Erratum to ‘A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter’ [J. Power Sources 373 (2018) 40–53]

Li, Yi; Abdel-Monema, Mohamed; Gopalakrishnan, Rahul; Berecibar, Maitane; Nanini-Maury, Elise; Omar, Noshin; Bossche, Peter Van den; Mierlo, Joeri Van

doi:10.1016/j.jpowsour.2018.05.035

Cited by 8 publications

(4 citation statements)

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“…For traditional ICA methods, IC peaks are the primary premise to estimate the aging state of battery. However, there are only a limited number of peaks that can be used to estimate SoH [16][17][18][19], that is, the sample range available for SoH estimation is too narrow, and there is no guarantee that the voltage corresponding to the IC peak will be passed in each charging process. Moreover, since IC peaks are the extreme values of the incremental capacity (dQ) during the entire battery charging process, the time to calculate dQ is also the longest, accordingly.…”

Section: Introductionmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

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Section: Introductionmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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“…2.8 Other Methods. Besides these discussed methods, fuzzy logic [92][93][94][95], incremental capacity analysis (ICA) method [96,97], Gaussian process regression method, Bayesian network, particle filter method, Thevenin model, and many other methods [98][99][100][101][102][103][104][105][106][107][108][109][110][111] are available which are used for estimation of battery health conditions. Many researchers have attempted to combine more than one method simultaneously for better result and accuracy of the estimations [80,83].…”

Section: Fault Diagnostic Methodsmentioning

confidence: 99%

A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles

Sarmah

Kalita

Garg

et al. 2019

Journal of Electrochemical Energy Conversion and Storage

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Lithium-ion (Li-ion) battery pack is vital for storage of energy produced from different sources and has been extensively used for various applications such as electric vehicles (EVs), watches, cookers, etc. For an efficient real-time monitoring and fault diagnosis of battery operated systems, it is important to have a quantified information on the state-of-health (SoH) of batteries. This paper conducts comprehensive literature studies on advancement, challenges, concerns, and futuristic aspects of models and methods for SoH estimation of batteries. Based on the studies, the methods and models for SoH estimation have been summarized systematically with their advantages and disadvantages in tabular format. The prime emphasis of this review was attributed toward the development of a hybridized method which computes SoH of batteries accurately in real-time and takes self-discharge into its account. At the end, the summary of research findings and the future directions of research such as nondestructive tests (NDT) for real-time estimation of battery SoH, finding residual SoH for the recycled batteries from battery packs, integration of mechanical aspects of battery with temperature, easy assembling–dissembling of battery packs, and hybridization of battery packs with photovoltaic and super capacitor are discussed.

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“…Wang et al [31] presented an algorithm to obtain the DV curve according to the center least squares used the location interval between two influence points or the transformation parameter of the DV curve, as a result, the error was within 2.5%. Li et al [32] proposed a simple and robust smoothing method based on Gaussian filter to reduce the noise on IC curves. Zheng et al [33] presented a SOC based ICA/DVA method to address the question, that is, the conventional cell terminal voltage based ICA/DVA methods are sensitive to the changed resistance and polarization during battery aging processes.…”

Section: Soh Definition and Computationmentioning

confidence: 99%

A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach

et al. 2018

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The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery’s SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well.

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Erratum to ‘A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter’ [J. Power Sources 373 (2018) 40–53]

Cited by 8 publications

References 0 publications

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles

A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach

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