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Battery Storage Technology for a Sustainable Future

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D1: Advanced Energy Materials".

Deadline for manuscript submissions: closed (15 December 2018) | Viewed by 41516

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Special Issue Editors

Prof. Dr. Cher Ming Tan

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Guest Editor

Center for Reliability Science and Technologies, Chang Gung University, Taoyuan City, Taiwan
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
Special Issues, Collections and Topics in MDPI journals

Dr. Zhongbao Wei

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Guest Editor

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

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Guest Editor

Energy Research Institute @ NTU (ERIAN), Nanyang Technological University, Singapore 637141, Singapore
Interests: lithium-ion batteries; battery management; battery charge control

Prof. Dr. Michael Gerard Pecht

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Guest Editor

Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
Interests: competitive product development; product characterization and qualification; supply chain creation and management; prognostics and health management; product reliability, risk assessment and mitigation
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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. Dr. Cher Ming Tan
Dr. Zhongbao Wei
Dr. Feng Leng
Prof. Dr. 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 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. Energies is an international peer-reviewed open access semimonthly 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

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

Published Papers (9 papers)

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Research

11 pages, 3201 KiB

Open AccessArticle

Effect of Compression Ratio of Graphite Felts on the Performance of an All-Vanadium Redox Flow Battery

by Chin-Lung Hsieh, Po-Hong Tsai, Ning-Yih Hsu and Yong-Song Chen

Energies 2019, 12(2), 313; https://doi.org/10.3390/en12020313 - 19 Jan 2019

Cited by 14 | Viewed by 3919

Abstract

All-vanadium redox flow batteries (VRFBs) are considered promising candidates for large-scale energy storage systems due to their flexible power scale design, high efficiency, deep discharge, long cycle life and environmental friendliness. The performance and efficiency of a VRFB is affected by many factors, including component materials, battery design, electrolyte composition and operating conditions. Among the key components, porous electrodes play a key role, as the electrochemical reaction occurs on the fiber surface of the electrode. As such, many studies have focused on improving reaction kinetics by modifying the surface of the electrode. In this work, the effect of varying the compression ratio (CR) of graphite felts on the performance and efficiency of a VRFB are investigated. The impedance of a single VRFB under varying CRs of graphite felts at various operating conditions were also measured. The results suggest that performance of a VRFB increases with increasing CR due to the decrease of area resistance and concentration overpotential. The porous electrode compressed from 6.5 to 4 mm demonstrates the optimal energy efficiency of 73% at the operating current density 80 mA cm⁻² and electrolyte flow rate 100 mL min⁻¹. Full article

(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)

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22 pages, 8314 KiB

Open AccessFeature PaperArticle

State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries

by Xian Wang, Zhengxiang Song, Kun Yang, Xuyang Yin, Yingsan Geng and Jianhua Wang

Energies 2019, 12(1), 183; https://doi.org/10.3390/en12010183 - 07 Jan 2019

Cited by 9 | Viewed by 4060

Abstract

Lithium-bismuth liquid metal batteries have much potential for stationary energy storage applications, with characteristics such as a large capacity, high energy density, low cost, long life-span and an ability for high current charge and discharge. However, there are no publications on battery management systems or state-of-charge (SoC) estimation methods, designed specifically for these devices. In this paper, we introduce the properties of lithium-bismuth liquid metal batteries. In analyzing the difficulties of traditional SoC estimation techniques for these devices, we establish an equivalent circuit network model of a battery and evaluate three SoC estimation algorithms (the extended Kalman filter, the unscented Kalman filter and the particle filter), using constant current discharge, pulse discharge and hybrid pulse (containing charging and discharging processes) profiles. The results of experiments performed using the equivalent circuit battery model show that the unscented Kalman filter gives the most robust and accurate performance, with the least convergence time and an acceptable computation time, especially in hybrid pulse current tests. The time spent on one estimation with the three algorithms are 0.26 ms, 0.5 ms and 1.5 ms. Full article

(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)

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20 pages, 5013 KiB

Open AccessArticle

Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery

by Quan Sun, Hong Zhang, Jianrong Zhang and Wentao Ma

Energies 2018, 11(11), 3123; https://doi.org/10.3390/en11113123 - 12 Nov 2018

Cited by 20 | Viewed by 2969

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 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)

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19 pages, 4864 KiB

Open AccessArticle

An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method

by Muhammad Umair Ali, Muhammad Ahmad Kamran, Pandiyan Sathish Kumar, Himanshu, Sarvar Hussain Nengroo, Muhammad Adil Khan, Altaf Hussain and Hee-Je Kim

Energies 2018, 11(11), 2940; https://doi.org/10.3390/en11112940 - 27 Oct 2018

Cited by 27 | Viewed by 4534

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 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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27 pages, 3165 KiB

Open AccessArticle

Coupling Analysis and Performance Study of Commercial 18650 Lithium-Ion Batteries under Conditions of Temperature and Vibration

by Lijun Zhang, Zhongqiang Mu and Xiangyu Gao

Energies 2018, 11(10), 2856; https://doi.org/10.3390/en11102856 - 22 Oct 2018

Cited by 25 | Viewed by 6077

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 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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20 pages, 1637 KiB

Open AccessArticle

Constructing Accurate Equivalent Electrical Circuit Models of Lithium Iron Phosphate and Lead–Acid Battery Cells for Solar Home System Applications

by Yunhe Yu, Nishant Narayan, Victor Vega-Garita, Jelena Popovic-Gerber, Zian Qin, Marnix Wagemaker, Pavol Bauer and Miro Zeman

Energies 2018, 11(9), 2305; https://doi.org/10.3390/en11092305 - 01 Sep 2018

Cited by 26 | Viewed by 4914

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 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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23 pages, 7197 KiB

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

by Wei He, Michael Pecht, David Flynn and Fateme Dinmohammadi

Energies 2018, 11(8), 2120; https://doi.org/10.3390/en11082120 - 14 Aug 2018

Cited by 58 | Viewed by 6332

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 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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16 pages, 2136 KiB

Open AccessArticle

Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method

by Zhongbao Wei, Feng Leng, Zhongjie He, Wenyu Zhang and Kaiyuan Li

Energies 2018, 11(7), 1810; https://doi.org/10.3390/en11071810 - 11 Jul 2018

Cited by 31 | Viewed by 3893

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 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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15 pages, 4257 KiB

Open AccessArticle

A Fast Equalizer with Adaptive Balancing Current Control

by Shun-Chung Wang, Chun-Yu Liu and Yi-Hua Liu

Energies 2018, 11(5), 1052; https://doi.org/10.3390/en11051052 - 25 Apr 2018

Cited by 5 | Viewed by 3754

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 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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