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Special Issue "Battery Storage Technology for a Sustainable Future"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: 15 December 2018

Special Issue Editors

Guest Editor
Prof. Cher Ming Tan

Center for Reliability Science and Technologies, Chang Gung University, Taoyuan City, Taiwan
Website | E-Mail
Interests: Reliability and failure physics modeling of electronic components and systems; Finite element modeling of materials degradation; Reliability of Li Ion battery and high power LEDs; Statistical modeling of engineering systems; Nano-materials and devices reliability; Prognosis & health management of engineering system
Guest Editor
Dr. Zhongbao Wei

Energy Research Institute @ NTU (ERIAN), Nanyang Technological University, Singapore 637141, Singapore
Website | E-Mail
Interests: lithium-ion batteries; all-vanadium redox flow battery; battery management; system identification; condition monitoring; battery charge control
Guest Editor
Dr. Feng Leng

Energy Research Institute @ NTU (ERIAN), Nanyang Technological University, Singapore 637141, Singapore
E-Mail
Guest Editor
Prof. Michael Gerard Pecht

Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
Website | E-Mail

Special Issue Information

Dear Colleagues,

The emerging concerns over fossil-fuel supply depletion, global warming and environmental pollution have led to increasing interest in the utility of renewable energies, such as wind and solar, which, unfortunately, are highly intermittent and difficult to match with users’ requirements. Battery storage systems are the key technology to address the intermittency and facilitate the future penetration of renewables in a stable and consistent manner. The battery systems are also at the forefront of applications for the end-user sector electrification such as electrical vehicles to pursuit an efficient and low-carbon society. However, major challenges still exist in the advance of battery technology, such as the battery management and control, system prognostics, reliability analysis, and thermal management. The Special Issue, therefore, seeks to contribute to the battery storage agenda through enhanced scientific and multi-disciplinary knowledge related to modeling, reliability, prognosis, management and control. We invite research articles, reviews, and case studies that provide critical overview on the state-of-the-art technologies from different disciplines. With this Special Issue we aim to present a platform to communicate and synergize knowledge from both specialized and interdisciplinary studies. Topics of interest of this Special Issue include, but are not limited to:

  • Advanced energy storage technologies;
  • Reliability and failure physics modeling;
  • Diagnosis, prognosis, and health management for battery systems;
  • Battery system modeling, simulation, and optimization;
  • Application of battery system in electrical or hybrid driven systems;
  • Online condition monitoring methods for SOC, SOH, SOF, RUL, etc.;
  • Optimal charging techniques;
  • Cell balancing, consistency assessment, thermal modeling and management.

Prof. Cher Ming Tan
Dr. Zhongbao Wei
Dr. Feng Leng
Prof. Michael Gerard Pecht
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 papers will be 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 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 1600 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

  • Diagnosis prognosis, and health management
  • Reliability
  • Testing and modeling
  • Battery management system
  • State estimation
  • Charge control
  • Cell balancing
  • Thermal modeling

Published Papers (7 papers)

