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Testing and Management of Lithium-Ion Batteries

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: closed (20 October 2020) | Viewed by 69448

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Special Issue Editor

Prof. Dr. Daniel-Ioan Stroe

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Guest Editor

Department of Energy Technology, Aalborg University, Pontoppidanstræde 111, 9220 Aalborg, Denmark
Interests: energy storage; lithium-ion batteries; battery performance and lifetime testing; accelerated aging; battery performance-degradation modeling; state-of-charge estimation; state-of-health estimation; remaining useful lifetime prediction; aging mechanisms; power and energy management strategies; lithium-ion capacitors; hybrid renewable energy systems
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Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies on the subject area of “Testing and Management of Lithium-ion Batteries”. After dominating the portable electronics market, Lithium-ion batteries have become the key energy storage technology for propelling electric vehicles (EV, HEV, and PHEV), and they are entering the renewable energy storage sector (e.g., grid support applications, microgrids, renewables’ grid integration enhancement). Nevertheless, Li-ion batteries are highly non-linear systems with their performance behavior strongly influenced by the short-term and long-term operating conditions. Thus, before being deployed in a certain application, extensive testing is required in order to understand and learn the behavior of the battery at various real-life conditions. Subsequently, based on these knowledge, battery models, state estimation methods, and battery cell balancing algorithms can be developed in order to achieve an optimal management of the battery cells in a battery pack, which will ensure battery lifetime maximization and battery cost optimization.

Prof. Daniel-Ioan Stroe
Guest Editor

Manuscript Submission Information

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Keywords

Lithium-ion batteries testing
Performance and lifetime testing
Lithium-ion battery packs
Battery management systems
State-of-charge and state-of-health estimation
Lithium-ion battery balancing
Power and energy management of Lithium-ion batteries

Published Papers (12 papers)

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Research

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21 pages, 10747 KiB

Open AccessArticle

The Degradation Behavior of LiFePO₄/C Batteries during Long-Term Calendar Aging

by Xin Sui, Maciej Świerczyński, Remus Teodorescu and Daniel-Ioan Stroe

Energies 2021, 14(6), 1732; https://doi.org/10.3390/en14061732 - 20 Mar 2021

Cited by 33 | Viewed by 10564

Abstract

With widespread applications for lithium-ion batteries in energy storage systems, the performance degradation of the battery attracts more and more attention. Understanding the battery’s long-term aging characteristics is essential for the extension of the service lifetime of the battery and the safe operation of the system. In this paper, lithium iron phosphate (LiFePO₄₎ batteries were subjected to long-term (i.e., 27–43 months) calendar aging under consideration of three stress factors (i.e., time, temperature and state-of-charge (SOC) level) impact. By means of capacity measurements and resistance calculation, the battery’s long-term degradation behaviors were tracked over time. Battery aging models were established by a simple but accurate two-step nonlinear regression approach. Based on the established model, the effect of the aging temperature and SOC level on the long-term capacity fade and internal resistance increase of the battery is analyzed. Furthermore, the storage life of the battery with respect to different stress factors is predicted. The analysis results can hopefully provide suggestions for optimizing the storage condition, thereby prolonging the lifetime of batteries. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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16 pages, 682 KiB

Open AccessArticle

A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

by Shehzar Shahzad Sheikh, Mahnoor Anjum, Muhammad Abdullah Khan, Syed Ali Hassan, Hassan Abdullah Khalid, Adel Gastli and Lazhar Ben-Brahim

Energies 2020, 13(14), 3658; https://doi.org/10.3390/en13143658 - 15 Jul 2020

Cited by 33 | Viewed by 6477

Abstract

Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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15 pages, 9207 KiB

