Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

Ma, Yutao

doi:10.3390/pr8010002

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…In their implementation, some utilize Ultracapacitor-battery hybrids to get optimal performance from both energy storage devices [13]. It is also possible to use an Ultracapacitor as the primary energy storage device in electric vehicles, for example, electric scooters and buses [14]. Fast charging capabilities make Ultracapacitor a breakthrough in battery replacement technology [15].…”

Section: Ultracapacitorsmentioning

confidence: 99%

State of charge estimation of ultracapacitor based on equivalent circuit model using adaptive neuro-fuzzy inference system

Nurdiansyah

Windarko

Rakhmawati

et al. 2022

J. Mechatron. Electr. Power Veh. Technol.

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Ultracapacitors have been attracting interest to apply as energy storage devices with advantages of fast charging capability, high power density, and long lifecycle. As a storage device, accurate monitoring is required to ensure and operate safely during the charge/discharge process. Therefore, high accuracy estimation of the state of charge (SOC) is needed to keep the Ultracapacitor working properly. This paper proposed SOC estimation using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS is tested by comparing it to true SOC based on an equivalent circuit model. To find the best method, the ANFIS is modified and tested with various membership functions of triangular, trapezoidal, and gaussian. The results show that triangular membership is the best method due to its high accuracy. An experimental test is also conducted to verify simulation results. As an overall result, the triangular membership shows the best estimation. Simulation results show SOC estimation mean absolute percentage error (MAPE) is 0.70 % for charging and 0.83 % for discharging. Furthermore, experimental results show that MAPE of SOC estimation is 0.76 % for random current. The results of simulations and experimental tests show that ANFIS with a triangular membership function has the most reliable ability with a minimum error value in estimating the state of charge on the Ultracapacitor even under conditions of indeterminate random current.

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Section: Ultracapacitorsmentioning

confidence: 99%

State of charge estimation of ultracapacitor based on equivalent circuit model using adaptive neuro-fuzzy inference system

Nurdiansyah

Windarko

Rakhmawati

et al. 2022

J. Mechatron. Electr. Power Veh. Technol.

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The role of storage in energy security performance based on diversification and concentration for distributed energy systems

Servín-Campuzano,

Domínguez-Pérez,

Marín-Mendoza

et al. 2024

Renewable Energy

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Estimating state of charge of battery in renewable energy systems: a data-driven approach with artificial neural networks

Kharseh,

Balah,

Alamara

2024

Clean Energy

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Accurately determining the state of charge is crucial for efficient battery management and reliable operation in renewable energy systems. This study presents a novel approach utilizing an artificial neural network to estimate the state of charge of a battery based on key variables such as battery voltage, charging current, load current, and temperature. A photovoltaic solar energy system was installed at the American University of Ras Al Khaimah, where a comprehensive experiment was conducted over 146 hours, resulting in the collection of 3930 high-resolution data points at 2-minute intervals. The artificial neural network model was trained on 70% of the data, while the remaining 30% was used for validation and testing. The trained model achieved a mean square error of 0.000896 and an error rate of less than 2% in the state of charge estimation. The model demonstrated excellent predictive accuracy, with a determination coefficient (R²) of 99.9%, indicating a strong correlation between the predicted and actual state of charge values. These results validate the robustness of the developed artificial neural network model and its potential for real-time state of charge estimation in renewable energy systems, providing a reliable and computationally efficient alternative to traditional state of charge estimation methods.

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Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

Cited by 3 publications

References 37 publications

State of charge estimation of ultracapacitor based on equivalent circuit model using adaptive neuro-fuzzy inference system

State of charge estimation of ultracapacitor based on equivalent circuit model using adaptive neuro-fuzzy inference system

The role of storage in energy security performance based on diversification and concentration for distributed energy systems

Estimating state of charge of battery in renewable energy systems: a data-driven approach with artificial neural networks

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