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A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Performance, Ageing, Reliability and Safety".

Deadline for manuscript submissions: closed (18 January 2024) | Viewed by 19048

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Prof. Dr. Fu-Kwun Wang

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Guest Editor

Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
Interests: reliability life analysis; applied statistics; supply chain management; battery management systems; secondary cells

Dr. Shih-Che Lo

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Guest Editor

Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
Interests: supply chain management; logistics management; operation research; intelligent systems; big data predictive analytics and applications; quantum computing
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Special Issue Information

Dear Colleagues,

Electric vehicles (EVs) are good for the environment because they reduce air pollution. As more and more manufacturers develop and release new vehicles, electric vehicles are the mainstream of the future. However, one of the keys to the success of electric vehicles is their range. It is the battery capacity that can be recharged quickly and regularly to get you where you want to go.

The state of health (SOH) and remaining useful life (RUL) prediction of batteries such as lithium-ion and lithium polymer are an important part of their prediction and health management (PHM).

This Special Issue highlights research efforts towards advanced battery lifetime prediction methodologies and/or algorithm development studies, in terms of contributions (i.e., research/perspective/review articles). Methodologies and characterization techniques to predict battery aging from cell to pack level are needed. Authors are encouraged to submit original articles addressing including, but not limited to, the following topics:

AI or data-driven battery life prediction;
Battery aging and lifetime prediction models;
Battery state of health estimation;
Diagnosis and prognosis of battery systems;
Lithium-ion batteries (cylindrical, prismatic, and pouch-type batteries);
Lithium polymer;
Nickel-metal hybrid batteries;
Online battery life prediction;
Physics-informed aging modeling;
Remaining useful life prediction;
Renewable energy-related technologies.

Prof. Dr. Fu-Kwun Wang
Dr. Shih-Che Lo
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.

Published Papers (10 papers)

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Research

36 pages, 6432 KiB

Open AccessArticle

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

by Hossam M. Hussein, Mustafa Esoofally, Abhishek Donekal, S M Sajjad Hossain Rafin and Osama Mohammed

Batteries 2024, 10(3), 89; https://doi.org/10.3390/batteries10030089 - 03 Mar 2024

Viewed by 1466

Abstract

Batteries have been considered a key element in several applications, ranging from grid-scale storage systems through electric vehicles to daily-use small-scale electronic devices. However, excessive charging and discharging will impair their capabilities and could cause their applications to fail catastrophically. Among several diagnostic indices, state-of-charge estimation is essential for evaluating a battery’s capabilities. Various approaches have been introduced to reach this target, including white, gray, and black box or data-driven battery models. The main objective of this work is to provide an extensive comparison of currently highly utilized machine learning-based estimation techniques. The paper thoroughly investigates these models’ architectures, computational burdens, advantages, drawbacks, and robustness validation. The evaluation’s main criteria were based on measurements recorded under various operating conditions at the Energy Systems Research Laboratory (ESRL) at FIU for the eFlex 52.8 V/5.4 kWh lithium iron phosphate battery pack. The primary outcome of this research is that, while the random forest regression (RFR) model emerges as the most effective tool for SoC estimation in lithium-ion batteries, there is potential to enhance the performance of simpler models through strategic adjustments and optimizations. Additionally, the choice of model ultimately depends on the specific requirements of the task at hand, balancing the need for accuracy with the complexity and computational resources available and how it can be merged with other SoC estimation approaches to achieve high precision. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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20 pages, 15224 KiB

Open AccessArticle

Lithium-Ion Supercapacitors and Batteries for Off-Grid PV Applications: Lifetime and Sizing

by Tarek Ibrahim, Tamas Kerekes, Dezso Sera, Abderezak Lashab and Daniel-Ioan Stroe

Batteries 2024, 10(2), 42; https://doi.org/10.3390/batteries10020042 - 23 Jan 2024

