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Applied Sciences
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New Progress in Construction Machinery and Vehicle Engineering
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New Progress in Construction Machinery and Vehicle Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (1 February 2020) | Viewed by 13425

Special Issue Editors

Dr. Octavian Postolache

E-Mail Website
Guest Editor
Instituto de Telecomunicações, Universidade de Aveiro Campus Universitário de, R. Santiago, 3810-193 Aveiro, Portugal
Interests: internet of medical things; remote sensing solutions for healthcare; embedded AI for healthcare; smart sensors; virtual reality and mixed reality for healthcare
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Subhas Mukhopadhyay

E-Mail Website
Guest Editor
School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: smart sensors; sensing technology; WSN; IoT; ICT; smart grid; energy harvesting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proposed Special Issue will focus on the original innovation, integrated innovation, and introduced and absorbed re-innovation in the field of construction machinery and vehicle engineering technology.
In view of the development trends regarding big data, cloud computing, networking technology, virtual reality technology applied in the field of construction machinery, and vehicle engineering, innovative solutions and new theories will be promoted in this Special Issue.
We invite authors to submit high-quality research articles and review articles, covering modern theories and methods of construction machinery and vehicle engineering. The topics of the Special Issue are expressed by but are not limited to the following:

  • Technological trends in the intelligent development of international construction machinery and vehicle engineering;
  • Modern design theory and method of construction machinery and vehicle engineering;
  • Virtual reality technology applied in machinery design;
  • Innovation of whole machine and key components;
  • Quality, cost control, and management;
  • Application of networking technology in construction machinery and vehicle engineering;
  • Application of artificial intelligence in construction machinery and vehicle engineering;
  • Application of big data analytics in construction machinery and vehicle engineering;
  • Application of cloud computing in construction machinery and vehicle engineering;
  • Computer vision and application in construction machinery and vehicle engineering.

Prof. Dr. Octavian Postolache
Prof. Subhas Mukhopadhyay
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

