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Machines
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Emerging Technologies in New Energy Vehicle
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Emerging Technologies in New Energy Vehicle

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (6 January 2023) | Viewed by 9631

Special Issue Editors

Dr. Feng Xiao

E-Mail Website
Guest Editor
State Key Laboratory of Automotive Simulation and Control, Jilin university, Changchun 130025, China
Interests: key technologies of new energy vehicles; computer vision; new energy vehicle electric wheel
Special Issues, Collections and Topics in MDPI journals
Dr. Shixin Song

E-Mail Website
Guest Editor
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
Interests: key technologies of new energy vehicles; computer vision; new energy vehicle electric wheel
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electric energy and hydrogen energy are some of the most promising renewable energy applications, and the development of hybrid electric vehicles, electric vehicles, and fuel cell vehicles has become a research hotspot. With the deepening of research, some emerging technologies can also be gradually applied in new energy vehicle-related research. This Special Issue is open to researchers, engineers, and students worldwide, focusing on new energy vehicle-related fields. Authors are welcome to publish original research papers describing theory development, system applications, and algorithm demonstrations, including, but not limited to: new energy vehicle technology; energy management strategies; association algorithms between new energy vehicles and traffic information; application of computer vision, reinforcement learning, deep learning, etc., in new energy vehicles; new energy vehicle control algorithms; development of motor control strategies in new energy vehicles; battery management systems.

Dr. Feng Xiao
Dr. Shixin Song
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. Machines 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.

Keywords

  • new energy vehicle technology
  • energy management strategies
  • association algorithms between new energy vehicles and traffic information
  • application of computer vision, reinforcement learning, deep learning, etc., in new energy vehicles
  • new energy vehicle control algorithms
  • development of motor control strategies in new energy vehicles
  • battery management systems

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Related Special Issue

Published Papers (5 papers)

