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Vehicles
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Sustainable Vehicle Drives
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Sustainable Vehicle Drives

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 4835

Special Issue Editor

Prof. Dr. Stephan Rinderknecht

E-Mail Website
Guest Editor
Institute for Mechatronic Systems in Mechanical Engineering, Technische Universität Darmstadt, 64287 Darmstadt, Germany
Interests: E-mobility; transmissions and drivetrains; condition prediction and predictive maintenance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have been able to follow exciting progress in the research area of sustainable vehicle drives. Driven by the need to significantly decrease the CO2 emissions of the transportation sector to reduce the consequences of climate change, disruptive technologies have been of great interest. Here, the three aspects of the sustainability triangle, namely, ecological aspects, economics and social affairs, must be considered integrally. The increasing number of alternative powertrain solutions as well as the growing use of new methodologies such as machine learning, optimization and the use of big data within powertrain applications are promising developments for addressing these pertinent topics. Prominent examples include sustainable individual mobility regarding efficiency and emissions, the electification of vehicle fleets, coupling of energy and mobility sectors and sustainable commercial traffic.

For this Special Issue of Vehicles entitled “Sustainable Vehicle Drives”, we are seeking original contributions within this research area. Topics include, but are not limited to, new powertrain topologies and concepts, developments to increase efficiency and reduce emissions, naturalistic driving studies, sustainable solutions for commercial vehicles as well as the application of modern methods in powertrain applications design.

Prof. Dr. Stephan Rinderknecht
Guest Editor

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. Vehicles is an international peer-reviewed open access quarterly 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 1600 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.

Keywords

  • sustainable powertrain concepts
  • optimization for powertrain design
  • naturalistic driving
  • sustainable commercial traffic
  • machine learning
  • vehicle efficiency
  • coupling of energy and mobility sector
  • sustainability triangle

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Published Papers (2 papers)

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Research

12 pages, 2770 KiB  
Article
The Vehicle Intention Recognition with Vehicle-Following Scene Based on Probabilistic Neural Networks
by Kaixuan Chen and Guangqiang Wu
Vehicles 2023, 5(1), 332-343; https://doi.org/10.3390/vehicles5010019 - 9 Mar 2023
Cited by 1 | Viewed by 2168
Abstract
In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is [...] Read more.
In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is proposed, which enables the rear vehicle to obtain the driving intention of the front vehicle without communication between the two vehicles. First, real vehicle data with different intents are collected and time—frequency domain variables are extracted. Secondly, Principal Component Analysis (PCA) is performed on the variables in order to obtain comprehensive features. Meanwhile, two cases are classified according to whether the front vehicle can transmit data to the rear vehicle. Finally, two recognition models are trained separately according to a PNN algorithm, and the two models obtained from the training are verified separately. When the front vehicle can communicate with the rear vehicle, the recognition accuracy of the corresponding PNN model reaches 96.39% (simulation validation) and 95.08% (real vehicle validation). If it cannot, the recognition accuracy of the corresponding PNN model reaches 78.18% (simulation validation) and 73.74% (real vehicle validation). Full article
(This article belongs to the Special Issue Sustainable Vehicle Drives)
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26 pages, 9079 KiB  
Article
Static Model-Based Optimization and Multi-Input Optimal Control of Automatic Transmission Upshift during Inertia Phase
by Ivan Cvok, Joško Deur, Mislav Hihlik, Yijing Zhang, Vladimir Ivanovic and Yuji Fujii
Vehicles 2023, 5(1), 177-202; https://doi.org/10.3390/vehicles5010011 - 7 Feb 2023
Cited by 2 | Viewed by 2100
Abstract
Step-ratio automatic transmission upshift performance can be improved by modulating the off-going (OFG) clutch during the inertia phase. In this paper, a static powertrain performance model is derived and applied for the purpose of numerically efficient, multi-objective shift control parameter optimization for the [...] Read more.
Step-ratio automatic transmission upshift performance can be improved by modulating the off-going (OFG) clutch during the inertia phase. In this paper, a static powertrain performance model is derived and applied for the purpose of numerically efficient, multi-objective shift control parameter optimization for the inertia phase. The optimization is aimed at finding the optimal node parameters for simplified, piecewise linear, open-loop profiles of oncoming (ONC) clutch, OFG clutch, and engine torque reduction control variables. The performance indices, i.e., the optimization objectives, include shift comfort, clutch thermal loss, and shift time. The optimization results in 3D Pareto optimal frontiers, which are then analyzed and compared with those obtained by using the previously developed, nonlinear model-based, genetic algorithm optimization tool. The derived method is employed in order to develop a static model-based predictive control (S-MPC) strategy, which commands ONC clutch torque control input while retaining open-loop controls for engine and OFG clutch control inputs. The S-MPC strategy aims at providing the prespecified shift time, while the shift time accuracy is relaxed to some extent by using a control input dead zone element to avoid chattering effect. The S-MPC system performance is verified through simulation and compared with the genetic algorithm benchmark. The simulation results demonstrate that the S-MPC strategy approaches the benchmark performance. Full article
(This article belongs to the Special Issue Sustainable Vehicle Drives)
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