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Actuators
Special Issues
Design, Control, and Optimization of Powertrain for New Energy Vehicles
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Design, Control, and Optimization of Powertrain for New Energy Vehicles

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Land Transport".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 12725

Special Issue Editors

Dr. Jinglai Wu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Huazhong University of Science and Technology, Wuhan, China
Interests: dyanmic modelling of powertrain; energy management strategy of NEVs; gear shift control of NEVs; parameters optimization of NEVs powertrains
Prof. Dr. Jiageng Ruan

E-Mail Website
Guest Editor
Intelligent Machinery Research Institute, Beijing University of Technology, Beijing, China
Interests: powertrain topology; design and optimization of electric vehicles; energy storage system configuration; sizing and energy management strategy optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New energy vehicles (NEVs), including battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and fuel-cell electric vehicles (FCEVs), are the inevitable current trend of the automotive industry which aim to reduce carbon dioxide emissions and air pollution in cities. The powertrain, which includes energy storage systems, power machines, and transmission, has decisive influence on the dynamic and economic performance of these new energy vehicles. Systematic research into how to design, control, and optimize NEV powertrains is still crucial to improve their acceptance in the market. Therefore, it is necessary to explore the ideas, investigate the methodology and validate the technology related to designing, controlling, and optimizing NEV powertrains to improve their comprehensive performance. This Special Issue is aimed at providing an open platform to share the innovations, contributions and discussions surrounding the development of NEV powertrains. 

Papers are welcome on topics related, but not limited, to:

  • Configuration and parameters design of powertrain,
  • Dynamic modelling and control of powertrain,
  • Energy management strategy of hybrid power flow,
  • Battery management strategy,
  • Life prediction, extension and fault diagnostic of energy storage system
  • Gear/mode shift control of driving systems,
  • Vibration modelling, analysis, and optimization of driving system,
  • Full lifecycle cost optimization of driving system,
  • Application of V2X in NEVs powertrains.

Dr. Jinglai Wu
Prof. Dr. Jiageng Ruan
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. Actuators is an international peer-reviewed open access monthly 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 (6 papers)

