Control, Modelling, and Management of Batteries

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

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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.

• battery modeling
• battery management system
• battery second-life usage
• battery lifespan
• battery safety
• battery applications

Title: State of Health Estimation of Lithium-ion Batteries Based on Multiphysics Features and Weighted CNN-BiLSTM
Authors: Zhang Chaolong
Affiliation: Jinling Institute of Science and Technology

Title: A comparative study of feature selection methods for machine learning based lithium-ion battery state of health estimation
Authors: Ji Wu; Zhen Cheng; Mingqiang Lin*
Affiliation: Hefei University of Technology

Title: Residual value evaluation of retired batteries based on electrochemical impedance spectroscopy and machine learning
Authors: Hanchao Lv; Pengfei Ke; Xin Lai*
Affiliation: University of Shanghai for Science and Technology

Title: A probabilistic scheme for lifetime prognostics of lithium-ion batteries using stochastic modeling and approximated Monte-Carlo filter
Authors: Haonan Chen; Guangzhong Dong
Affiliation: School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China
Abstract: The prognostics of battery health are attracting ever-growing research attention due to their important role in managing energy storage systems for vehicular and power grid applications. An accurate remaining useful life (RUL) prediction with a trustful probability density function is critical for uncertainty quantization of battery reliability and decision-making of maintenance. Therefore, this paper proposes a probabilistic scheme for lifetime prognostics of lithium-ion batteries using stochastic modeling and approximated Monte-Carlo filter. First, stochastic-process-based models with different drift parameters are established to describe aging behaviors with different degradation patterns. Then, an approximated Monte-Carlo filter using the Gaussian kernel function is proposed to online estimate model parameters and generate the probability density functions of the predicted RUL by extrapolation of degradation paths. Finally, three battery aging datasets with accelerating, decelerating, and linear degradation paths are employed to evaluate the proposed methods. The accuracy, robustness, and complexity of different stochastic processes are validated and compared to evaluate their suitability for different aging patterns. Comparative experiments with sequential-importance-resampling particle filters are also performed to evaluate the accuracy, complexity, and probability density function shape. Results indicate that the proposed approximated filter can provide similar prediction accuracy but with much lower computational complexity, and it can also generate a stable Gaussian-like probability density function, which is more suitable and trustful for the health management of energy storage systems.

Title: Lithium-ion battery SOH prediction based on data preprocessing and improved SABO optimized deep hybrid kernel extreme learning machine
Authors: Juncheng Fu a; Jinhao Meng b; Zhengxiang Song b; Chunling Wu c
Affiliation: a:National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, Xi'an Jiaotong University, Xi’an 710049, Shaanxi Province, China; b:School of Electrical Engineering, Xi'an Jiaotong University, Xi’an 710049, Shaanxi Province, China; c:School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, Shaanxi Province, China
Abstract: Accurately estimating the State of Health (SOH) in lithium-ion batteries is crucial for optimizing performance, cost management, enhancing safety, and promoting environmental sustainability. To enhance the robustness and accuracy of SOH prediction, a method combining Sequential Variational Mode Decomposition (SVMD) and an improved Subtraction Average Based Optimizer (SABO) for optimizing a Deep Hybrid Kernel Extreme Learning Machine (DHKELM) is proposed. Firstly, the original capacity sequence is decomposed using SVMD into a series of stationary components to reduce prediction difficulty and mitigate noise influence. Subsequently, for each decomposed component, a prediction model is established using DHKELM, which leverages the advantages of deep learning and extreme learning machines, offering strong generalization and nonlinear capture capabilities. Furthermore, to address the parameter selection challenge in DHKELM, the improved SABO is employed to optimize its parameters, achieving better SOH prediction results. Finally, the prediction results of each component are superimposed and combined.

Title: Advancements in Vibration Testing: Effects on Thermal Performance and Degradation of Modern Batteries
Authors: KHURSHEED SABEEL; Maher Al-Greer*; Imran Bashir
Affiliation: Teesside University
Abstract: Lithium-ion cells are increasingly adopted as primary power storage systems in modern applications such as e-bikes, powertrains, electric vehicles, satellites, and spacecraft, where severe and consistent vibration exists. The market share of this battery has also grown, thus understanding the impact of vibrations on the mechanical properties and electrical performance of such batteries. A few studies have evaluated the effects of vibration on fatigue and degradation of battery cell materials and battery pack structure. This review paper focuses on the current progress in assessing the impact of vibrations and dynamic loads on Li-ion batteries. Computational, theoretical and experimental research performed in industry and academia in the past has been reviewed. The impact of random vibrations and dynamic loads on the mechanical characteristics of battery packs has been investigated in correlation to vibration and battery performance. However, it is vital to clarify the degradation mechanism that impacts battery cells' safety and electrical performance.