Special Issue "Testing and Management of Lithium-Ion Batteries"

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

Deadline for manuscript submissions: 20 April 2020.

Special Issue Editor

Prof. Daniel-Ioan Stroe
E-Mail Website1 Website2
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; battery state estimation; aging mechanisms; power and energy management strategies; 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 (6 papers)

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Research

Open AccessArticle
A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs
Energies 2020, 13(4), 830; https://doi.org/10.3390/en13040830 - 14 Feb 2020
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 [...] Read more.
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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Open AccessArticle
An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries
Energies 2020, 13(2), 478; https://doi.org/10.3390/en13020478 - 18 Jan 2020
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 [...] Read more.
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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Open AccessArticle
SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators
Energies 2020, 13(2), 375; https://doi.org/10.3390/en13020375 - 13 Jan 2020
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, [...] Read more.
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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Open AccessArticle
Electrochemical Impedance Spectroscopy on the Performance Degradation of LiFePO4/Graphite Lithium-Ion Battery Due to Charge-Discharge Cycling under Different C-Rates
Energies 2019, 12(23), 4507; https://doi.org/10.3390/en12234507 - 27 Nov 2019
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 [...] Read more.
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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Open AccessArticle
Double-Layer E-Structure Equalization Circuit for Series Connected Battery Strings
Energies 2019, 12(22), 4252; https://doi.org/10.3390/en12224252 - 08 Nov 2019
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 [...] Read more.
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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Open AccessArticle
State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation
Energies 2019, 12(21), 4036; https://doi.org/10.3390/en12214036 - 23 Oct 2019
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) [...] Read more.
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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