuring the sound level difference from two microphones close to the surface, and calculating the level difference according to an impedance model of the ground.

OS8-2-4 A Study on the Characteristics of Internal and External Pressure Variation for KTX

Experimental study has been conducted to clarify the internal and external pressure variation for KTX(Korea Train eXpress). Abrupt pressure variation gives rise to the ear-discomfort for passenger and fatigue for car body. In this study, the pressure variation of internal and external pressure variation are measured by using KTX and on-board portable data acquisition system in commercial line. The tunnels from 200m to 4000m in length are chosen for the investigation of tunnel length effects. From the results of experiment, the internal pressure variation rate for all the test tunnels is lower than the standard criteria of 200Pa/s. And, the critical tunnel lengths for pressure wave pattern are classified into 7 groups by using theoretical L-t diagram.

Analysis of Electric Vehicle Usage of a Hyundai Santa Fe Fleet in Hawaii

Here we report the analysis of vehicle usage of a fleet of 15 Hyundai Santa Fe electric-sport-utility-vehicles (e-SUVs), which was tested in Honolulu, Hawaii, from July 2001 to June 2003. The 15 vehicles were dispatched to the Hickam Air Force Base (HAFB), City and County (C&C) of Honolulu, Hawaiian Electric Co. (HECO), and the Hawaii Electric Vehicle Demonstration Project (HEVDP) office for field evaluation. More than 25,000 trips were recorded using on-board data acquisition systems in all vehicles during the two-year period, representing a total driving distance of more than 255,000 km. We used a systematic approach to conduct driving cycle analysis (DCA) from the second-by-second trip data. Detailed breakdown of the driving cycles in terms of driving patterns was generated and summarized as functions of vehicle operating time and mileage for each vehicle over the evaluation period. In this paper, we illustrate how to analyze the vehicle usage from such a DCA and the real-life data in the database. The vehicle usage analysis (VUA) includes frequency and extent of vehicle operation in addition to the DCA. We intend to correlate vehicle performance via DCA and VUA for comparison among different operating organizations to allow us develop a more effective fleet operation in the future.

Використання реконфігурованих елементів у бортових системах збору й обробки даних

Використання реконфігурованих елементів у бортових системах збору й обробки даних