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Metals
Special Issues
Lithium Batteries: Latest Advances and Prospects
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Lithium Batteries: Latest Advances and Prospects

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 3604

Special Issue Editors

Prof. Dr. Shunli Wang

E-Mail Website
Guest Editor
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China
Interests: lithium battery modeling; state estimation; battery balancing; battery management system; new energy system
Special Issues, Collections and Topics in MDPI journals
Dr. Carlos Fernandez

E-Mail Website
Guest Editor
School of Pharmacy and Life Sciences, Robert Gordon University, Aberdeen AB107GJ, UK
Interests: nanomaterials; graphene and graphene-based compounds; energy storage devices; 2D materials; functional materials; sensors; environmental and pharmaceutical devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy, information, and environmental protection are important issues facing mankind in the 21st century. Faced with the exhaustion of non-renewable energy, such as oil, the research, and development of clean energy and renewable energy has attracted extensive attention from all countries.

With the deepening of its research, it is widely believed that lithium-ion batteries will become one of the main candidate power sources in the 21st century. The significant advantages of lithium-ion batteries lie in the high specific energy, long cycle life, low self-discharge rate, no memory effect, and environmental pollution. At present, the research hotspots of lithium-ion batteries mainly focus on three aspects: large capacity, long life, and safety.

At present, in-depth research has been carried out in battery design, cathode and anode material preparation process, electrolyte and additive improvement, battery production process, and integrated battery protection circuit, and a large number of research results have been applied to production practice. The main obstacle and bottlenecks of lithium-ion batteries are the charging time and safety. The security issue of lithium-ion batteries has always been a major problem plaguing the industry. The recall of electronic devices and accidents also make consumers pay more and more attention to the safety of batteries. This has caused widespread concern about the safety of batteries.

For this Special Issue in Metals, we welcome reviews and articles in the areas of principle, theoretical calculation, characterization, and applications of lithium-ion batteries.

Prof. Dr. Shunli Wang
Dr. Carlos Fernandez
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. Metals 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 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

  • lithium-ion battery
  • equivalent modeling
  • neural networks
  • battery management system
  • state of charge
  • state of health
  • state of energy
  • state of safety
  • remaining useful life prediction
  • deep learning
  • data-driven forecasting
  • support vector machine
  • safe protection
  • security maintenance.

Published Papers (2 papers)

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Research

18 pages, 4195 KiB  
Article
A Novel Adaptive Back Propagation Neural Network-Unscented Kalman Filtering Algorithm for Accurate Lithium-Ion Battery State of Charge Estimation
by Yangtao Wang, Shunli Wang, Yongcun Fan, Yanxin Xie and Carlos Fernandez
Metals 2022, 12(8), 1369; https://0-doi-org.brum.beds.ac.uk/10.3390/met12081369 - 18 Aug 2022
Cited by 3 | Viewed by 1312
Abstract
Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint [...] Read more.
Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint estimation algorithm, the Adaptive Back Propagation Neural Network and Unscented Kalman Filtering algorithm (ABP-UKF), is proposed. It combines the advantages of the robust learning rate in the Back Propagation (BP) neural network and the linearization error reduction in the Unscented Kalman Filtering (UKF) algorithm. In the BP neural network part, the self-adjustment of the learning factor accompanies the whole estimation process, and the improvement of the self-adjustment algorithm corrects the shortcomings of the UKF algorithm. In the verification part, the model is validated using a segmented double-exponential fit. Using the Ampere-hour integration method as the reference value, the estimation results of the UKF algorithm and the Back Propagation Neural Network and Unscented Kalman Filtering (BP-UKF) algorithm are compared, and the estimation accuracy of the proposed method is improved by 1.29% under the Hybrid Pulse Power Characterization (HPPC) working conditions, 1.28% under the Beijing Bus Dynamic Stress Test (BBDST) working conditions, and 2.24% under the Dynamic Stress Test (DST) working conditions. The proposed ABP-UKF algorithm has good results in estimating the SOC of lithium-ion batteries and will play an important role in the high-precision energy management process. Full article
(This article belongs to the Special Issue Lithium Batteries: Latest Advances and Prospects)
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17 pages, 2426 KiB  
Article
Incremental Capacity Curve Health-Indicator Extraction Based on Gaussian Filter and Improved Relevance Vector Machine for Lithium–Ion Battery Remaining Useful Life Estimation
by Yongcun Fan, Jingsong Qiu, Shunli Wang, Xiao Yang, Donglei Liu and Carlos Fernandez
Metals 2022, 12(8), 1331; https://0-doi-org.brum.beds.ac.uk/10.3390/met12081331 - 09 Aug 2022
Cited by 4 | Viewed by 1663
Abstract
Accurate prediction of the remaining useful life (RUL) of lithium–ion batteries is the focus of lithium–ion battery health management. To achieve high–precision RUL estimation of lithium–ion batteries, a novel RUL prediction model is proposed by combining the extraction of health indicators based on [...] Read more.
Accurate prediction of the remaining useful life (RUL) of lithium–ion batteries is the focus of lithium–ion battery health management. To achieve high–precision RUL estimation of lithium–ion batteries, a novel RUL prediction model is proposed by combining the extraction of health indicators based on incremental capacity curve (IC) and the method of improved adaptive relevance vector machine (RVM). First, the IC curve is extracted based on the charging current and voltage data. To attenuate the noise effects on the IC curve, Gaussian filtering is used and the optimal filtering window is determined to remove the noise interference. Based on this, the peak characteristics of the IC curve are analyzed and four groups of health indicators are extracted, and the strong correlation between health indicators and capacity degradation is determined using Pearson correlation analysis. Then, to optimize the traditional fixed kernel parameter RVM model, an RVM regression model whose kernel parameters are optimized by the Bayesian algorithm is established. Finally, four sets of datasets under CS2 battery in the public dataset of the University of Maryland are carried out for experimental validation. The validation results show that the improved RVM model has better short–term prediction performance and long–term prediction stability, the RUL prediction error is less than 20 cycles, and the mean absolute error is less than 0.02. The performance of the improved RVM model is better than that of the traditional RVM model. Full article
(This article belongs to the Special Issue Lithium Batteries: Latest Advances and Prospects)
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