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State of the Art Electric Vehicle Technology in China

A topical collection in Energies (ISSN 1996-1073). This collection belongs to the section "E: Electric Vehicles".

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Editors


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Collection Editor
Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
Interests: high power density motor drive and power electronic technology with the main application areas of electrical vehicle and e-transportation etc.
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Collection Editor
State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
Interests: energy storage; electrochemistry; materials chemistry

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Collection Editor
State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China
Interests: lithium ion battery fire dynamics and prevention; glazing behavior under thermal loading; hazardous chemicals leakage and sub-consequence disaster; the inherent of spontaneous combustion; wind turbine fire
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Collection Editor
School of Materials, Sun Yat-sen University, Shenzhen 518107, China
Interests: lithium ion batteries; solid state batteries; interface; structure; phase transition; high energy density cathodes; electrode process dynamics; energy storage and conversion; first-principles calculations; solid state ionics
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grade E-Mail Website
Collection Editor
School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China
Interests: battery thermal management; thermal energy storage; phase change heat transfer; micro/nano heat transfer; novel heat pipe
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Collection Editor
School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China
Interests: battery reliability analysis; battery health management; battery state estimation
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Topical Collection Information

Dear Colleagues,

In the trend of electrification, intelligence, sharing and networking, and increasingly stringent environmental requirements, electrification is the future development direction of automobiles. In the past decade, China's electric vehicle market has developed rapidly, and now it has become the largest electric vehicle market in the world. Its sales of electric passenger cars account for half of the world's total, and the sales of electric buses and electric trucks account for more than 90% of the world's total.

Research in pure electric vehicles in China began in the 1960s. With the strong support of the state, some universities, automobile research institutes, and production enterprises jointly developed rechargeable batteries and pure electric vehicles and achieved fruitful results. At present, China has achieved independent mass production of pure electric cars and pure electric buses and is at the forefront in the performance technology of key components such as high-power Ni MH battery and lithium-ion battery. There is not a big gap between China and traditional automobile powers in the development of pure electric vehicle technology, and the country even has reached the world-leading level in some fields, such as zinc-air battery and lithium-ion battery research. With the change in China's automobile industry form and consumption mode, automobiles, transportation, information communication, and other industries are mutually enabling each other. In the future, cross-industry and cross-field integration development will become a major development trend of electric vehicles. The electric vehicle industry, however, despite its rapid development, still faces many challenges, including insufficient infrastructure, short driving range, long charging time, and safety.

We set up this Topical Collection to collect papers on China's advanced electric vehicle technology and development trends, while providing some new directions for thinking about electric vehicle development in China and even the world. We invite papers on innovative technical developments, reviews, case studies, as well as analytical and assessment papers from different disciplines that are relevant to the topic of electric vehicles.

Dr. Xuhui Wen
Prof. Dr. Kangli Wang
Prof. Dr. Qingsong Wang
Prof. Dr. Xia Lu
Prof. Dr. Zhonghao Rao
Prof. Dr. Junnian Wang
Prof. Dr. Quanqing Yu
Collection 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 collection 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. Energies 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 2600 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

  • Intelligent electric vehicles 
  • Energy storage 
  • Motor drive 
  • Power electronic
  • Vehicle dynamics 
  • Big date 
  • Optimization and management

Published Papers (5 papers)

2022

Jump to: 2021

19 pages, 2118 KiB  
Article
Research on TVD Control of Cornering Energy Consumption for Distributed Drive Electric Vehicles Based on PMP
by Wen Sun, Yang Chen, Junnian Wang, Xiangyu Wang and Lili Liu
Energies 2022, 15(7), 2641; https://doi.org/10.3390/en15072641 - 04 Apr 2022
Cited by 3 | Viewed by 1175
Abstract
This paper aims to study the torque optimization control of distributed drive electric vehicles in the cornering process and reduce the cornering energy consumption. The main energy consumption of the vehicle in the cornering process is analyzed clearly based on the 7-DOF vehicle [...] Read more.
This paper aims to study the torque optimization control of distributed drive electric vehicles in the cornering process and reduce the cornering energy consumption. The main energy consumption of the vehicle in the cornering process is analyzed clearly based on the 7-DOF vehicle dynamics model. The torque vectoring distribution (TVD) of a distributed drive electric vehicle in the process of turning was studied on the basis of the Pontryagin Minimum Principle (PMP). The Beetle Antenna Search–Particle Swarm Optimization (BAS-PSO) algorithm was used to optimize the torque distribution coefficient offline, and the algorithm was improved to improve the operation speed. Based on the vehicle dynamics characteristics, the table of torque distribution coefficient of minimum turning energy consumption and the optimal energy-saving degree of TVD control in different bending conditions were worked out. Full article
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2021

