The Application of Hardware-in-the-Loop (HIL) Simulation for Evaluation of Active Safety of Vehicles Equipped with Electronic Stability Control (ESC) Systems

Tumasov, A. V.; Vashurin, A.S.; Trusov, Yu.P.; Toropov, E. I.; Moshkov, P.S.; Kryaskov, V.S.; Vasilyev, A. S.

doi:10.1016/j.procs.2019.02.057

Cited by 22 publications

(14 citation statements)

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“…HIL is a technique for developing and testing complex real-time embedded systems. HIL simulation provides an active structure by transferring the control plant's complexity to the test platform [17]. With the aid of the shield, it was easy to build and test the system.…”

Section: Methodsmentioning

confidence: 99%

Smart Fingerprint Biometric and RFID Time-Based Attendance Management System

Badmus¹,

Odekunle²,

Oyewobi³

2021

EJECE

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Attendance documentation is a crucial metric for determining a student's dedication, qualification, and status. Several types of systems have been developed for automated attendance. The majority of these implementations were built on a single factor template, representing a line of security error. In recent years, rapid development has taken place in several applications such as authorization, data access, and access control by implementing biometrics and radio frequency identification technologies. This research work focuses on designing a smart protocol for RFID authentication in combination with fingerprint biometric information, which guarantees confidentiality, identity verification, and data protection. Reaction time and event performance evaluation were the tests performed on the system. Ten students with unique fingerprints and RFID cards are assessed for the test. The test results show an average duration of 20.61 respective seconds for each student, and zero percent of false refusal rate was recorded in the implementation. Finally, the result shows the system is reliable and efficient. Each result revealed high-speed performance due to software and hardware coordination. Biometric systems provided data integrity and security, RFID provided limited complexity, and the proposed method's technical programming pattern provided an average run time.

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Section: Methodsmentioning

confidence: 99%

Smart Fingerprint Biometric and RFID Time-Based Attendance Management System

Badmus¹,

Odekunle²,

Oyewobi³

2021

EJECE

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“…In 2017, a semi-physical simulation test rig of urban EMU network control was tested, which could simulate actual train operation [12]. In 2019, a research was dedicated to the development and validation of a HILS test bench for virtual real-time testing of the GAZ Group light commercial vehicles equipped with Electronic Stability Control(ESC) systems [13]. Though the application of mod-ern real-time simulation technologies based on HILS allows to decrease the number of onsite tests, most of them are all focused on high-speed EMU.…”

Section: A Intelligent Experiments Platform Of Train Braking Systemmentioning

confidence: 99%

A Hybrid Temporal Data Mining Method for Intelligent Train Braking Systems

2022

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As big data mining technology penetrates into various fields, cross-domain topics driven by data predictive analysis have become important entry points for solving traditional problems. Due to the complex changes of the pressure sensor and the interaction of different grouped trains during the train braking process, the mechanism modeling is difficult, the data is highly temporalized, and the data distribution is not stable. Facing the development trend of long-grouped-heavy-duty train captains, if the braking analysis of the train by temporal data mining of small groups can be used for predictive analysis, it will make innovative progress in the entire train braking field. This paper focuses on combining latest technology such as machine learning, transfer learning and lifelong learning to construct the first predictive analysis research framework in the field of train braking systems. Based on the principle of train braking process and temporal data collected from intelligent experiment platform, a baseline has firstly been built to solve fixed-grouped and multi-grouped temporal prediction problems. Then a predictive algorithm for model verification and update for lifelong learning is established to automatically update model parameters over time. Finally, relying on the parameter transfer in transfer learning, a multi-grouped temporal data prediction analysis is performed. Through comparing the training results of the "pre-trained" model on the general domain, the "tuned" model on both general domain and the target domain, and the "target only" model on the target domain separately, multi-domain tuning results show their applicable scope and transfer conditions. In summary, this work can contribute to intelligently upgrading the semi-physical intelligent test platform for long-grouped-heavy-duty trains.INDEX TERMS train braking system, temporal data mining, lifelong learning, transfer learning

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“…As mentioned above, PEMFCs and DC/DCs together constituted a range extender. The following assumptions were made regarding the modeling before creating the PEMFC bond graph model: The PEMFC model was established based on Equations (6)- (8), (20) and (24). Figure 7 shows the model for the PEMFC and DC/DC.…”

Section: Bond Graph Model Of Range Extendermentioning

confidence: 99%

“…HiL is widely used in aircraft and aerospace industries, vehicle systems, power systems, robotics, and marine systems [23]. Research on HiL approaches to automobile systems has mainly focused on the development of electronic control units for power systems [24], adaptive cruise control [25], hybrid vehicle EMS validation [26,27], and vehicle communication validation [28]. In recent years, it has become increasingly important to develop a new method of HiL, as improvements in electrification technology have made the power systems more complex, but also more efficient [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method

Song

Wang

An³

et al. 2021

Energies

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In view of the aggravation of global pollution and greenhouse effects, fuel cell electric vehicles (FCEVs) have attracted increasing attention, owing to their ability to release zero emissions. Extended-range fuel cell vehicles (E-RFCEVs) are the most widely used type of fuel cell vehicles. The powertrain system of E-RFCEV is relatively complex. Bond graph theory was used to model the important parts of the E-RFCEV powertrain system: Battery, motor, fuel cell, DC/DC, vehicle, and driver. In order to verify the control effect of energy management strategy (EMS) in a real-time state, bond graph theory was applied to hardware-in-the-loop (HiL) development. An HiL simulation test-bed based on the bond graph model was built, and the HiL simulation verification of the energy management strategy was completed. Based on the comparison to a power-following EMS, it was found that fuzzy logic EMS is more adaptive to vehicle driving conditions. This study aimed to apply bond graph theory to HiL simulations to verify that bond graph modeling is applicable to complex systems.

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The Application of Hardware-in-the-Loop (HIL) Simulation for Evaluation of Active Safety of Vehicles Equipped with Electronic Stability Control (ESC) Systems

Cited by 22 publications

References 1 publication

Smart Fingerprint Biometric and RFID Time-Based Attendance Management System

Smart Fingerprint Biometric and RFID Time-Based Attendance Management System

A Hybrid Temporal Data Mining Method for Intelligent Train Braking Systems

Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method

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