18332216. APPARATUS AND METHOD FOR DIAGNOSING FAULT FOR LITHIUM-AIR BATTERY BASED POWER SUPPLY DEVICE simplified abstract (HYUNDAI MOTOR COMPANY)
Contents
APPARATUS AND METHOD FOR DIAGNOSING FAULT FOR LITHIUM-AIR BATTERY BASED POWER SUPPLY DEVICE
Organization Name
Inventor(s)
Youngsuk Cho of Sejong-si (KR)
Kyounghan Ryu of Yongin-si (KR)
APPARATUS AND METHOD FOR DIAGNOSING FAULT FOR LITHIUM-AIR BATTERY BASED POWER SUPPLY DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18332216 titled 'APPARATUS AND METHOD FOR DIAGNOSING FAULT FOR LITHIUM-AIR BATTERY BASED POWER SUPPLY DEVICE
Simplified Explanation: The patent application describes an apparatus for diagnosing faults in a lithium-air battery-based power supply device using a neural network model trained on collected data.
Key Features and Innovation:
- Data collection part measures operational state changes in power supply device components for each type of fault.
- Training part trains a neural network model to classify the type of fault using the collected data.
- Fault diagnosis part diagnoses the type of fault by inputting residual changes of each component during operation as input values for the trained neural network model.
Potential Applications: This technology can be applied in various industries utilizing lithium-air battery-based power supply devices, such as electric vehicles, renewable energy systems, and portable electronics.
Problems Solved: The technology addresses the challenge of efficiently and accurately diagnosing faults in complex power supply devices, improving maintenance and reliability.
Benefits:
- Enhanced fault diagnosis accuracy
- Increased operational efficiency
- Reduced downtime and maintenance costs
Commercial Applications: The technology can be utilized in the automotive, energy, and consumer electronics sectors to improve the reliability and performance of lithium-air battery-based power supply devices.
Prior Art: Readers can explore prior research on fault diagnosis in power supply devices and neural network applications in fault classification to understand the existing knowledge in this field.
Frequently Updated Research: Researchers are continuously exploring advancements in neural network models for fault diagnosis in various energy storage systems, including lithium-air batteries.
Questions about Lithium-Air Battery Fault Diagnosis: 1. How does the neural network model improve fault diagnosis in lithium-air battery-based power supply devices? 2. What are the potential challenges in implementing this technology in real-world applications?
Original Abstract Submitted
An apparatus for diagnosing a fault for a lithium-air battery based power supply device includes a data collection part configured to collect data by measuring operational state changes occurring in the power supply device components for each type of fault, a training part configured to train a neural network model using the collected data to classify the type of fault, and a fault diagnosis part configured to diagnose the type of fault by inputting residual changes of each component during operation of the power supply device as input values for the trained neural network model.