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Research

Open AccessArticle Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery
Energies 2018, 11(11), 3123; https://doi.org/10.3390/en11113123 (registering DOI)
Received: 6 October 2018 / Revised: 1 November 2018 / Accepted: 10 November 2018 / Published: 12 November 2018
PDF Full-text (2353 KB)
Abstract
As an effective computing technique, Kalman filter (KF) currently plays an important role in state of charge (SOC) estimation in battery management systems (BMS). However, the traditional KF with mean square error (MSE) loss faces some difficulties in handling the presence of non-Gaussian
[...] Read more.
As an effective computing technique, Kalman filter (KF) currently plays an important role in state of charge (SOC) estimation in battery management systems (BMS). However, the traditional KF with mean square error (MSE) loss faces some difficulties in handling the presence of non-Gaussian noise in the system. To ensure higher estimation accuracy under this condition, a robust SOC approach using correntropy unscented KF (CUKF) filter is proposed in this paper. The new approach was developed by replacing the MSE in traditional UKF with correntropy loss. As a robust estimation method, CUKF enables the estimate process to be achieved with stable and lower estimation error performance. To further improve the performance of CUKF, an adaptive update strategy of the process and measurement error covariance matrices was introduced into CUKF to design an adaptive CUKF (ACUKF). Experiment results showed that the proposed ACUKF-based SOC estimation method could achieve accurate estimate compared to CUKF, UKF, and adaptive UKF on real measurement data in the presence of non-Gaussian system noises. Full article
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
Open AccessArticle An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method
Energies 2018, 11(11), 2940; https://doi.org/10.3390/en11112940
Received: 23 August 2018 / Revised: 10 October 2018 / Accepted: 26 October 2018 / Published: 27 October 2018
PDF Full-text (4864 KB) | HTML Full-text | XML Full-text
Abstract
Reliable and accurate state of charge (SOC) monitoring is the most crucial part in the design of an electric vehicle (EV) battery management system (BMS). The lithium ion battery (LIB) is a highly complex electrochemical system, which performance changes with age. Therefore, measuring
[...] Read more.
Reliable and accurate state of charge (SOC) monitoring is the most crucial part in the design of an electric vehicle (EV) battery management system (BMS). The lithium ion battery (LIB) is a highly complex electrochemical system, which performance changes with age. Therefore, measuring the SOC of a battery is a very complex and tedious process. This paper presents an online data-driven battery model identification method, where the battery parameters are updated using the Lagrange multiplier method. A battery model with unknown battery parameters was formulated in such a way that the terminal voltage at an instant time step is a linear combination of the voltages and load current. A cost function was defined to determine the optimal values of the unknown parameters with different data points measured experimentally. The constraints were added in the modified cost function using Lagrange multiplier method and the optimal value of update vector was determined using the gradient approach. An adaptive open circuit voltage (OCV) and SOC estimator was designed for the LIB. The experimental results showed that the proposed estimator is quite accurate and robust. The proposed method effectively tracks the time-varying parameters of a battery with high accuracy. During the SOC estimation, the maximum noted error was 1.28%. The convergence speed of the proposed method was only 81 s with a deliberate 100% initial error. Owing to the high accuracy and robustness, the proposed method can be used in the design of a BMS for real time applications. Full article
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
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Open AccessArticle Coupling Analysis and Performance Study of Commercial 18650 Lithium-Ion Batteries under Conditions of Temperature and Vibration
Energies 2018, 11(10), 2856; https://doi.org/10.3390/en11102856
Received: 26 September 2018 / Revised: 13 October 2018 / Accepted: 18 October 2018 / Published: 22 October 2018
PDF Full-text (3165 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
At present, a variety of standardized 18650 commercial cylindrical lithium-ion batteries are widely used in new energy automotive industries. In this paper, the Panasonic NCR18650PF cylindrical lithium-ion batteries were studied. The NEWWARE BTS4000 battery test platform is used to test the electrical performances
[...] Read more.
At present, a variety of standardized 18650 commercial cylindrical lithium-ion batteries are widely used in new energy automotive industries. In this paper, the Panasonic NCR18650PF cylindrical lithium-ion batteries were studied. The NEWWARE BTS4000 battery test platform is used to test the electrical performances under temperature, vibration and temperature-vibration coupling conditions. Under the temperature conditions, the discharge capacity of the same battery at the low temperature was only 85.9% of that at the high temperature. Under the vibration condition, mathematical statistics methods (the Wilcoxon Rank-Sum test and the Kruskal-Wallis test) were used to analyze changes of the battery capacity and the internal resistance. Changes at a confidence level of 95% in the capacity and the internal resistance were considered to be significantly different between the vibration conditions at 5 Hz, 10 Hz, 20 Hz and 30 Hz versus the non-vibration condition. The internal resistance of the battery under the Y-direction vibration was the largest, and the difference was significant. Under the temperature-vibration coupling conditions, the orthogonal table L9 (34) was designed. It was found out that three factors were arranged in order of temperature, vibration frequency and vibration direction. Among them, the temperature factor is the main influencing factor affecting the performance of lithium-ion batteries. Full article
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
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Open AccessArticle Constructing Accurate Equivalent Electrical Circuit Models of Lithium Iron Phosphate and Lead–Acid Battery Cells for Solar Home System Applications
Energies 2018, 11(9), 2305; https://doi.org/10.3390/en11092305
