State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

Wang, Kai; Liu, Chunli; Sun, Jing; Zhao, Kun; Wang, Licheng; Song, Jianwei; Duan, Chongxiong; Li, Liwei

doi:10.1155/2021/8816250

Cited by 44 publications

(38 citation statements)

References 76 publications

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“…On the load side, the working mechanism of the electric vehicle charging pile is three-phase AC input. After filtering and rectification (AC/DC), pulsating DC is obtained, then the high-frequency inverter is carried out through a power conversion circuit (DC/DC), and finally, secondary high-frequency current integration filtering is carried out through a high-frequency isolation transformer to achieve stable DC output [12,13]. Impact loads such as electric arc furnaces and rolling mills will cause extremely serious voltage fluctuation, flicker, negative sequence, and harmonic disturbance during production.…”

Section: High Proportion Powermentioning

confidence: 99%

Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

et al. 2021

Mathematical Problems in Engineering

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With a high proportion of renewable distributed generation and time-varying load connected to the distribution network, great challenges have appeared in the reactive power optimization control of the active distribution networks. This paper first introduces the characteristics of active distribution networks, the mechanism and research status of wind power, photovoltaic, and other renewable distributed generators, and time-varying loads participating in reactive power and voltage optimization. Then, the paper summarizes the methods of reactive power optimization and voltage regulation of active distribution network, including multi-timescale voltage optimization, coordinated optimization of network reconfiguration and reactive power optimization, coordinated optimization of active and reactive power optimization based on model predictive control, hierarchical and zoning control of reactive power, and voltage and power electronic switch voltage regulation. The pros and cons of the reactive power optimization algorithms mentioned above are summarized. Finally, combined with the development trend of the energy Internet, the future directions of reactive power and voltage control technology in the active distribution network are discussed.

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Section: High Proportion Powermentioning

confidence: 99%

Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

et al. 2021

Mathematical Problems in Engineering

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“…Both of the high-and medium-voltage capacitors consist of dozens or hundreds of capacitance units in series. Each capacitance unit is made of aluminum foil and insulating film which are stacked together and immersed in insulating oil [28][29][30][31][32]. e CVD converts a primary high voltage into a medium voltage ranging from 10∼25 kV as the input voltage of the EMU.…”

Section: Structure and Operation Principle Of The Cvtmentioning

confidence: 99%

Studying the Effect of Stray Capacitance on the Measurement Accuracy of the CVT Based on the Boundary Element Method

et al. 2021

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The capacitive voltage transformer (CVT) is a special measuring and protecting device, which is commonly applied in high-voltage power systems. Its measurement accuracy is affected seriously by the stray capacitances of the capacitance voltage divider (CVD) to ground and other charged parts. In this study, based on the boundary element method, a mathematical model was established firstly to calculate the stray capacitance. Then, the voltage distribution of the CVD was obtained by the CVD’s equivalent circuit model. Next, the effect of stray capacitance on the voltage distribution and the voltage difference ratio (VDR) of CVD was analysed in detail. We finally designed three types of shield and optimized their structure parameters to reduce VDR. The results indicated that the average deviation rate between calculated and experimental measured voltages is only 0.015%; that is to say, the method has high calculation precision. The stray capacitance of the CVD to ground is far larger than that of the CVD to the high-voltage terminal. It results in the inhomogeneous distribution of voltage and the increase of VDR. For the test CVT, its VDR exceeds the requirement of class 0.2. Among all of the three types of shield, the C type reduced the VDR of the test CVT the most. After optimizing the structure parameters of C-type shield, the VDR is further reduced to 0.08%. It is not only in accord with the requirement of class 0.2 but also has an adequate margin.

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“…This type of algorithm has a small number of calculations and may easily be applied online, which is more suitable for steady parameter systems. The parameters of battery ECM change with alterations in the external environment (such as temperature), whereas RLS is unable to track changes in the parameters, causing significantly reduced accuracy of parameter identification with time 21 . Moreover, forgetting factor recursive least square (FFRLS) is not subject to data saturation due to environmental changes in battery ECM parameters.…”

Section: Introductionmentioning

confidence: 99%

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

Wang

Meng

Chang

et al. 2021

Intl J of Energy Research

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Summary Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium‐ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF‐AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞‐CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium‐ion battery SOC. FF‐AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD‐CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF‐AGLS and H∞‐SVD‐CKF was able to accurately track model parameters and SOC with a strong degree of robustness.

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State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

Cited by 44 publications

References 76 publications

Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

Studying the Effect of Stray Capacitance on the Measurement Accuracy of the CVT Based on the Boundary Element Method

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

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