Cited by 1 | Viewed by 1800

Abstract

The intermittent nature of power generation from photovoltaics (PV) requires reliable energy storage solutions. Using the storage system outdoors exposes it to variable temperatures, affecting both its storage capacity and lifespan. Utilizing and optimizing energy storage considering climatic variations and new storage technologies is still a research gap. Therefore, this paper presents a modified sizing algorithm based on the Golden Section Search method, aimed at optimizing the number of cells in an energy storage unit, with a specific focus on the unique conditions of Denmark. The considered energy storage solutions are Lithium-ion capacitors (LiCs) and Lithium-ion batteries (LiBs), which are tested under different temperatures and C-rates rates. The algorithm aims to maximize the number of autonomy cycles—defined as periods during which the system operates independently of the grid, marked by intervals between two consecutive 0% State of Charge (SoC) occurrences. Testing scenarios include dynamic temperature and dynamic load, constant temperature at 25 °C, and constant load, considering irradiation and temperature effects and cell capacity fading over a decade. A comparative analysis reveals that, on average, the LiC storage is sized at 70–80% of the LiB storage across various scenarios. Notably, under a constant-temperature scenario, the degradation rate accelerates, particularly for LiBs. By leveraging the modified Golden Section Search algorithm, this study provides an efficient approach to the sizing problem, optimizing the number of cells and thus offering a potential solution for energy storage in off-grid PV systems. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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15 pages, 3290 KiB

Open AccessArticle

An Extended Kalman Filter Design for State-of-Charge Estimation Based on Variational Approach

by Ziheng Zhou and Chaolong Zhang

Batteries 2023, 9(12), 583; https://doi.org/10.3390/batteries9120583 - 12 Dec 2023

Cited by 4 | Viewed by 1745

Abstract

State of charge (SOC) is a very important variable for using batteries safely and reliably. To improve the accuracy of SOC estimation, a novel variational extended Kalman filter (EKF) technique based on least square error method is herein provided by establishing a second-order equivalent circuit model for the battery. It was found that when SOC decreased, resistance polarization occurred in the electrochemical model, and the parameters in the equivalent RC model varied. To decrease the modeling error in the equivalent circuit model, the system parameters were identified online depending on the SOC’s estimated result. Through the SOC-estimation process, the variation theorem was introduced, which enabled the system parameters to track the real situations based on the output measured. The experiment results reveal the comparison of the SOC-estimation results of the variational EKF algorithm, the traditional EKF algorithm, the recursive least square (RLS) EKF algorithm, and the forgotten factor recursive least square (FFRLS) EKF algorithm based on different indices, including the mean square error (MSE) and the mean absolute error (MAE). The variational EKF algorithm provided in this paper has higher estimation accuracy and robustness than the traditional EKF, which verifies the superiority and effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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24 pages, 10583 KiB

Open AccessArticle

Assessment of Health Indicators to Detect the Aging State of Commercial Second-Life Lithium-Ion Battery Cells through Basic Electrochemical Cycling

by Emanuele Michelini, Patrick Höschele, Syed Muhammad Abbas, Christian Ellersdorfer and Jörg Moser

Batteries 2023, 9(11), 542; https://doi.org/10.3390/batteries9110542 - 01 Nov 2023

Cited by 1 | Viewed by 2195

Abstract

Upon reaching certain limits, electric vehicle batteries are replaced and may find a second life in various applications. However, the state of such batteries in terms of aging and safety remains uncertain when they enter the second-life market. The aging mechanisms within these batteries involve a combination of processes, impacting their safety and performance. Presently, direct health indicators (HIs) like state of health (SOH) and internal resistance increase are utilized to assess battery aging, but they do not always provide accurate indications of the battery’s health state. This study focuses on analyzing various HIs obtained through a basic charging–discharging cycle and assessing their sensitivity to aging. Commercial 50 Ah pouch cells with different aging histories were tested, and the HIs were evaluated. Thirteen HIs out of 31 proved to be highly aging-sensitive, and thus good indicators. Namely, SOH upon charging and discharging, Coulombic efficiency, constant current discharge time, voltage relaxation profile trend, voltage–charge area upon discharging, hysteresis open circuit voltage HIs, and temperature difference between the tabs upon charging. The findings offer valuable insights for developing robust qualification algorithms and reliable battery health monitoring systems for second-life batteries, ensuring safe and efficient battery operation in diverse second-life applications. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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22 pages, 5182 KiB

Open AccessArticle

Online State-of-Health Estimation for Fast-Charging Lithium-Ion Batteries Based on a Transformer–Long Short-Term Memory Neural Network

by Yuqian Fan, Yi Li, Jifei Zhao, Linbing Wang, Chong Yan, Xiaoying Wu, Pingchuan Zhang, Jianping Wang, Guohong Gao and Liangliang Wei