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Research

23 pages, 4950 KiB  
Article
Adaptive Starting Control Strategy for Hybrid Electric Vehicles Equipped with a Wet Dual-Clutch Transmission
by Jun Guo and Yunqing Zhang
Actuators 2023, 12(3), 123; https://doi.org/10.3390/act12030123 - 14 Mar 2023
Cited by 5 | Viewed by 1666
Abstract
To improve the starting performance of the P2.5 plug-in hybrid electric vehicles with a wet dual-clutch transmission, an adaptive starting control strategy is presented in this paper, which controls the two clutches simultaneously involved in the starting process. A fuzzy controller is designed [...] Read more.
To improve the starting performance of the P2.5 plug-in hybrid electric vehicles with a wet dual-clutch transmission, an adaptive starting control strategy is presented in this paper, which controls the two clutches simultaneously involved in the starting process. A fuzzy controller is designed to identify the starting intention and determine the target torque under different working conditions. The starting process is divided into five periods, and linear quadratic optimal control is adopted to obtain the reference torque trajectory for the third period, while the others are determined by adaptive control by changing the adjustment coefficients according to the starting conditions. The combined pressure feedback controller based on the PID algorithm is proposed to control the wet clutch torque to follow the reference torque trajectory. The MATLAB/Simulink software platform is used to simulate the control strategy. The results show that the proposed strategy can shorten the starting time and reduce the level of jerk. Moreover, with the first-gear starting, the friction work of the whole process and clutch C1 is, respectively, reduced by 1.68% and 4.62% for slowly starting and 23.37% and 23.6% for quickly starting, which can significantly prolong the service lifetime of the clutch compared with the traditional single-clutch starting strategy. Full article
(This article belongs to the Special Issue Design, Control, and Optimization of Powertrain for New Energy Vehicles)
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27 pages, 5324 KiB  
Article
Integrated Vehicle Controller for Path Tracking with Rollover Prevention of Autonomous Articulated Electric Vehicle Based on Model Predictive Control
by Yonghwan Jeong
Actuators 2023, 12(1), 41; https://doi.org/10.3390/act12010041 - 12 Jan 2023
Cited by 1 | Viewed by 1690
Abstract
This paper presents an integrated controller for an autonomous articulated electric vehicle (AAEV) for path tracking and rollover prevention. The AAEV is vulnerable to rollover due to the characteristics of the articulated frame steering (AFS) mechanism, which shows improved maneuverability and agility but [...] Read more.
This paper presents an integrated controller for an autonomous articulated electric vehicle (AAEV) for path tracking and rollover prevention. The AAEV is vulnerable to rollover due to the characteristics of the articulated frame steering (AFS) mechanism, which shows improved maneuverability and agility but not front wheel steering. In addition, the ratio between height and track width is high, so the AAEV is prone to rolling over. Therefore, the proposed controller was designed to achieve the two goals, following the reference path and managing the velocity to improve the safety of the AAEV. Vehicle behavior was modeled by a kinematic model with actuation delay. A local linearization was used to improve the accuracy of the vehicle model and reduce the computational load. Reference states of the position and heading were determined to follow the reference path and prevent the rollover. A model predictive control (MPC)-based reference state tracker was designed to optimize the articulation angle rate and longitudinal acceleration commands. The simulation study was conducted to evaluate the proposed algorithm with a comparison of the base algorithms. The reference path for the simulation was an S-shaped path with discontinuous curvature. Simulation results showed that the proposed algorithm reduces the path tracking error and load-transfer ratio. Full article
(This article belongs to the Special Issue Design, Control, and Optimization of Powertrain for New Energy Vehicles)
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16 pages, 2681 KiB  
Article
Real-Time Modeling of Vehicle’s Longitudinal-Vertical Dynamics in ADAS Applications
by Wei Dai, Yongjun Pan, Chuan Min, Sheng-Peng Zhang and Jian Zhao
Actuators 2022, 11(12), 378; https://doi.org/10.3390/act11120378 - 16 Dec 2022
Cited by 1 | Viewed by 1698
Abstract
The selection of an appropriate method for modeling vehicle dynamics heavily depends on the application. Due to the absence of human intervention, the demand for an accurate and real-time model of vehicle dynamics for intelligent control increases for autonomous vehicles. This paper develops [...] Read more.
The selection of an appropriate method for modeling vehicle dynamics heavily depends on the application. Due to the absence of human intervention, the demand for an accurate and real-time model of vehicle dynamics for intelligent control increases for autonomous vehicles. This paper develops a multibody vehicle model for longitudinal-vertical dynamics applicable to advanced driver assistance (ADAS) applications. The dynamic properties of the chassis, suspension, and tires are considered and modeled, which results in accurate vehicle dynamics and states. Unlike the vehicle dynamics models built into commercial software packages, such as ADAMS and CarSim, the proposed nonlinear dynamics model poses the equations of motion using a subset of relative coordinates. Therefore, the real-time simulation is conducted to improve riding performance and transportation safety. First, a vehicle system is modeled using a semi-recursive multibody dynamics formulation, and the vehicle kinematics and dynamics are accurately calculated using the system tree-topology. Second, a fork-arm removal technique based on the rod-removal technique is proposed to reduce the number of bodies, relative coordinates, and equations constrained by loop-closure. This increase the computational efficiency even further. Third, the dynamic simulations of the vehicle are performed on bumpy and sloping roads. The accuracy and efficiency of the numerical results are compared to the reference data. The comparative results demonstrate that the proposed vehicle model is effective. This efficient model can be utilized for the intelligent control of vehicle ADAS applications, such as forward collision avoidance, adaptive cruise control, and platooning. Full article
