State-of-Charge Estimation of Lithium-Ion Battery Using Square Root Spherical Unscented Kalman Filter (Sqrt-UKFST) in Nanosatellite

Aung, Htet Htet; Low, Kay Soon; Goh, Shu Ting

doi:10.1109/tpel.2014.2361755

Cited by 123 publications

(60 citation statements)

References 36 publications

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“…Thus, the nonlinear function h(x x x) must play a positive role for the observer design. This is the main reason to establish the one-sided Lipschitz Condition Equation (17).…”

Section: Propertymentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

et al. 2017

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This work is focused on the state of charge (SOC) estimation of a lithium-ion battery based on a nonlinear observer. First, the second-order resistor-capacitor (RC) model of the battery pack is introduced by utilizing the physical behavior of the battery. Then, for the nonlinear function of the RC model, a one-sided Lipschitz condition is proposed to ensure that the nonlinear function can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the H ∞ method, which is formulated as linear matrix inequalities (LMIs). Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for any operating conditions. Finally, both the static and dynamic experimental cases are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter (EKF).

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“…Thus, the nonlinear function h(x x x) must play a positive role for the observer design. This is the main reason to establish the one-sided Lipschitz Condition Equation (17).…”

Section: Propertymentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

et al. 2017

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“…UKF is implemented in [98] to estimate SoC using a modification of ECM (a resistance and a capacitor correction factor), to include the impact of different current rates and SoC on the battery internal resistance, and the impact of different temperatures and current rates on the battery capacity. To deal with the variation of battery parameters due to temperature changes [106,107], we propose a SoC estimation approach and online parameter updating using a dual square root UKF based on unit spherical unscented transform. A relatively simple modification to the depicted UKF is the double UKF algorithm used in [72].…”

Section: Real Batterymentioning

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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“…The method depends more strongly on the accuracy of model parameters, which are difficult to achieve and computationally intensive. In addition, an inaccurate matrix of the system noise, such as the relevance, mean value and covariance matrix, could lead to filter divergence and affect its stability [12]. To improve the robustness of the EKF, a sigma-point KF (SPKF) is employed to better address the model nonlinearities [13].…”

Section: A Review Of Estimation Approachesmentioning

confidence: 99%

A Novel State-of-Charge Estimation Method of Lithium-Ion Batteries Combining the Grey Model and Genetic Algorithms

Chen

Wang

Liu

et al. 2018

IEEE Trans. Power Electron.

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Abstract-In order to guarantee safe and reliable operation of electric vehicle batteries and to optimise their energy and capacity utilisation, it is indispensable to estimate their state-of-charge (SoC). This study aimed to develop a novel estimation approach based on the Grey model and Genetic Algorithms without the need of a high-fidelity battery model demanding high computation power. A SoC analytical model was established using the Grey System theory based on a limited amount of incomplete data in contrast to conventional methods. The model was further improved by applying a sliding window mechanism to adjust the model parameters according to the evolving operating status and conditions. In addition, the Genetic Algorithms were introduced to identify the optimal adjustment coefficient, λ, in a traditional Grey model (1, 1) model to further improve the source estimation accuracy. For experimental verification, two types of Lithium-ion batteries were used as the device-under-test that underwent typical passenger car driving cycles. The proposed SoC estimation method were verified under diverse battery discharging conditions and it demonstrated superior accuracy and repeatability compared to the benchmarking GM method.

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State-of-Charge Estimation of Lithium-Ion Battery Using Square Root Spherical Unscented Kalman Filter (Sqrt-UKFST) in Nanosatellite

Abstract: State-of-charge estimation of lithium-ion battery using square root spherical unscented kalman filter (Sqrt-UKFST) in nanosatellite

Cited by 123 publications

References 36 publications

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

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

A Novel State-of-Charge Estimation Method of Lithium-Ion Batteries Combining the Grey Model and Genetic Algorithms

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