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New Advances in Battery Technologies
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New Advances in Battery Technologies

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 7464

Special Issue Editors

Dr. Xiaoyu Li

E-Mail Website
Guest Editor
College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Interests: Li-ion battery; electric vehicle power battery
Dr. Guodong Fan

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: battery intelligent management system; modeling and simulation of battery system
Dr. Wenhui Wang

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Interests: advanced oxidation process; green synthesis of H2O2; Li/Na batteries; battery recycling
Special Issues, Collections and Topics in MDPI journals
Dr. Jinlei Sun

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: battery state estimation; battery balancing; battery aging and life prediction; battery thermal management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Batteries are widely used as energy storage devices in electric vehicles, rail transit, aerospace, power grids, and other fields. In general, the demand for batteries in different application scenarios varies. The material and structure of batteries are constantly being updated and optimized to provide excellent performance in the targeted application areas. At the same time, the safe and efficient use of batteries has also attracted substantial attention. With the development of intelligent sensing technology, battery state evaluation methods are developing towards the combination of multi-scale and multi-physical field information. Furthermore, with the increase in battery usage, the secondary utilization and recycling of electric vehicle power batteries have also become matters of concern in recent years. Efficiently evaluating the residual value of batteries and effectively coping with retired batteries are both essential.

This Special Issue aims to present and disseminate the latest developments related to battery design, modeling, state evaluation, battery reuse, and material recycling.

Topics of interest for publication include, but are not limited to:

  • All aspects of different material types of lithium-ion batteries, sodium-ion batteries, solid-state batteries, etc.;
  • Battery technologies for electric vehicles and grid energy storage applications;
  • Special battery technologies for deep-space and deep-sea applications;
  • Modeling and application of multiple physical fields, such as battery electricity, heat, stress, and ultrasound;
  • Analysis and modeling of battery aging and failure mechanism;
  • Analysis and prevention technologies of battery thermal runaway;
  • Battery fault diagnosis technologies;
  • Application of an artificial intelligence method in battery design/state evaluation;
  • Application of Big Data in battery intelligent operation and maintenance;
  • Fast evaluation methods of health status and residual value of decommissioned batteries;
  • Technologies related to battery secondary utilization and material recycling.

Dr. Xiaoyu Li
Dr. Guodong Fan
Dr. Wenhui Wang
Dr. Jinlei Sun
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

  • battery
  • design
  • modeling
  • multiple physical fields
  • multiscale evaluation
  • state evaluation
  • fault diagnosis
  • secondary utilization
  • material recycling

Published Papers (4 papers)