Batteries 2023, 9(11), 539; https://doi.org/10.3390/batteries9110539 - 31 Oct 2023

Cited by 1 | Viewed by 1760

Abstract

With the rapid development of machine learning and cloud computing, deep learning methods based on big data have been widely applied in the assessment of lithium-ion battery health status. However, enhancing the accuracy and robustness of assessment models remains a challenge. This study introduces an innovative T-LSTM prediction network. Initially, a one-dimensional convolutional neural network (1DCNN) is employed to effectively extract local and global features from raw battery data, providing enriched inputs for subsequent networks. Subsequently, LSTM and transformer models are ingeniously combined to fully utilize their unique advantages in sequence modeling, further enhancing the accurate prediction of battery health status. Experiments were conducted using both proprietary and open-source datasets, and the results validated the accuracy and robustness of the proposed method. The experimental results on the proprietary dataset show that the T-LSTM-based estimation method exhibits excellent performance in various evaluation metrics, with MSE, RMSE, MAE, MAPE, and MAXE values of 0.43, 0.66, 0.53, 0.58, and 1.65, respectively. The performance improved by 30–50% compared to that of the other models. The method demonstrated superior performance in comparative experiments, offering novel insights for optimizing intelligent battery management and maintenance strategies. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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11 pages, 2748 KiB

Open AccessArticle

A Novel Battery State of Charge Estimation Based on Voltage Relaxation Curve

by Suhyeon Lee and Dongho Lee

Batteries 2023, 9(10), 517; https://doi.org/10.3390/batteries9100517 - 21 Oct 2023

Cited by 1 | Viewed by 1856

Abstract

Lithium-ion batteries, known for their high efficiency and high energy output, have gained significant attention as energy storage devices. Monitoring the state of charge through battery management systems plays a crucial role in enhancing the safety and extending the lifespan of lithium-ion batteries. In this paper, we propose a state-of-charge estimation method to overcome the limitations of the traditional open-circuit voltage method and electrochemical impedance spectroscopy. We verified changes in the shape of the voltage relaxation curve based on battery impedance through simulations and analyzed the impact of individual impedance on the voltage relaxation curve using differential equations. Based on this relationship, we estimated the impedance from the battery’s voltage relaxation curve through curve fitting and subsequently estimated the state of charge using a pre-established lookup table. In addition, we introduced a partial curve-fitting method to reduce the estimation time compared to the existing open-circuit voltage method and confirmed the trade-off relationship between the estimation time and estimation error. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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21 pages, 7022 KiB

Open AccessArticle

An Enhanced Single-Particle Model Using a Physics-Informed Neural Network Considering Electrolyte Dynamics for Lithium-Ion Batteries

by Chenyu Xue, Bo Jiang, Jiangong Zhu, Xuezhe Wei and Haifeng Dai

Batteries 2023, 9(10), 511; https://doi.org/10.3390/batteries9100511 - 15 Oct 2023

Cited by 4 | Viewed by 2780

Abstract

As power sources for electric vehicles, lithium-ion batteries (LIBs) have many advantages, such as high energy density and wide temperature range. In the algorithm design process for LIBs, various battery models with different model structures are needed, among which the electrochemical model is widely used due to its high accuracy. However, the electrochemical model is composed of multiple nonlinear partial differential equations (PDEs) that make the simulating process time-consuming. In this paper, a physics-informed neural network single-particle model (PINN SPM) is proposed to improve the accuracy of the single-particle model (SPM) under high C-rates, while ensuring high solving speed. In PINN SPM, an SPM-Net is designed to solve the distribution of lithium-ion concentration in the electrolyte. In the neural network learning process, a loss function is designed based on the physical constraints brought by the PDEs, which reduces the error of the neural network under dynamic working conditions. Finally, the PINN SPM proposed in this paper can achieve a maximum relative error of up to 1.2% compared with the high-fidelity data generated from the P2D model under various conditions. Additionally, the PINN SPM is 20.8% faster than traditional numerical solution methods with the same computational resources. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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20 pages, 1257 KiB

Open AccessArticle

A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization

by Dinghong Chen, Weige Zhang, Caiping Zhang, Bingxiang Sun, Haoze Chen, Sijia Yang and Xinwei Cong

Batteries 2023, 9(8), 414; https://doi.org/10.3390/batteries9080414 - 08 Aug 2023