Open AccessArticle

Broadband Impedance Measurement of Lithium-Ion Battery in the Presence of Nonlinear Distortions

by Jussi Sihvo, Tomi Roinila and Daniel-Ioan Stroe

Energies 2020, 13(10), 2493; https://doi.org/10.3390/en13102493 - 15 May 2020

Cited by 19 | Viewed by 2718

Abstract

The impedance of a Lithium-ion (Li-ion) battery has been shown to be a valuable tool in evaluating the battery characteristics such as the state-of-charge (SOC) and state-of-health (SOH). Recent studies have shown impedance-measurement methods based on broadband pseudo-random sequences (PRS) and Fourier techniques. The methods can be efficiently applied in real-time applications where the conventional electrochemical-impedance spectroscopy (EIS) is not well suited to measure the impedance. The techniques based on the PRS are, however, strongly affected by the battery nonlinearities. This paper presents the use of a direct-synthesis ternary (DST) signal to minimize the effect caused by the nonlinearities. In such a signal, the second- and third-order harmonics are suppressed from the signal energy spectrum. As a result, the effect of the second- and third-order nonlinearities are suppressed from the impedance measurements. The impedance measurements are carried out for a nickel manganese cobalt Li-ion battery cell. The performance of the method is compared to the conventional EIS, as well as to other PRS signals which are more prone to battery nonlinearities. The Kronig–Kramers (K–K) transformation test is used to validate the uniqueness of the measured impedance spectra. It is shown that the measurement method based on the DST produces highly accurate impedance measurements under nonlinear distortions of the battery. The method shows a good K–K test behavior indicating that the measured impedance complies well to a linearized equivalent circuit model that can be used for the SOC and SOH estimation of the battery. Due to the good performance, low measurement time, and simplicity of the DST, the method is well suited for practical battery applications. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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15 pages, 4736 KiB

Open AccessArticle

Low Voltage Battery Management System with Internal Adaptive Charger and Fuzzy Logic Controller

by Omer Faruk Goksu, Ahmet Yigit Arabul and Revna Acar Vural

Energies 2020, 13(9), 2221; https://doi.org/10.3390/en13092221 - 02 May 2020

Cited by 3 | Viewed by 4617

Abstract

Lithium ion (Li-Ion) and lithium polymer (Li-Po) batteries need to be used within certain voltage/current limits. Failure to observe these limits may result in damage to the battery. In this work, we propose a low voltage battery management system (LV-BMS) that balances the processes of the battery cells in the battery pack and the activating-deactivating of cells by guaranteeing that the operation is within these limits. The system operates autonomously and provides energy from the internal battery. It has a modular structure and the software is designed to control the charging and discharging of eight battery cells at most. A STM32F103 microcontroller is used for system control. The fuzzy logic controller (FLC) is used to set the discharge voltage limit to prevent damage to the battery cells, shorten the settlement time and create a specialized design for charge control. The proposed structure enables solar panel or power supplies with different voltage values between 5 V and 8 V to be used for charging. The experimental results show there was a 42% increase in usage time and the voltage difference between the batteries was limited to a maximum of 65 mV. Moreover, the charge current settles at about 20 ms, which is a much faster response when compared to a PID controller. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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11 pages, 1309 KiB

Open AccessArticle

Run to Failure: Aging of Commercial Battery Cells beyond Their End of Life

by Andreas Ziegler, David Oeser, Thiemo Hein, Daniel Montesinos-Miracle and Ansgar Ackva

Energies 2020, 13(8), 1858; https://doi.org/10.3390/en13081858 - 11 Apr 2020

Cited by 6 | Viewed by 3131

Abstract

The aim of this work is to age commercial battery cells far beyond their expected lifetime. There is a gap in the literature regarding run to failure tests of lithium-ion batteries that this work intends to address. Therefore, twenty new Samsung ICR18650-26F cells were aged as a battery pack in a run to failure test. Aging took place with a constant load current and a constant charge current to accelerate capacity decrease. Important aging parameters such as capacity and internal resistance were measured at each cycle to monitor their development. The end of the test was initiated by the explosion of a single battery cell, after which the battery pack was disassembled and all parameters of the still intact single cells were measured. The distribution of all measured capacities and internal resistances is displayed graphically. This clearly shows the influence of the exploded cell on the cells in its immediate vicinity. Selected cells from this area of the battery were subjected to computed tomography (CT) to detect internal defects. The X-rays taken with computed tomography showed clear damage within the jelly roll, as well as the triggered safety mechanisms. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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17 pages, 3611 KiB