(This article belongs to the Special Issue Design, Control, and Optimization of Powertrain for New Energy Vehicles)
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21 pages, 2311 KiB  
Article
Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles
by Fei Ju, Nikolce Murgovski, Weichao Zhuang and Liangmo Wang
Actuators 2022, 11(12), 356; https://doi.org/10.3390/act11120356 - 01 Dec 2022
Cited by 2 | Viewed by 2318
Abstract
This paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal [...] Read more.
This paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal solution for the integrated optimal control problem (OCP), we first build an MPC, referred to as a joint MPC, in which the goal is to minimize battery energy consumption while maintaining cabin-cooling comfort. Second, we divide the integrated OCP into two small-scale problems and devise a co-optimization MPC (co-MPC), where speed planning on hilly roads and cabin-cooling management with propulsion power information are addressed successively. Our proposed MPC methods are then validated through two case studies. The results show that both the joint MPC and co-MPC can produce significant energy benefits while maintaining driving and thermal comfort. Compared to regular constant-speed cruise control that is equipped with a proportion integral (PI)-based AC controller, the benefits to the battery energy earned by the joint MPC and co-MPC range from 2.09% to 2.72%. Furthermore, compared with the joint MPC, the co-MPC method can achieve comparable performance in energy consumption and temperature regulation but with reduced computation time. Full article
(This article belongs to the Special Issue Design, Control, and Optimization of Powertrain for New Energy Vehicles)
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21 pages, 5737 KiB  
Article
Driving Torque Control of Dual-Motor Powertrain for Electric Vehicles
by Jinglai Wu, Bing Wang and Xianqian Hong
Actuators 2022, 11(11), 320; https://doi.org/10.3390/act11110320 - 03 Nov 2022
Cited by 5 | Viewed by 2420
Abstract
This paper investigates the driving torque control method for the dual-motor powertrain in electric vehicles (EVs) to achieve the performance of accurate vehicle speed tracking, seamless driving mode shift, and high energy efficiency. The configuration of the dual-motor powertrain is based on the [...] Read more.
This paper investigates the driving torque control method for the dual-motor powertrain in electric vehicles (EVs) to achieve the performance of accurate vehicle speed tracking, seamless driving mode shift, and high energy efficiency. The configuration of the dual-motor powertrain is based on the parallel axle transmission structure, which does not contain any clutch or synchronizer. The powertrain provides three driving modes that are two single-motor driving modes and one dual-motor combined driving mode. A detailed dynamic model of the dual-motor powertrain is built to simulate the dynamic response of an EV. An energy management strategy (EMS) is used to select the driving mode and determine the ideal driving torque of two motors. The dynamic control strategy tries to track the ideal vehicle speed when uncertain parameters existed and avoid power interruption or impact during the mode shift. Three dynamic control strategies are proposed, which are the backward dynamic control strategy (BDCS), combined forward and backward dynamic control strategy (CFBDCS), and nested forward and backward dynamic control strategy (NFBDCS). The simulation results demonstrate that the NFBDCS has the best comprehensive performance in vehicle speed tracking, seamless mode shift, and good system energy efficiency. Full article
(This article belongs to the Special Issue Design, Control, and Optimization of Powertrain for New Energy Vehicles)
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13 pages, 3534 KiB  
Article
The Improved Deeplabv3plus Based Fast Lane Detection Method
by Zhong Wang, Yin Zhao, Yang Tian, Yahui Zhang and Landa Gao
Actuators 2022, 11(7), 197; https://doi.org/10.3390/act11070197 - 18 Jul 2022
Cited by 1 | Viewed by 1816
Abstract
Lane detection is one of the most basic and essential tasks for autonomous vehicles. Therefore, the fast and accurate recognition of lanes has become a hot topic in industry and academia. Deep learning based on a neural network is also a common method [...] Read more.
Lane detection is one of the most basic and essential tasks for autonomous vehicles. Therefore, the fast and accurate recognition of lanes has become a hot topic in industry and academia. Deep learning based on a neural network is also a common method for lane detection. However, due to the huge computational burden of the neural network, its real-time performance is often difficult to meet the requirements in the fast-changing actual driving scenes. A lightweight network combining the Squeeze-and-Excitation block and the Self-Attention Distillation module is proposed in this paper, which is based on the existing deeplabv3plus network and specifically improves its real-time performance. After experimental verification, the proposed network achieved 97.49% accuracy and 60.0% MIOU at a run time of 8.7 ms, so the network structure achieves a good trade-off between real-time performance and accuracy. Full article
(This article belongs to the Special Issue Design, Control, and Optimization of Powertrain for New Energy Vehicles)
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