Received: 7 August 2018 / Revised: 20 August 2018 / Accepted: 22 August 2018 / Published: 1 September 2018
PDF Full-text (1637 KB) | HTML Full-text | XML Full-text
Abstract
The past few years have seen strong growth of solar-based off-grid energy solutions such as Solar Home Systems (SHS) as a means to ameliorate the grave problem of energy poverty. Battery storage is an essential component of SHS. An accurate battery model can
[...] Read more.
The past few years have seen strong growth of solar-based off-grid energy solutions such as Solar Home Systems (SHS) as a means to ameliorate the grave problem of energy poverty. Battery storage is an essential component of SHS. An accurate battery model can play a vital role in SHS design. Knowing the dynamic behaviour of the battery is important for the battery sizing and estimating the battery behaviour for the chosen application at the system design stage. In this paper, an accurate cell level dynamic battery model based on the electrical equivalent circuit is constructed for two battery technologies: the valve regulated lead–acid (VRLA) battery and the LiFePO 4 (LFP) battery. Series of experiments were performed to obtain the relevant model parameters. This model is built for low C-rate applications (lower than 0.5 C-rate) as expected in SHS. The model considers the non-linear relation between the state of charge ( S O C ) and open circuit voltage ( V OC ) for both technologies. Additionally, the equivalent electrical circuit model for the VRLA battery was improved by including a 2nd order RC pair. The simulated model differs from the experimentally obtained result by less than 2%. This cell level battery model can be potentially scaled to battery pack level with flexible capacity, making the dynamic battery model a useful tool in SHS design. Full article
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
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Open AccessFeature PaperArticle A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering
Energies 2018, 11(8), 2120; https://doi.org/10.3390/en11082120
Received: 25 July 2018 / Revised: 7 August 2018 / Accepted: 9 August 2018 / Published: 14 August 2018
PDF Full-text (7197 KB) | HTML Full-text | XML Full-text
Abstract
State-of-charge (SOC) is one of the most critical parameters in battery management systems (BMSs). SOC is defined as the percentage of the remaining charge inside a battery to the full charge, and thus ranges from 0% to 100%. This percentage value provides important
[...] Read more.
State-of-charge (SOC) is one of the most critical parameters in battery management systems (BMSs). SOC is defined as the percentage of the remaining charge inside a battery to the full charge, and thus ranges from 0% to 100%. This percentage value provides important information to manufacturers about the performance of the battery and can help end-users identify when the battery must be recharged. Inaccurate estimation of the battery SOC may cause over-charge or over-discharge events with significant implications for system safety and reliability. Therefore, it is crucial to develop methods for improving the estimation accuracy of battery SOC. This paper presents an electrochemical model for lithium-ion battery SOC estimation involving the battery’s internal physical and chemical properties such as lithium concentrations. To solve the computationally complex solid-phase diffusion partial differential equations (PDEs) in the model, an efficient method based on projection with optimized basis functions is presented. Then, a novel moving-window filtering (MWF) algorithm is developed to improve the convergence rate of the state filters. The results show that the developed electrochemical model generates 20 times fewer equations compared with finite difference-based methods without losing accuracy. In addition, the proposed projection-based solution method is three times more efficient than the conventional state filtering methods such as Kalman filter. Full article
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
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Open AccessArticle Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method
Energies 2018, 11(7), 1810; https://doi.org/10.3390/en11071810
Received: 21 June 2018 / Revised: 8 July 2018 / Accepted: 9 July 2018 / Published: 11 July 2018
PDF Full-text (2136 KB) | HTML Full-text | XML Full-text
Abstract
The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint
[...] Read more.
The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation’s alertness and numerical stability so as to achieve an accurate online adaption of model parameters. Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data–model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy. Full article
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
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Open AccessArticle A Fast Equalizer with Adaptive Balancing Current Control
Energies 2018, 11(5), 1052; https://doi.org/10.3390/en11051052
Received: 16 March 2018 / Revised: 12 April 2018 / Accepted: 12 April 2018 / Published: 25 April 2018
PDF Full-text (4257 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a fast equalizer for series-connected battery packs with adaptive balancing current control is proposed. As the duty cycle of the power switch in conventional equalizers is kept constant during the equalization process, smaller voltage difference between cells will decrease balancing
[...] Read more.
In this paper, a fast equalizer for series-connected battery packs with adaptive balancing current control is proposed. As the duty cycle of the power switch in conventional equalizers is kept constant during the equalization process, smaller voltage difference between cells will decrease balancing current and consequently result in extended balancing time, especially in the later phase of equalization. To deal with this problem and take the battery nonlinearity and circuit parameter non-ideality into consideration, an adaptive balancing current control based on a fuzzy logic inference is proposed. The presented approach can adjust the duty ratio adaptively, according to voltages of individual cells and pack, to keep the balancing current nearly constant: Therefore, the balancing time can be shortened and the balancing efficiency can be improved. Finally, experimental results of three compared methods will be given and discussed to validate the feasibility, effectiveness, and performance improvement of the studied method. Full article
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
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