Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

Jiang, Bo; Dai, Haifeng; Wei, Xuezhe; Zhu, Letao; Sun, Zhonghui

doi:10.3390/en10030390

Cited by 23 publications

(12 citation statements)

References 28 publications

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“…Many physical models based on the dynamics of the battery have been purposed recently. Xia et al [11] and Jiang et al [12] developed an equivalent circuit based model to estimate battery dynamics. Huang et al [13] defined a new variable for the variation of the potential to perform data regression on SoC and SoH estimation.…”

Section: Introductionmentioning

confidence: 99%

Semi-Empirical Capacity Fading Model for SoH Estimation of Li-Ion Batteries

et al. 2019

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A fast and accurate capacity estimation method for lithium-ion batteries is developed. This method applies our developed semi-empirical model to a discharge curve of a lithium-ion battery for the determination of its maximum stored charge capacity after each discharge cycle. This model provides an accurate state-of-health (SoH) estimation with a difference of less than 2.22% when compared with the electrochemistry-based electrical (ECBE) SoH calculation. The model parameters derived from a lithium-ion battery can also be applied to other cells in the same pack with less than 2.5% difference from the complex ECBE model, showing the extendibility of the model. The parameters (k1, k2, and k3) calculated in the work can also be used to study the changes in battery internal structure, such as capacity losses at normal conditions, as well as cycling at high temperatures. The time for estimation after each discharge cycle is only 5 s, making it is suitable for on-line in-situ estimation.

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Section: Introductionmentioning

confidence: 99%

Semi-Empirical Capacity Fading Model for SoH Estimation of Li-Ion Batteries

et al. 2019

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“…Figure 5 shows the second-order ECM. By employing a simple ECM, like the Thevenin ECM, the SoC can be directly calculated by the transformation of the model equations [38,[63][64][65]. The advantage of this approach is its simplicity, which enables easy implementation on a low-cost target microcontroller.…”

Section: Model-based Estimation Methodsmentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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“…Some studies 8,10,25 used offline identification to obtain model parameters. Other studies 23,26,32 adopted online identification methods. Pei et al 26 adopted the extended Kalman filter (EKF) 33,34 algorithm to identify model parameters online.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of state‐of‐power measurement for lithium‐ion batteries

et al. 2020

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The peak power of a battery is a vital feature for electric vehicles to maximize battery efficiency and ensure the safe operation of the system. Currently, the estimation and prediction of the state-of-power are based either on precise model algorithms or a large amount of test data. However, these methods will lead to conservative measurements. In this study, 11 Ah LiFePO 4 cells are selected as the research object, and a new peak power test experimental procedure is proposed, which is mainly based on the constant power charge/discharge modes. Moreover, the conception of power sensitivity is proposed, which will make the procedure easier to determine the set power during the constant power experiments. The results indicate that the method can assist researchers in achieving a full understanding of the peak power characteristic of batteries.

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Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

Cited by 23 publications

References 28 publications

Semi-Empirical Capacity Fading Model for SoH Estimation of Li-Ion Batteries

Semi-Empirical Capacity Fading Model for SoH Estimation of Li-Ion Batteries

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Experimental investigation of state‐of‐power measurement for lithium‐ion batteries

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