Open AccessArticle

A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs

by Zhengyu Liu, Jingjie Zhao, Hao Wang and Chao Yang

Energies 2020, 13(4), 830; https://doi.org/10.3390/en13040830 - 14 Feb 2020

Cited by 35 | Viewed by 4360

Abstract

An accurate lithium-ion battery state of health (SOH) estimate is a key factor in guaranteeing the reliability of electronic equipment. This paper proposes a new method that is based on an indirect enhanced health indicator (HI) and uses support vector regression (SVR) to estimate SOH values. First, three original features that can describe the dynamic changes of the battery charging and discharging processes are extracted. Considering the coupling relationship between pairs of the original health indicators, we use the differential evolution (DE) algorithm to optimize their corresponding feature parameters and combine them to form an enhanced health indicator. Second, this paper modifies the kernel function of the SVR model to describe the trend of SOH as the number of cycles increases, with simultaneous hyperparameters optimization via DE algorithm. Third, the proposed model and other published methods are compared in terms of accuracy on the same NASA datasets. We also evaluated the generalization performance of the model in dynamic discharging experiments. The simulation results demonstrate that the proposed method can provide more accurate SOH estimation values. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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16 pages, 5246 KiB

Open AccessArticle

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

by Xingtao Liu, Chaoyi Zheng, Ji Wu, Jinhao Meng, Daniel-Ioan Stroe and Jiajia Chen

Energies 2020, 13(2), 478; https://doi.org/10.3390/en13020478 - 18 Jan 2020

Cited by 33 | Viewed by 3399

Abstract

In this paper, an improved method for estimating the state of charge (SOC) of lithium-ion batteries is proposed, which is developed from the particle filter (PF). An improved genetic particle filter (GPF), owing to the advantages of the PF and genetic algorithm, is proposed to overcome the disadvantage of the traditional particle filter: lacking the diversity of particles. Firstly, the relationship between SOC and open-circuit voltage (OCV) is identified on the low-current OCV test. Secondly, a first-order resistor and capacitance (RC) model is established, then, the least-squares algorithm is used to identify the model parameters via the incremental current test. Thirdly, GPF and the improved GPF (IGPF) are proposed to solve the problems of the PF. The method based on the IGPF is proposed to estimate the state of power (SOP). Finally, IGPF, GPF, and PF are employed to estimate the SOC on the federal urban driving schedule (FUDS). The results show that compared with traditional PF, the errors of the IGPF are 20% lower, and compared with GPF, the maximum error of the IGPF has declined 1.6% SOC. The SOC that is estimated by the IGPF is applied to estimate the SOP for battery, considering the restrictions from the peak SOC, the voltage, and the instruction manual. The result shows that the method based on the IGPF can successfully estimate SOP. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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20 pages, 4219 KiB

Open AccessArticle

SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators

by Jianfang Jia, Jianyu Liang, Yuanhao Shi, Jie Wen, Xiaoqiong Pang and Jianchao Zeng

Energies 2020, 13(2), 375; https://doi.org/10.3390/en13020375 - 13 Jan 2020

Cited by 123 | Viewed by 7724

Abstract

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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14 pages, 5982 KiB

Open AccessArticle

Electrochemical Impedance Spectroscopy on the Performance Degradation of LiFePO₄/Graphite Lithium-Ion Battery Due to Charge-Discharge Cycling under Different C-Rates

by Yusuke Abe, Natsuki Hori and Seiji Kumagai

Energies 2019, 12(23), 4507; https://doi.org/10.3390/en12234507 - 27 Nov 2019

Cited by 57 | Viewed by 10101

Abstract

Lithium-ion batteries (LIBs) using a LiFePO₄ cathode and graphite anode were assembled in coin cell form and subjected to 1000 charge-discharge cycles at 1, 2, and 5 C at 25 °C. The performance degradation of the LIB cells under different C-rates was analyzed by electrochemical impedance spectroscopy (EIS) and scanning electron microscopy. The most severe degradation occurred at 2 C while degradation was mitigated at the highest C-rate of 5 C. EIS data of the equivalent circuit model provided information on the changes in the internal resistance. The charge-transfer resistance within all the cells increased after the cycle test, with the cell cycled at 2 C presenting the greatest increment in the charge-transfer resistance. Agglomerates were observed on the graphite anodes of the cells cycled at 2 and 5 C; these were more abundantly produced in the former cell. The lower degradation of the cell cycled at 5 C was attributed to the lowered capacity utilization of the anode. The larger cell voltage drop caused by the increased C-rate reduced the electrode potential variation allocated to the net electrochemical reactions, contributing to the charge-discharge specific capacity of the cells. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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16 pages, 4442 KiB