13 pages, 16350 KiB  
Article
Monitoring Vehicles on Highway by Dual-Channel φ-OTDR
by Shaohua Xu, Zujun Qin, Wentao Zhang and Xianming Xiong
Appl. Sci. 2020, 10(5), 1839; https://doi.org/10.3390/app10051839 - 07 Mar 2020
Cited by 18 | Viewed by 2557
Abstract
As a fully distributed sensor, the phase-sensitive optical time domain reflectometer (φ-OTDR) has attracted remarkable attention in real-time vibration detection. We present a dual-channel φ-OTDR (DC-φ-OTDR), formed by two single-channel φ-OTDRs (SC-φ-OTDR), to monitor running vehicles on a highway. In the double-channel system, [...] Read more.
As a fully distributed sensor, the phase-sensitive optical time domain reflectometer (φ-OTDR) has attracted remarkable attention in real-time vibration detection. We present a dual-channel φ-OTDR (DC-φ-OTDR), formed by two single-channel φ-OTDRs (SC-φ-OTDR), to monitor running vehicles on a highway. In the double-channel system, an improved algorithm (will be referred to as the CDM&V) is proposed to alleviate the strong dependence of vibration detection on the differential step as in the widely used conventional differential method (CDM). The DC-φ-OTDR is first tested over campus road before applying it to locate moving vehicles on the highway. For comparison purposes, both the DC-φ-OTDR and SC-φ-OTDR are used to monitor the vehicles with respective signal processing methods of the CDM and CDM&V. The experimental results at campus show that the dual-path scheme can undoubtedly reduce vibration misjudgment relative to the single one due to the very small possibility of false measurements occurred simultaneously at the same location in both channels. In signal demodulation, the CDM&V greatly relaxes the constraints on the differencing interval for identifying the vehicle-caused vibration. With a step size of 5 or lower, the CDM fails to locate the running vehicle at z=~8.5 km, but the CDM&V successfully demonstrates the feasible capability of locating the vibration. With an increase in the differential interval, both the CDM and CDM&V are able to detect the vibration signal, but with the latter showing a much better noise suppression performance and hence a larger SNR. Importantly, in comparison with the SC-φ-OTDR system, the DC-φ-OTDR exhibits a considerable enhanced SNR for the detection signal regardless of which processing algorithm (i.e., CDM, CDM&V) is used. The vehicle locations positioned by the DC-φ-OTDR are confirmed by the monitoring cameras. Full article
(This article belongs to the Special Issue New Progress in Construction Machinery and Vehicle Engineering)
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11 pages, 3891 KiB  
Article
Behavior Analysis of Active and Proactive Headrest during Low-Velocity Rear-End Collisions
by Yun Sik Yang, Young Shin Kim and Euy Sik Jeon
Appl. Sci. 2020, 10(4), 1451; https://doi.org/10.3390/app10041451 - 21 Feb 2020
Viewed by 3543
Abstract
The accidents caused by car collisions can be categorized into front collision, side collision, and rear-end collision, among which the fatal accident incidence rate of rear-end collisions is the highest. Because neck injury is the most common injury associated with rear-end collision, the [...] Read more.
The accidents caused by car collisions can be categorized into front collision, side collision, and rear-end collision, among which the fatal accident incidence rate of rear-end collisions is the highest. Because neck injury is the most common injury associated with rear-end collision, the car headrest should be redesigned to minimize such injuries. In this study, we investigated the neck injury indices in active and proactive headrests using a sled test. The predictability of injury indices was examined through the behavior analysis of the head and seat. The characteristics of the head–neck behavior and the structure of the headrest were studied. Furthermore, the neck injury indices corresponding to the two headrests were compared. The predictability of major neck injury indices was examined, which may be used as a reference for developing an active deployment system to complement the existing headrest deployment characteristics. Full article
(This article belongs to the Special Issue New Progress in Construction Machinery and Vehicle Engineering)
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21 pages, 10350 KiB  
Article
Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle
by Zhong Wang and Xiaohong Jiao
Appl. Sci. 2020, 10(3), 745; https://doi.org/10.3390/app10030745 - 21 Jan 2020
Cited by 6 | Viewed by 2136
Abstract
Hybrid hydraulic technology has the advantages of high-power density and low price and shows good adaptability in construction machinery. A complex hybrid powertrain architecture requires optimization and management of power demand distribution and an accurate response to desired power distribution of the power [...] Read more.
Hybrid hydraulic technology has the advantages of high-power density and low price and shows good adaptability in construction machinery. A complex hybrid powertrain architecture requires optimization and management of power demand distribution and an accurate response to desired power distribution of the power source subsystems in order to achieve target performances in terms of fuel consumption, drivability, component lifetime, and exhaust emissions. For hybrid hydraulic vehicles (HHVs) that are used in construction machinery, the challenge is to design an appropriate control scheme to actually achieve fuel economy improvement taking into consideration the relatively low energy density of the hydraulic accumulator and frequent load changes, the randomness of the driving conditions, and the uncertainty of the engine dynamics. To improve fuel economy and adaptability of various driving conditions to online energy management and to enhance the response performance of an engine to a desired torque, a hierarchical model predictive control (MPC) scheme is presented in this paper using the example of a spray-painting construction vehicle. The upper layer is a stochastic MPC (SMPC) based energy management control strategy (EMS) and the lower layer is an MPC-based tracking controller with disturbance estimator of the diesel engine. In the SMPC-EMS of the upper-layer management, a Markov model is built using driving condition data of the actual construction vehicle to predict future torque demands over a finite receding horizon to deal with the randomness of the driving conditions. A multistage stochastic optimization problem is formulated, and a scenario-based enumeration approach is used to solve the stochastic optimization problem for online implementation. In the lower-layer tracking controller, a disturbance estimator is designed to handle the uncertainty of the engine, and the MPC is introduced to ensure the tracking performance of the output torque of the engine for the distributed torque from the upper-layer SMPC-EMS, and therefore really achieve high efficiency of the diesel engine. The proposed strategy is evaluated using both simulation MATLAB/Simulink and the experimental test platform through a comparison with several existing strategies in two real driving conditions. The results demonstrate that the proposed strategy (SMPC+MPC) improves miles per gallon an average by 7.3% and 5.9% as compared with the control strategy (RB+PID) consisting of a rule-based (RB) management strategy and proportional-integral-derivative (PID) controller of the engine in simulation and experiment, respectively. Full article
(This article belongs to the Special Issue New Progress in Construction Machinery and Vehicle Engineering)
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19 pages, 13532 KiB  
Article
Improving Energy Recovery Rate of the Regenerative Braking System by Optimization of Influencing Factors
by Lei Xu, Xiaohui He and Xinmin Shen
Appl. Sci. 2019, 9(18), 3807; https://doi.org/10.3390/app9183807 - 11 Sep 2019
Cited by 13 | Viewed by 4491
Abstract
The braking energy can be recovered and recycled by the regenerative braking system, which is significant to improve economics and environmental effect of the hydraulic hybrid vehicle. Influencing factors for the energy recovery rate of regenerative braking system in hydraulic hybrid vehicle were [...] Read more.
The braking energy can be recovered and recycled by the regenerative braking system, which is significant to improve economics and environmental effect of the hydraulic hybrid vehicle. Influencing factors for the energy recovery rate of regenerative braking system in hydraulic hybrid vehicle were investigated in this study. Based on the theoretical analysis of accumulator and energy recovery rate, modeling of the regenerative braking system and its energy management strategy was conducted in the simulation platform of LMS Imagine Lab AMESim. The simulation results indicated that the influencing factors included braking intensity, initial pressure of the accumulator, and initial braking speed, and the optimal energy recovery rate of 87.61% was achieved when the parameters were 0.4, 19 MPa, and 300 rpm, respectively. Experimental bench was constructed and a series of experiments on energy recovery rate with different parameters were conducted, which aimed to validate the simulation results. It could be found, that with the optimal parameters obtained in the simulation process, the actual energy recovery rate achieved in the experiment was 83.33%, which was almost consistent with the simulation result. The obtained high energy recovery rate would promote the application of regenerative braking system in the hydraulic hybrid vehicle. Full article
(This article belongs to the Special Issue New Progress in Construction Machinery and Vehicle Engineering)
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