2017
DOI: 10.1109/tste.2017.2699288
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State of Charge Estimation of Vanadium Redox Flow Battery Based on Sliding Mode Observer and Dynamic Model Including Capacity Fading Factor

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Cited by 108 publications
(71 citation statements)
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“…Battery equivalent circuit model [46] and electrochemical model [47,48] in the forms of standard state space are usually selected to estimate battery SOC, while SOC is one of the state variables in these battery models. Then various state observers are adopted for online SOC estimation [49][50][51][52][53], such as Kalman filter (KF), extended Kalman filter (EKF), adaptive Kalman filter (AKF), unscented Kalman filter (UKF), slide mode observer and H1 filter. The accuracy of these modelbased approaches largely depends on the training of the battery models, the adopted state observers, and the parameter tuning such as the key parameters in model and the noise covariance matrix for KF observers.…”
Section: Soc Estimationmentioning
confidence: 99%
“…Battery equivalent circuit model [46] and electrochemical model [47,48] in the forms of standard state space are usually selected to estimate battery SOC, while SOC is one of the state variables in these battery models. Then various state observers are adopted for online SOC estimation [49][50][51][52][53], such as Kalman filter (KF), extended Kalman filter (EKF), adaptive Kalman filter (AKF), unscented Kalman filter (UKF), slide mode observer and H1 filter. The accuracy of these modelbased approaches largely depends on the training of the battery models, the adopted state observers, and the parameter tuning such as the key parameters in model and the noise covariance matrix for KF observers.…”
Section: Soc Estimationmentioning
confidence: 99%
“…One of the reasons for the high price of battery modules is that their electrochemical properties are difficult to grasp. Nowadays, battery-related research, in addition to materials and electrochemical fields, generally includes monitoring of battery characteristics [1][2][3][4][5][6][7][8][9][10], design of battery management systems [11][12][13][14], estimation of state of charge [15][16][17][18][19][20], estimation of state of health [21][22][23][24][25][26], and so on.…”
Section: Introductionmentioning
confidence: 99%
“…The estimation of the state of charge (SOC) of the battery is currently a very important research topic [15][16][17][18][19][20]. In general, the state of charge is defined as the ratio of the current available battery Figure 1 depicts the circuit architecture of the proposed diagnostic system, which consists of a modified class E resonant circuit, a micro-controller (MCU: dsPIC33FJ64GS606), a USB(universal serial bus) UART (universal asynchronous receiver/transmitter) module (FT-232), an auxiliary power circuit, a signal (voltage, current, and temperature) capturing circuit, and a DC (direct current) level offset circuit.…”
Section: Introductionmentioning
confidence: 99%
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“…To enable more accurate prediction of RFB behaviour and to gain more detailed knowledge about physico-chemical changes, observer-based approaches for lumped parameter models of RFBs have been developed. In some cases such models can provide simultaneous and continuous estimation of the main battery states and parameters [24], [25], [26], [27], [28]. Generally, these approaches use electrical equivalent-circuit models (ECMs) to emulate the RFB's electrical behaviour, and perform state/parameter estimation with an extended Kalman filter (EKF) [24], [29], [25], [28].…”
Section: Introductionmentioning
confidence: 99%