Battery Management System with Temperature Monitoring Through Fuzzy Logic Control

Lithium iron phosphate (LiFePO4) has become the top choice battery chemical in photovoltaic (PV) system nowadays due to numerous advantages as compared to lead acid batteries. However, LiFePO4 needs a battery management system to optimize energy utilization. State of charge (SoC), state of health (SoH), cell balancing, remaining useful life are some of its crucial parameters. This review paper discusses overview of battery management system (BMS) functions, LiFePO4 characteristics, key issues, estimation techniques, main features, and drawbacks of using this battery type.

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Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch

Calinao Jr.,

Gustilo,

Dadios

et al. 2024

JACIII

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This study integrates fuzzy logic-controlled data switching and the radial basis function neural network (RBFNN) for fault detection and classification in grid-tied solar energy systems. The fuzzy logic controller filters out invalid sensor data through a data switch, ensuring that the fault detection and classification system receives reliable input. Training data were prepared through data normalization using the z-score function and principal component analysis, thereby reducing data complexity and standardizing the inputs. The resulting RBFNN model exhibited a low mean squared error with a value of 7.67×10-4, indicating its ability to classify faults based on the actual system scenarios. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess the effectiveness of the RBFNN model. The model demonstrated high accuracy (96.4%), precision (98.281%), recall (98.013%), and F1-score (98.147%), indicating the suitability and effectiveness of the RBFNN model to identify and classify faults in grid-tied solar energy systems.

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Battery Management System with Temperature Monitoring Through Fuzzy Logic Control

Cited by 3 publications

References 9 publications

Adaptive Neuro-Fuzzy Inference System-Based DC Voltage Sensor for Solar Energy Measurement

Adaptive Neuro-Fuzzy Inference System-Based DC Voltage Sensor for Solar Energy Measurement

A Review of Smart Battery Management Systems for LiFePO₄: Key Issues and Estimation Techniques for Microgrids

Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch

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Battery Management System with Temperature Monitoring Through Fuzzy Logic Control

Cited by 3 publications

References 9 publications

Adaptive Neuro-Fuzzy Inference System-Based DC Voltage Sensor for Solar Energy Measurement

Adaptive Neuro-Fuzzy Inference System-Based DC Voltage Sensor for Solar Energy Measurement

A Review of Smart Battery Management Systems for LiFePO4: Key Issues and Estimation Techniques for Microgrids

Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch

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Product

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A Review of Smart Battery Management Systems for LiFePO₄: Key Issues and Estimation Techniques for Microgrids