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Research

18 pages, 10456 KiB  
Article
Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks
by Yong Tian, Qianyuan Dong, Jindong Tian and Xiaoyu Li
Energies 2023, 16(2), 674; https://doi.org/10.3390/en16020674 - 06 Jan 2023
Cited by 4 | Viewed by 1566
Abstract
Accurate capacity estimation of onboard lithium-ion batteries is crucial to the performance and safety of electric vehicles. In recent years, data-driven methods based on partial charging curve have been widely studied due to their low requirement of battery knowledge and easy implementation. However, [...] Read more.
Accurate capacity estimation of onboard lithium-ion batteries is crucial to the performance and safety of electric vehicles. In recent years, data-driven methods based on partial charging curve have been widely studied due to their low requirement of battery knowledge and easy implementation. However, existing data-driven methods are usually based on a fixed voltage segment or state of charge, which would be failed if the charging process does not cover the predetermined segment due to the user’s free charging behavior. This paper proposes a capacity estimation method using multiple small voltage sections and back propagation neural networks. It is intended to reduce the requirement of the length of voltage segment for estimating the complete battery capacity in an incomplete charging cycle. Firstly, the voltage segment most possibly covered is selected and divided into a number of small sections. Then, sectional capacity and skewness of the voltage curve are extracted from these small voltage sections, and severed as health factors. Secondly, the Box–Cox transformation is adopted to enhance the correlation between health factors and the capacity. Thirdly, multiple back propagation neural networks are constructed to achieve capacity estimation based on each voltage section, and their weighted average is taken as the final result. Finally, two public datasets are employed to verify the accuracy and generalization of the proposed method. Results show that the root mean square error of the fusion estimation is lower than 4.5%. Full article
(This article belongs to the Special Issue New Advances in Battery Technologies)
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19 pages, 3607 KiB  
Article
Fast Charging Optimization for Lithium-Ion Batteries Based on Improved Electro-Thermal Coupling Model
by Ran Li, Xue Wei, Hui Sun, Hao Sun and Xiaoyu Zhang
Energies 2022, 15(19), 7038; https://doi.org/10.3390/en15197038 - 25 Sep 2022
Cited by 4 | Viewed by 1824
Abstract
New energy automobiles possess broad application prospects, and the charging technology of vehicle power batteries is one of the key technologies in the development of new energy automobiles. Traditional lithium battery charging mostly adopts the constant current-constant voltage method, but continuous and frequent [...] Read more.
New energy automobiles possess broad application prospects, and the charging technology of vehicle power batteries is one of the key technologies in the development of new energy automobiles. Traditional lithium battery charging mostly adopts the constant current-constant voltage method, but continuous and frequent charging application conditions will cause temperature rise and accelerated capacity decay, which easily bring about safety problems. Aiming at the above-mentioned problems related to the charging process of lithium-ion batteries, this paper proposes an optimization strategy and charging method for lithium-ion batteries based on an improved electric-thermal coupling model. Through the HPPC experiment, the parameter identification of the second-order RC equivalent circuit model was completed, and the electric-thermal coupling model of the lithium battery was established. Taking into account the two factors of charging time and charging temperature rise, the multi-stage charging strategy of the lithium-ion battery is optimized by the particle swarm optimization algorithm. The experimental results show that the multi-stage constant current charging method proposed in this paper not only reduces the maximum temperature during the charging process by an average of 0.83% compared with the maximum temperature of the battery samples charged with the traditional constant current-constant voltage (CC-CV) charging method but also reduces the charging time by an average of 13.87%. Therefore, the proposed optimized charging strategy limits the charging temperature rise to a certain extent on the basis of ensuring fast charging and provides a certain theoretical basis for the thermal management of the battery system and the design and safe charging method of the battery charging system. Full article
(This article belongs to the Special Issue New Advances in Battery Technologies)
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15 pages, 4212 KiB  
Article
Lithium Battery State-of-Charge Estimation Based on AdaBoost.Rt-RNN
by Ran Li, Hui Sun, Xue Wei, Weiwen Ta and Haiying Wang
Energies 2022, 15(16), 6056; https://doi.org/10.3390/en15166056 - 21 Aug 2022
Cited by 6 | Viewed by 1455
Abstract
Real-time and accurate state-of-charge estimation performs an important role in the smooth operation of various electric vehicle battery management systems. Neural network theory represents one of the most effective and commonly used methods of SOC prediction. However, traditional neural network methods are disadvantaged [...] Read more.
Real-time and accurate state-of-charge estimation performs an important role in the smooth operation of various electric vehicle battery management systems. Neural network theory represents one of the most effective and commonly used methods of SOC prediction. However, traditional neural network methods are disadvantaged by such issues as the limited range of application, limited generalization ability, and low accuracy, which makes it difficult to meet the increasing safety requirements on electric vehicles. In view of these problems, an ensemble learning algorithm based on the AdaBoost.Rt is proposed in this paper. AdaBoost.Rt recurrent neural network model is purposed to ensure the accurate prediction of lithium battery SOC. Relying on a chain-connected recurrent neural network model, this method enables the correlation adaptability of sample data in the spatio-temporal dimension. The ensemble learning method was adopted to devise a method of multi-RNN model integration, with the RNN model as the base learner, thus constructing the AdaBoost.Rt-RNN strong learner model. According to the results of simulation and experimental comparisons, the integrated algorithm proposed in this paper is applicable to improve the accuracy of SOC prediction and the generalization performance of the model. Full article
(This article belongs to the Special Issue New Advances in Battery Technologies)
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Graphical abstract

19 pages, 5343 KiB  
Article
State Characterization of Lithium-Ion Battery Based on Ultrasonic Guided Wave Scanning
by Xiaoyu Li, Chuxin Wu, Chen Fu, Shanpu Zheng and Jindong Tian
Energies 2022, 15(16), 6027; https://doi.org/10.3390/en15166027 - 19 Aug 2022
Cited by 8 | Viewed by 2071
Abstract
Accurate state characterization of batteries is conducive to ensuring the safety, reliability, and efficiency of their work. In recent years, ultrasonic non-destructive testing technology has been gradually applied to battery state estimation. In this paper, research on the state characterization of lithium-ion batteries [...] Read more.
Accurate state characterization of batteries is conducive to ensuring the safety, reliability, and efficiency of their work. In recent years, ultrasonic non-destructive testing technology has been gradually applied to battery state estimation. In this paper, research on the state characterization of lithium-ion batteries based on ultrasonic guided wave (UGW) scanning is carried out. The laser Doppler vibrometer (LDV) and the X-Y stage are used to obtain the surface scanning UGW signal and the line scanning UGW signal of lithium-ion batteries under different states of charge and different aging degrees. The propagation law of UGWs in the battery is analyzed by surface scanning signals, then the energy spectrum of the signals is calculated, showing that the aging of the battery attenuates the transmission energy of UGWs. The “point” parameters are extracted from the scanning point signals. On this basis, the “line” parameters composed of line scanning multi-point signals are extracted. By analyzing the changing law of parameters during the charge–discharge process of batteries, several characteristic parameters that can be used to characterize the battery state of charge and state of health are obtained. The method has good consistency in the state characterization of the three batteries and provides a new approach for non-destructive testing and evaluation of battery states. Full article
(This article belongs to the Special Issue New Advances in Battery Technologies)
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