The potential dangers of lithium battery fires are becoming increasingly prominent, and how to warn in advance has become an important safety issue. This article introduces a new method to use sound recognition technology to predict the fire of lithium batteries. This method uses machine learning algorithm to identify the unique sounds made by the battery due to the rise in internal pressure before the fire occurs, thereby issuing an alarm in advance to avoid fire. This technology not only has high accuracy, but also maintains good stability in various background noise environments, showing its huge application potential.
The safety hazards of lithium battery fires are often worrying, and scientists have proposed a method to use sound to early warning of battery fires. Research has found that lithium-ion batteries undergo a series of chemical reactions before they catch fire, which causes the internal pressure of the battery to gradually increase, eventually causing the battery to expand. The battery housing is usually hard and cannot accommodate this expansion, so the safety valves inside the battery can break when the pressure is too high, making a unique sound. This sound is a bit similar to the clicking and hissing sound when opening a soda bottle. To this end, a research team at the National Institute of Standards and Technology (NIST) has developed a machine learning algorithm specifically used to identify this specific rupture sound. During the algorithm training process, the researchers collaborated with the laboratory of Xi'an University of Science and Technology to collect audio data from 38 explosive batteries. By adjusting the speed and tone of these audio data, the research team generated more than 1,000 unique audio samples to further train the algorithm. Test results show that this algorithm can identify the rupture sound of overheated batteries with 94% accuracy. It is worth noting that the researchers also introduced various background noises during the test, including footsteps, door closing sounds and bottle opening sounds, and found that only a few noises would interfere with the algorithm's judgment. This discovery shows the robustness of the algorithm. The research team said the technology has the potential to be used to develop a new type of fire alarm that can be installed in multiple places such as homes, offices, warehouses and electric vehicle parking lots. By issuing an alert in advance, this technology can provide people with plenty of time to evacuate and ensure personal safety. Key points: The research team uses sound recognition technology to warn of lithium battery fires in advance to ensure safety. Through machine learning algorithms, the test accuracy rate is as high as 94%, which has good robustness. It is expected to develop new fire alarms, which are widely used in various places, and provide people with safety guarantees.
This lithium battery fire warning technology based on sound recognition provides an effective way to improve battery safety with its high accuracy and robustness. It is expected to be widely used in various scenarios in the future to ensure the safety of people's lives and property. It is worth it Looking forward to its further development and application.