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Selected Papers from the International Conference on Electric and Intelligent Vehicles (ICEIV 2021) on Advanced Energy Storage System

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 8827

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Special Issue Editors

Dr. Chun Wang

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Guest Editor

School of Mechanical Engineering, Sichuan University of Science and Engineering, No. 519, Huixing Road, Zigong 643000, China
Interests: electric vehicles; energy management strategy; battery management systems; energy storage
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. JongHoon Kim

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Guest Editor

Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Interests: battery management systems; next generation battery; vanadium redox flow battery; energy storage system; fuel cell system
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xiao-Guang Yang

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Guest Editor

National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: electrochemical energy systems; batteries; fuel cells; flow Batteries; heat & mass transfer; physics-based modeling

Dr. Aihua Tang

E-Mail Website
Guest Editor

School of Vehicle Engineering, Chongqing University of Technology, Chongqing 400054, China
Interests: battery system modeling; state estimation and life prediction; battery system fault diagnosis and health status estimation under big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The International Conference on Electric and Intelligent Vehicles (ICEIV 2021) will be held on 25–28 June 2021 in Nanjing, China. The conference will be organized and hosted by China Electrotechnical Society, Beijing Institute of Technology, Energy Storage System and Equipment Technical Committee and Advanced Energy Storage and Application. The conference has become a significant platform for researchers and professors in those areas to meet and to discuss, share, and exchange their recent progress. The conference ICEIV 2021 has the theme “Towards Intelligent E-Mobility” and will deal in particular with research and development of vehicles and the integration of power systems, control systems and energy storage technology.

A set of papers will be selected for submission of a substantially improved version to this Special Issue on “Advanced Energy Storage System—Selected Papers from the International Conference on Electric and Intelligent Vehicles (ICEIV 2021)”.

Dr. Chun Wang
Prof. Dr. Jong Hoon Kim
Prof. Dr. Xiao-Guang Yang
Dr. Aihua Tang
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. 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

electric vehicle technology
energy consumption analysis technology
energy management and optimization control
battery system test and evaluation
battery system modeling, estimation, and RUL prediction
battery system thermal runaway warning and protection
battery system thermal management technology
hybrid power system modeling, simulation, and optimization technology
power system optimization and intelligent control technology
integration of renewable energy sources
fuel cells technology
electrochemical energy systems

Related Special Issue

Advanced Energy Storage Technologies and Applications (AESAs) in Energies (2 articles)

Published Papers (4 papers)

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Research

20 pages, 6575 KiB

Open AccessArticle

An Approach for Pricing of Charging Service Fees in an Electric Vehicle Public Charging Station Based on Prospect Theory

by Yan Bao, Fangyu Chang, Jinkai Shi, Pengcheng Yin, Weige Zhang and David Wenzhong Gao

Energies 2022, 15(14), 5308; https://0-doi-org.brum.beds.ac.uk/10.3390/en15145308 - 21 Jul 2022

Cited by 7 | Viewed by 1774

Abstract

Within the context of sustainable development and a low-carbon economy, electric vehicles (EVs) are regarded as a promising alternative to engine vehicles. Since the increase of charging EVs brings new challenges to charging stations and distribution utility in terms of economy and reliability, EV charging should be coordinated to form a friendly and proper load. This paper proposes a novel approach for pricing of charging service fees in a public charging station based on prospect theory. This behavioral economics-based pricing mechanism will guide EV users to coordinated charging spontaneously. By introducing prospect theory, a model that reflects the EV owner’s response to price is established first, considering the price factor and the state-of-charge (SOC) of batteries. Meanwhile, the quantitative relationship between the utility value and the charging price or SOC is analyzed in detail. The EV owner’s response mechanism is used in modeling the charging load after pricing optimization. Accordingly, by using the particle swarm optimization algorithm, pricing optimization is performed to achieve multiple objectives such as minimizing the peak-to-valley ratio and electricity costs of the charging station. Through case studies, the determined time-of-use charging prices by pricing optimization is validated to be effective in coordinating EV users’ behavior, and benefiting both the station operator and power systems. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Electric and Intelligent Vehicles (ICEIV 2021) on Advanced Energy Storage System)

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16 pages, 4586 KiB

Open AccessArticle

A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty

by Chun Wang, Chaocheng Fang, Aihua Tang, Bo Huang and Zhigang Zhang

Energies 2022, 15(12), 4309; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124309 - 12 Jun 2022

Cited by 7 | Viewed by 1601

Abstract

An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. The analysis results show that the root mean square error (RMSE) of the variable temperature model is reduced by 90.187% compared with the non-variable temperature model. In addition, the SOC estimation error of the proposed NARX-AEKF fusion estimation method based on the variable temperature model remains within 2.055%. Even when the SOC initial error is 0.150, the NARX-AEKF fusion estimation method can quickly converge to the reference value within 5.000 s. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Electric and Intelligent Vehicles (ICEIV 2021) on Advanced Energy Storage System)

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15 pages, 3528 KiB

Open AccessArticle

Research on Fuel Cell Fault Diagnosis Based on Genetic Algorithm Optimization of Support Vector Machine

by Weiwei Huo, Weier Li, Chao Sun, Qiang Ren and Guoqing Gong

Energies 2022, 15(6), 2294; https://0-doi-org.brum.beds.ac.uk/10.3390/en15062294 - 21 Mar 2022

Cited by 11 | Viewed by 2171

Abstract

The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learning machine and a support vector machine are applied to classify the usual faults of fuel cells, including air compressor faults, air supply pipe and return pipe leaks, stack flooding faults and temperature controller faults. The accuracy of fault classification was 78.67% and 83.33% respectively. In order to improve the efficiency of fault classification, a genetic algorithm is used to optimize the parameters of the support vector machine. The simulation results show that the accuracy of fault classification was improved to 94% after optimization. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Electric and Intelligent Vehicles (ICEIV 2021) on Advanced Energy Storage System)

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Graphical abstract

12 pages, 3853 KiB

Open AccessArticle

Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

by Longxing Wu, Kai Liu, Hui Pang and Jiamin Jin

Energies 2021, 14(17), 5265; https://0-doi-org.brum.beds.ac.uk/10.3390/en14175265 - 25 Aug 2021

Cited by 32 | Viewed by 2232

Abstract

State of Charge (SOC) is essential for a smart Battery Management System (BMS). Traditional SOC estimation methods of lithium-ion batteries are usually conducted using battery equivalent circuit models (ECMs) and the impact of current sensor bias on SOC estimation is rarely considered. For this reason, this paper proposes an online SOC estimation based on a simplified electrochemical model (EM) for lithium-ion batteries considering sensor bias. In EM-based SOC estimation structure, the errors from the current sensor bias are addressed by proportional–integral observer. Then, the accuracy of the proposed EM-based SOC estimation is validated under different operating conditions. The results indicate that the proposed method has good performance and high accuracy in SOC estimation for lithium-ion batteries, which facilitates the on-board application in advanced BMS. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Electric and Intelligent Vehicles (ICEIV 2021) on Advanced Energy Storage System)

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