“Perhaps one of the most significant drawbacks of using batteries is that they’re required to operate in a relatively narrow temperature range. The battery cells’ safety and stability depend on maintaining internal temperatures within specific limits. If the temperature exceeds the critical level on either end, thermal runaway can occur, destroying the battery or, even worse, starting a fire.
Thermal runaway is a chain reaction within a battery cell that can be very difficult to stop once it has started. It occurs when the temperature inside a battery reaches the point that causes a chemical reaction to occur inside the battery. This chemical reaction produces even more heat, which drives the temperature higher, causing further chemical reactions that create more heat.
In thermal runaway, the battery cell temperature rises incredibly fast (milliseconds). The energy stored in that battery is released very suddenly. This chain reaction creates extremely high temperatures (around 752 degrees Fahrenheit / 400 degrees Celsius). These temperatures can cause gassing of the battery and a fire that is so hot it can be nearly impossible to extinguish.”
Battery internal faults are one of the major factors causing safety concern, performance degradation, and cost increases.
The current method of anomaly detection uses parameters such as voltage and current, calculates the rate of change of internal resistance and voltage, and refers to methods such as relative temperature rise to check whether there is something in the battery pack that is broken and can no longer be used or may soon go wrong.
When these damages/faults accumulate to a certain level, a warning signal of the system may appear. It may happen that when you get a signal, serious accidents may have already occurred.
Storage battery degradation is a typical multivariate anomaly type. Data are characterized by high volumes, high dimensionality, complex parameter relationships.
In a recent project, QCM technology has been shown to deliver early warning of thermal runaway before conventional multivariate anomaly detection algorithms.
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