Jump to: 2022

16 pages, 7181 KiB  
Article
Torque Vectoring Control of RWID Electric Vehicle for Reducing Driving-Wheel Slippage Energy Dissipation in Cornering
by Junnian Wang, Siwen Lv, Nana Sun, Shoulin Gao, Wen Sun and Zidong Zhou
Energies 2021, 14(23), 8143; https://doi.org/10.3390/en14238143 - 04 Dec 2021
Cited by 2 | Viewed by 2046
Abstract
The anxiety of driving range and inconvenience of battery recharging has placed high requirements on the energy efficiency of electric vehicles. To reduce driving-wheel slip energy consumption while cornering, a torque vectoring control strategy for a rear-wheel independent-drive (RWID) electric vehicle is proposed. [...] Read more.
The anxiety of driving range and inconvenience of battery recharging has placed high requirements on the energy efficiency of electric vehicles. To reduce driving-wheel slip energy consumption while cornering, a torque vectoring control strategy for a rear-wheel independent-drive (RWID) electric vehicle is proposed. First, the longitudinal linear stiffness of each driving wheel is estimated by using the approach of recursive least squares. Then, an initial differential torque is calculated for reducing their overall tire slippage energy dissipation. However, before the differential torque is applied to the two side of driving wheels, an acceleration slip regulation (ASR) is introduced into the overall control strategy to avoid entering into the tire adhesion saturation region resulting in excessive slip. Finally, the simulations of typical manoeuvring conditions are performed to verify the veracity of the estimated tire longitudinal linear stiffness and effectiveness of the torque vectoring control strategy. As a result, the proposed torque vectoring control leads to the largest reduction of around 17% slip power consumption for the situations carried out above. Full article
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15 pages, 5024 KiB  
Article
Research on Optimal Torque Control of Turning Energy Consumption for EVs with Motorized Wheels
by Wen Sun, Juncai Rong, Junnian Wang, Wentong Zhang and Zidong Zhou
Energies 2021, 14(21), 6947; https://doi.org/10.3390/en14216947 - 22 Oct 2021
Cited by 5 | Viewed by 1452
Abstract
This paper aims to explore torque optimization control issue in the turning of EV (Electric Vehicles) with motorized wheels for reducing energy consumption in this process. A three-degree-of-freedom (3-DOF) vehicle dynamics model is used to analyze the total longitudinal force of the vehicle [...] Read more.
This paper aims to explore torque optimization control issue in the turning of EV (Electric Vehicles) with motorized wheels for reducing energy consumption in this process. A three-degree-of-freedom (3-DOF) vehicle dynamics model is used to analyze the total longitudinal force of the vehicle and explain the influence of torque vectoring distribution (TVD) on turning resistance. The Genetic Algorithm-Particle Swarm Optimization Hybrid Algorithm (GA-PSO) is used to optimize the torque distribution coefficient offline. Then, a torque optimization control strategy for obtaining minimum turning energy consumption online and a torque distribution coefficient (TDC) table in different cornering conditions are proposed, with the consideration of vehicle stability and possible maximum energy-saving contribution. Furthermore, given the operation points of the in-wheel motors, a more accurate TDC table is developed, which includes motor efficiency in the optimization process. Various simulation results showed that the proposed torque optimization control strategy can reduce the energy consumption in cornering by about 4% for constant motor efficiency ideally and 19% when considering the motor efficiency changes in reality. Full article
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17 pages, 7302 KiB  
Article
Research on Energy Management Method of Plug-In Hybrid Electric Vehicle Based on Travel Characteristic Prediction
by Yangyang Ma, Pengyu Wang and Tianjun Sun
Energies 2021, 14(19), 6134; https://doi.org/10.3390/en14196134 - 26 Sep 2021
Cited by 8 | Viewed by 1839
Abstract
In the research on energy management methods of plug-in hybrid electric vehicles, it is expected that a future trend will be to optimize energy management using the information provided by the global positioning system (GPS) and intelligent transportation system (ITS), which is relatively [...] Read more.
In the research on energy management methods of plug-in hybrid electric vehicles, it is expected that a future trend will be to optimize energy management using the information provided by the global positioning system (GPS) and intelligent transportation system (ITS), which is relatively scarce in current research. This study proposes a PHEV energy management method based on travel characteristic prediction. Firstly, this study processes the historical travel data of a certain driver obtained by GPS and ITS and uses the established Markov trajectory prediction model based on key points to predict the trajectory and mileage. Then, on the basis of characteristics analysis of historical travel data, while considering traffic information to form a target cycle, the driving cycles are classified and identified based on traffic information predictions. Then, according to the reasonable SOC allocation range of the four typical cycles, the planning algorithm of the SOC reference trajectory is determined and verified. Finally, based on the previous work, an A-ECMS energy management method based on travel characteristic prediction is established. By comparing different energy management methods, the developed energy management method based on travel characteristic prediction can reasonably utilize power batteries. The fuel saving is about 8.95% higher than the rule-based energy management method, which can effectively improve the whole vehicle’s fuel economy and optimization ability. Full article
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14 pages, 4613 KiB  
Article
Aging Behavior of Lithium Titanate Battery under High-Rate Discharging Cycle
by Chu Wang, Zehui Liu, Yaohong Sun, Yinghui Gao and Ping Yan
Energies 2021, 14(17), 5482; https://doi.org/10.3390/en14175482 - 02 Sep 2021
Cited by 12 | Viewed by 2937
Abstract
The high-rate discharging performance of a lithium titanate battery is one of its main properties. In conditions that require ultra-high-rate discharging, a lithium titanate battery can be discharged continuously at a current of 50 C (50 times of its maximum capacity) or higher. [...] Read more.
The high-rate discharging performance of a lithium titanate battery is one of its main properties. In conditions that require ultra-high-rate discharging, a lithium titanate battery can be discharged continuously at a current of 50 C (50 times of its maximum capacity) or higher. In this paper, we take cylindrical steel shell lithium titanate cells as the research object and perform aging cycles at 66 C on these cells. The ultra-high-rate discharging cycles cause a rapid high-power capacity fading while the available capacity at normal current rate is not affected. The capacity at 66 C decreases to 80% of initial value in 10 cycles. This paper also analyzes the aging process of a lithium titanate battery at high-rate discharging with incremental capacity (IC) analysis, and presents the aging behavior of lithium titanate battery qualitatively, which is inconsistent with existing research. We attribute the aging mechanism of ultra-high-rate discharging cycles to the decrease of ionic mobility and increase of polarization resistance. Mechanical damage is observed in the CT scan of an aged cell, which we presume to be the result of rapid strain of cathode material. Full article
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