Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery

Wei, Zhongbao; Tseng, King Jet; Wai, Nyunt; Lim, Tuti Mariana; Skyllas‐Kazacos, Maria

doi:10.1016/j.jpowsour.2016.09.123

Cited by 174 publications

(67 citation statements)

References 42 publications

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“…Then, the system is applied to the Kalman filter, resulting in an extended Kalman filter (EKF) on the true nonlinear system applied for the SOC estimation [44,51,52,82,[89][90][91][92][93][94][95].…”

Section: Adaptive Filter Algorithmmentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

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Section: Adaptive Filter Algorithmmentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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“…Lithium‐ion batteries contain various physicochemical reactions, which are sensitive to temperature, current rate, aging, and SoC, so time‐invariant ECMs are inapplicable and some online and offline approaches are developed to update ECM parameters . For the pursuit of battery states, separate estimators for states and parameters may greatly increase the computational burden while joint estimators may bring cross‐interference problems . In this work, a lightweight filter is used to update the parameters independently, with which the problems of high computational cost and cross‐interference can be effectively alleviated.…”

Section: Battery Modelingmentioning

confidence: 99%

Available power prediction limited by multiple constraints for LiFePO₄ batteries based on central difference Kalman filter

Xie

Chen

2018

Int J Energy Res

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Summary The available power of vehicle‐mounted batteries needs to be real‐time predicted to accommodate prospective driving demands of overtaking, gradient climbing, constant‐speed cruising, and regenerative braking. Generally, battery power capabilities are limited by multiple constraints, for example, terminal voltage, current, state‐of‐charge (SoC), and State‐of‐Energy (SoE). This paper constructs SoC and SoE estimators resorting to the square‐root central difference Kalman filter (SR‐CDKF) and an equivalent circuit model with online updated parameters. In addition, a battery thermal evolution model is formulated, whereby the ordinarily ignored temperature constraint is taken into account. Based on above achievements, a battery power prediction scheme conforming to multiple constraints is realized. Finally, experimental verifications are conducted on a LiFePO4 battery pack subject to consecutive Federal Urban Driving Schedule profiles. The SR‐CDKF‐based SoC and SoE estimators give accurate results under different conditions. In contrast to the conventional PNGV‐HPPC method, the proposed method behaves with more reliability and robustness at different time horizons, temperatures, and aging levels. The assessment results justify the effectiveness of the battery modeling and algorithm utilization efforts.

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“…Theoretically, a higher order ECM can represent a wider bandwidth of the battery application and can generate more accurate voltage estimation results. However, the high order ECM can not only increase the computational burden, but also reduce the numerical stability for the further battery states' estimation [9,10]. Hence, considering a tradeoff among the model fidelity, the computational burden and the numerical stability, the second order ECM is employed in this paper [11][12][13][14][15][16][17][18].…”

Section: Review Of the Literaturementioning

confidence: 99%

“…In order to select the proper experimental datasets that can better describe the charging characteristic of the battery, the profiles of the polarization voltage during the pulse-charging and the following rest periods, which are also calculated from Equation (10), are compared in Figure 4. Figure 4a shows the polarization voltage under the pulse-charging excitation, and Figure 4b plots the absolute values of the polarization voltage during the following rest.…”

Section: Rc Network Parameters For the CC Charging Scenariomentioning

confidence: 99%

Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications

et al. 2016

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This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.

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Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery

Cited by 174 publications

References 42 publications

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

Available power prediction limited by multiple constraints for LiFePO₄ batteries based on central difference Kalman filter

Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications

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Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery

Cited by 174 publications

References 42 publications

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

Available power prediction limited by multiple constraints for LiFePO4 batteries based on central difference Kalman filter

Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications

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Available power prediction limited by multiple constraints for LiFePO₄ batteries based on central difference Kalman filter