Viewed by 1277

Abstract

The state of health (SOH) evaluation and remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) are crucial for health management. This paper proposes a novel sequence-to-sequence (Seq2Seq) prediction method for LIB capacity degradation based on the gated recurrent unit (GRU) neural network with the attention mechanism. An improved particle swarm optimization (IPSO) algorithm is developed for automatic hyperparameter search of the Seq2Seq model, which speeds up parameter convergence and avoids getting stuck in local optima. Before model training, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm decomposes the capacity degradation sequences. And the intrinsic mode function (IMF) components with the highest correlation are employed to reconstruct the sequences, reducing the influence of noise in the original data. A real-cycle-life data set under fixed operating conditions is employed to validate the superiority and effectiveness of the method. The comparison results demonstrate that the proposed model outperforms traditional GRU and RNN models. The predicted mean absolute percent error (MAPE) in SOH evaluation and RUL prediction can be as low as 0.76% and 0.24%, respectively. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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19 pages, 7232 KiB

Open AccessArticle

A Novel Fine-Tuning Model Based on Transfer Learning for Future Capacity Prediction of Lithium-Ion Batteries

by Jia-Hong Chou, Fu-Kwun Wang and Shih-Che Lo

Batteries 2023, 9(6), 325; https://doi.org/10.3390/batteries9060325 - 13 Jun 2023

Cited by 4 | Viewed by 1584

Abstract

Future capacity prediction of lithium-ion batteries is a highly researched topic in the field of battery management systems, owing to the gradual degradation of battery capacity over time due to various factors such as chemical changes within the battery, usage patterns, and operating conditions. The accurate prediction of battery capacity can aid in optimizing its usage, extending its lifespan, and mitigating the risk of unforeseen failures. In this paper, we proposed a novel fine-tuning model based on a deep learning model with a transfer learning approach comprising of two key components: offline training and online prediction. Model weights and prediction parameters were transferred from offline training using source data to the online prediction stage. The transferred Bi-directional Long Short-Term Memory with an Attention Mechanism model weights and prediction parameters were utilized to fine-tune the model by partial target data in the online prediction phase. Three battery batches with different charging policy were used to evaluate the proposed approach’s robustness, reliability, usability, and accuracy for the three charging policy batteries’ real-world data. The experiment results show that the proposed method’s efficacy improved, with an increase in the cycle number of the starting point, exhibiting a linear relationship with the starting point. The proposed method yields relative error values of 8.70%, 6.38%, 9.52%, 7.58%, 1.94%, and 2.29%, respectively, for the six target batteries in online prediction. Thus, the proposed method is effective in predicting the future capacity of lithium-ion batteries and holds potential for use in predictive maintenance applications. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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15 pages, 3029 KiB

Open AccessArticle

Transfer Learning Based on Transferability Measures for State of Health Prediction of Lithium-Ion Batteries

by Zemenu Endalamaw Amogne, Fu-Kwun Wang and Jia-Hong Chou

Batteries 2023, 9(5), 280; https://doi.org/10.3390/batteries9050280 - 19 May 2023

Cited by 6 | Viewed by 1509

Abstract

Lithium-ion (Li-ion) batteries are considered to be one of the ideal energy sources for automotive and electronic products due to their size, high levels of charge, higher energy density, and low maintenance. When Li-ion batteries are used in harsh environments or subjected to poor charging habits, etc., their degradation will be accelerated. Thus, online state of health (SOH) estimation becomes a hot research topic. In this study, normalized capacity is considered as SOH for the estimation and calculation of remaining useful lifetime (RUL). A multi-step look-ahead forecast-based deep learning model is proposed to obtain SOH estimates. A total of six batteries, including three as source datasets and three as target datasets, are used to validate the deep learning model with a transfer learning approach. Transferability measures are used to identify source and target domains by accounting for cell-to-cell differences in datasets. With regard to the SOH estimation, the root mean square errors (RMSEs) of the three target batteries are 0.0070, 0.0085, and 0.0082, respectively. Concerning RUL prediction performance, the relative errors of the three target batteries are obtained as 2.82%, 1.70%, and 0.98%, respectively. In addition, all 95% prediction intervals of RUL on the three target batteries include the end-of-life (EOL) value (=0.8). These results indicate that our method can be applied to battery SOH estimation and RUL prediction. Full article

(This article belongs to the Special Issue Advances in Battery Status Estimation and Prediction)

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