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Batteries
Special Issues
Control, Modelling, and Management of Batteries
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Control, Modelling, and Management of Batteries

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 4257

Special Issue Editors

Dr. Xiaopeng Tang

E-Mail Website
Guest Editor
Science Unit, Lingnan University, Tuen Mun, Hong Kong SAR 999077, China
Interests: lithium batteries; electric vehicles; battery management systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Lai

E-Mail Website
Guest Editor
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: management and control of the whole life cycle of lithium battery; electronic control technology of new energy vehicles

Special Issue Information

Dear Colleagues,

Lithium-ion batteries are generally regarded as key components of a sustainable society. However, they can only make a positive environmental impact if they have a long enough service life. Battery manufacturing is an energy-demanding process, and they can still be charged using electricity generated from fossil fuels. To achieve a safe yet effective utilization of these batteries, it is necessary to develop advanced techniques to control and manage these batteries.

This Special Issue will highlight recent studies that are related to the control, modeling, and management of batteries. Topics of interest include but are not limited to the following:

  1. Battery modeling, including models that describe the battery’s electrochemical behavior, dynamic behavior, etc.
  2. Estimation of the internal status of the battery and battery packs, including the state of charge, state of health, state of power, state of energy, remaining useful life, internal temperature, etc.
  3. Second-life use of retired batteries, including battery screening, battery reuse, battery recycling, etc.
  4. Techniques that can prolong the lifespan of batteries, including techniques in the stages of battery manufacturing, battery use, battery recycling, etc.
  5. Techniques that can enhance the performance of battery systems, including the power capability, energy capability, performance under extreme temperatures, etc.
  6. Battery applications, including electric vehicles, renewable energy storage systems, backup energy storage systems, etc.
  7. Beyond lithium-ion batteries, including Li–sulfur batteries, sodium-ion batteries, liquid flow batteries, fuel cells, etc.

Dr. Xiaopeng Tang
Dr. Xin Lai
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. Batteries 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 2700 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 modeling
  • battery management system
  • battery second-life usage
  • battery lifespan
  • battery safety
  • battery applications

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

14 pages, 4607 KiB  
Article
A Novel Capacity Estimation Method for Lithium-Ion Batteries Based on the Adam Algorithm
by Yingying Lian and Dongdong Qiao
Batteries 2025, 11(3), 85; https://doi.org/10.3390/batteries11030085 - 20 Feb 2025
Viewed by 358
Abstract
Accurate estimation of the capacity of lithium-ion batteries is crucial for battery management and secondary utilization, which can ensure the healthy and efficient operation of the battery system. In this paper, we propose multiple machine learning algorithms to estimate the capacity using the [...] Read more.
Accurate estimation of the capacity of lithium-ion batteries is crucial for battery management and secondary utilization, which can ensure the healthy and efficient operation of the battery system. In this paper, we propose multiple machine learning algorithms to estimate the capacity using the incremental capacity (IC) curve features, including the adaptive moment estimation (Adam) model, root mean square propagation (RMSprop) model, and support vector regression (SVR) model. The Kalman filter algorithm is first used to construct the IC curve, and the peak and corresponding voltages correlated with battery life were analyzed and extracted as capacity estimation features. The three models were then used to learn the relationship between aging features and capacity. Finally, the lithium-ion battery cycle aging data were used to validate the capacity estimation performance of the three proposed machine learning models. The results show that the Adam model performs better than the other two models, balancing efficiency and accuracy in the capacity estimation of lithium-ion batteries throughout the entire lifecycle. Full article
(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)
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24 pages, 11173 KiB  
Article
Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model
by Zhao Zhang, Runrun Zhang, Xin Liu, Chaolong Zhang, Gengzhi Sun, Yujie Zhou, Zhong Yang, Xuming Liu, Shi Chen, Xinyu Dong, Pengyu Jiang and Zhexuan Sun
Batteries 2024, 10(12), 433; https://doi.org/10.3390/batteries10120433 - 6 Dec 2024
Cited by 3 | Viewed by 1680
Abstract
Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks [...] Read more.
Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination (R2) exceeding 97%, significantly improving prediction accuracy and efficiency. Full article
(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)
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20 pages, 4412 KiB  
Article
Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm
by Juncheng Fu, Zhengxiang Song, Jinhao Meng and Chunling Wu
Batteries 2024, 10(11), 398; https://doi.org/10.3390/batteries10110398 - 8 Nov 2024
Cited by 2 | Viewed by 1564
Abstract
The accurate prediction of lithium-ion battery state of health (SOH) can extend battery life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is [...] Read more.
The accurate prediction of lithium-ion battery state of health (SOH) can extend battery life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). First, to address the limitations of traditional extreme learning machines (ELMs) in capturing non-linear features and their poor generalization ability, the concepts of auto encoders (AEs) and hybrid kernel functions are introduced to enhance ELM, resulting in the establishment of the DHKELM model for SOH prediction. Next, to tackle the challenge of parameter selection for DHKELM, an optimal point set strategy, the Gompertz growth model, and a Levy flight strategy are employed to optimize the parameters of DHKELM using IBKA before model training. Finally, the performance of IBKA-DHKELM is validated using two distinct datasets from NASA and CALCE, comparing it against ELM, DHKELM, and BKA-DHKELM. The results show that IBKA-DHKELM achieves the smallest error, with an RMSE of only 0.0062, demonstrating exceptional non-linear fitting capability, high predictive accuracy, and good robustness. Full article
(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)
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