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Research

20 pages, 2428 KiB  
Article
An Energy-Efficient Driving Method for Connected and Automated Vehicles Based on Reinforcement Learning
by Haitao Min, Xiaoyong Xiong, Fang Yang, Weiyi Sun, Yuanbin Yu and Pengyu Wang
Machines 2023, 11(2), 168; https://doi.org/10.3390/machines11020168 - 26 Jan 2023
Cited by 2 | Viewed by 2009
Abstract
The development of connected and automated vehicles (CAV) technology not only helps to reduce traffic accidents and improve traffic efficiency, but also has significant potential for energy saving and emission reduction. Using the dynamic traffic flow information around the vehicle to optimize the [...] Read more.
The development of connected and automated vehicles (CAV) technology not only helps to reduce traffic accidents and improve traffic efficiency, but also has significant potential for energy saving and emission reduction. Using the dynamic traffic flow information around the vehicle to optimize the vehicle trajectory is conducive to improving the energy efficiency of the vehicle. Therefore, an energy-efficient driving method for CAVs based on reinforcement learning is proposed in this paper. Firstly, a set of vehicle trajectory prediction models based on long and short-term memory (LSTM) neural networks are developed, which integrate driving intention prediction and lane change time prediction to improve the prediction accuracy of surrounding vehicle trajectories. Secondly, an energy-efficient driving model is built based on Proximity Policy Optimization (PPO) reinforcement learning. The model takes the current states and predicted trajectories of surrounding vehicles as input information, and outputs energy-saving control variables while taking into account various constraints, such as safety, comfort, and travel efficiency. Finally, the method is tested by simulation on the NGSIM dataset, and the results show that the proposed method can save energy consumption by 9–22%. Full article
(This article belongs to the Special Issue Emerging Technologies in New Energy Vehicle)
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20 pages, 4347 KiB  
Article
Research on Energy Consumption Generation Method of Fuel Cell Vehicles: Based on Naturalistic Driving Data Mining
by Yangyang Ma, Pengyu Wang, Bin Li and Jianhua Li
Machines 2022, 10(11), 1047; https://doi.org/10.3390/machines10111047 - 9 Nov 2022
Cited by 1 | Viewed by 1727
Abstract
In this paper, an energy consumption generation method is proposed to accurately calculate the energy consumption of fuel cell vehicles (FCVs). A specific driver drives on a route (from Jilin University to FAW Volkswagen) for 331 working days (1 April 2020 to 28 [...] Read more.
In this paper, an energy consumption generation method is proposed to accurately calculate the energy consumption of fuel cell vehicles (FCVs). A specific driver drives on a route (from Jilin University to FAW Volkswagen) for 331 working days (1 April 2020 to 28 July 2021) and collects more than 40,000 s of naturalistic driving data by means of a GPS receiver (FRII-D). To accurately calculate the energy consumption data of FCVs under actual driving cycles, naturalistic driving data mining is first studied. The principal component analysis (PCA) algorithm is used to reduce the dimension of the extracted driving cycle characteristic parameters, the K-means algorithm is used for driving cycle clustering, and the LVQ is used for driving cycle identification. Then, the characteristic parameters correlated to energy consumption are obtained based on the FCV model and regression analysis method. In addition, an energy consumption generation method is designed and proposed based on the characteristic parameters and identification results. Furthermore, the proposed energy consumption generation method can accurately calculate the energy consumption of FCVs, which also provides a reference for further research on the efficient energy management of FCVs. Full article
(This article belongs to the Special Issue Emerging Technologies in New Energy Vehicle)
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22 pages, 23174 KiB  
Article
Energy Management Strategy of Mild Hybrid Electric Vehicle Considering Motor Power Compensation
by Hengxu Lv, Chuanxue Song, Naifu Zhang, Da Wang and Chunyang Qi
Machines 2022, 10(11), 986; https://doi.org/10.3390/machines10110986 - 28 Oct 2022
Cited by 2 | Viewed by 1663
Abstract
An energy management control strategy based on the instantaneous optimization method of equivalent consumption minimization strategy (ECMS) under motor power compensation for mild hybrid vehicles is proposed in this study to improve fuel economy and ensure the dynamic performance of cars. A mild [...] Read more.
An energy management control strategy based on the instantaneous optimization method of equivalent consumption minimization strategy (ECMS) under motor power compensation for mild hybrid vehicles is proposed in this study to improve fuel economy and ensure the dynamic performance of cars. A mild hybrid platform is built, and the future supplementary model of electric energy and the future consumption model of electric energy are established according to different power flow directions. It determines the equivalent fuel consumption rate of powertrain as the objective function by defining the equivalent factor and corresponding derivation, carries out optimization calculation, and obtains the energy distribution relationship between the engine and the motor. The motor power compensation strategy based on the control strategy is adopted to solve the effect of turbocharged engines’ transient response on vehicle dynamics and fuel economy. The actual results showed that vehicle power and fuel economy can be improved under the control strategy and compensation strategy design. Meanwhile, different motors allow the compensating coefficient to have different power-boosting and fuel economy effects. Full article
(This article belongs to the Special Issue Emerging Technologies in New Energy Vehicle)
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18 pages, 3448 KiB  
Article
Yaw Stability Research of the Distributed Drive Electric Bus by Adaptive Nonsingular Fast Terminal Sliding Mode Control
by Huimin Zhu, Feng Zhang, Yong Zhang, Liang Su and Gang Gong
Machines 2022, 10(11), 969; https://doi.org/10.3390/machines10110969 - 24 Oct 2022
Cited by 2 | Viewed by 1644
Abstract
Due to the high center of gravity of distributed drive electric buses, it is crucial to enhance their stability and sliding mode control (SMC) is an effective method to enhance vehicle yaw stability. However, the traditional SMC needs to know the upper limits [...] Read more.
Due to the high center of gravity of distributed drive electric buses, it is crucial to enhance their stability and sliding mode control (SMC) is an effective method to enhance vehicle yaw stability. However, the traditional SMC needs to know the upper limits of the interference term in advance and select a better switching gain to obtain a better control effect, which is impossible for vehicle control. To solve the existing problems, an improved adaptive nonsingular fast terminal sliding mode (ANFTSM) control is presented to enhance the stability of distributed drive electric bus. An uncertainty term is introduced as a switching term in the sliding mode variable and the switching gain in the controller is obtained by parameter adaptation without knowing any uncertainty information. In addition, to enhance the stability of the vehicle in real-time, an adaptive neuro fuzzy inference system (ANFIS) for the weighting factor in the sliding surface is adjusted. A co-simulation of Matlab/Simulink–TruckSim is performed to verify the effectiveness of the algorithm under two typical conditions. The results indicate that the proposed control can follow the ideal value better which improves handling stability and chattering is weaker. Furthermore, the proposed control requires fewer control actions, and also reduces the motor torque variation. Full article
(This article belongs to the Special Issue Emerging Technologies in New Energy Vehicle)
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24 pages, 12925 KiB  
Article
A Hierarchical Energy Management Strategy for 4WD Plug-In Hybrid Electric Vehicles
by Zhiqi Guo, Jianhua Guo, Liang Chu, Chong Guo, Jincheng Hu and Zhuoran Hou
Machines 2022, 10(10), 947; https://doi.org/10.3390/machines10100947 - 18 Oct 2022
Cited by 7 | Viewed by 1953
Abstract
In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy [...] Read more.
In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy management strategy (H-EMS) for 4WD PHEVs is proposed in this paper to achieve energy management optimization. Firstly, the future speed information is predicted by the speed prediction method, and the upper energy management strategy adopts the model predictive control (MPC) based on the future speed information to carry out the power source distribution between the engine and the battery. Secondly, the lower energy management strategy performs the power component distribution of the front motor and the rear motor based on an equivalent consumption minimization strategy (ECMS). Finally, the simulation based on MATLAB/Simulink is performed, validating that the proposed method has more energy-saving capabilities, and the economy is improved by 11.87% compared with the rule-based (RB) energy management strategies. Full article
(This article belongs to the Special Issue Emerging Technologies in New Energy Vehicle)
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