Open AccessArticle

Double-Layer E-Structure Equalization Circuit for Series Connected Battery Strings

by Shungang Xu, Kai Gao, Xiaobing Zhang and Kangle Li

Energies 2019, 12(22), 4252; https://doi.org/10.3390/en12224252 - 08 Nov 2019

Cited by 5 | Viewed by 1722

Abstract

In order to eliminate the voltage imbalance among battery cells when they are connected in series, the paper proposes a double-layer E-structure (DLE) equalizer based on bidirectional buck–boost converters, which has the advantage of quick equalization speed and can be applied to arbitrary number batteries. Furthermore, a novel two-stage equalization control strategy is proposed for the DLE equalizer to decrease maximum voltage gap between the maximum and minimum voltage cells. The paper analyses the working principle of proposed equalizer in detail and describes the detailed design of the control strategy and implement process. Simulation and experiment results show that the proposed equalizer can improve equalization performance of battery cells compared with adjacent cell-to-cell (AC2C) equalizer. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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19 pages, 6290 KiB

Open AccessArticle

State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation

by Chuan-Xiang Yu, Yan-Min Xie, Zhao-Yu Sang, Shi-Ya Yang and Rui Huang

Energies 2019, 12(21), 4036; https://doi.org/10.3390/en12214036 - 23 Oct 2019

Cited by 11 | Viewed by 4441

Abstract

State-of-charge estimation and on-line model modification of lithium-ion batteries are more urgently required because of the great impact of the model accuracy on the algorithm performance. This study aims to propose an improved DUKF based on the state-parameter separation. Its characteristics include: (1) State-Of-Charge (SoC) is treated as the only state variable to eliminate the strong correlation between state and parameters. (2) Two filters are ranked to run the parameter modification only when the state estimation has converged. First, the double polarization (DP) model of battery is established, and the parameters of the model are identified at both the pulse discharge and long discharge recovery under Hybrid Pulse Power Characterization (HPPC) test. Second, the implementation of the proposed algorithm is described. Third, combined with the identification results, the study elaborates that it is unreliable to use the predicted voltage error of closed-loop algorithm as the criterion to measure the accuracy of the model, while the output voltage obtained by the open-loop model with dynamic parameters can reflect the real situation. Finally, comparative experiments are designed under HPPC and DST conditions. Results show that the proposed state-parameter separated IAUKF-UKF has higher SoC estimation accuracy and better stability than traditional DUKF. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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Review

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27 pages, 4565 KiB

Open AccessReview

A Review of Battery Technology in CubeSats and Small Satellite Solutions

by Vaclav Knap, Lars Kjeldgaard Vestergaard and Daniel-Ioan Stroe

Energies 2020, 13(16), 4097; https://doi.org/10.3390/en13164097 - 07 Aug 2020

Cited by 37 | Viewed by 9165

Abstract

CubeSats and small satellite solutions are increasing in popularity as they enable a fast, cheap, and agile way for satellite applications. An essential component of nearly every satellite is the energy storage device, which is practically equal to a battery. Consequently, an overview of past, present, and future battery technologies for CubeSats is presented. CubeSats use typically commercial off-the-shelf (COTS) batteries. They are not primarily dedicated to space, so their suitability to the space environment needs to be evaluated. Batteries are also considered as potentially dangerous goods. Thus, there are guidelines and standards that specify safety criteria and tests for the batteries in order to be allowed for transportation and launch. Furthermore, the character of satellites’ missions determines their demand on batteries in terms of current rates, depth-of-discharge, and lifetime. Thus, these expectations are discussed. A market survey was also carried out to identify currently available commercial battery solutions and their parameters. This work summarizes the status, requirements, and the market situation of batteries for CubeSats. Full article

(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)

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