However, the prediction model is trained based on all the battery aging data from unused to a failure threshold, so it is still necessary to use matrix data at different aging stages to analyze its contribution in predicting the remaining life to comprehensively evaluate the performance of each area in the entire learning process.
This currently only works if your battery-level or low-battery state is the actual state of a sensor entity. If your entities just expose the battery level as an attribute this is currently not considered but could be extended to do this. Click the badge to import this Blueprint: (needs Home Assistant Core 2021.3 or higher)
For example, the capacity data of battery #3 and battery #47 in region 9 show some importance in predicting their respective remaining life, while the capacity data of the other two batteries in this area are almost useless, and this phenomenon is more evident in the temperature data.
There is a target input for sensors you want to manually exclude from low-battery detection. It will only work for entities. If you select a device, you need to expand it so the underlying sensor is selected. Given the nature of this automation this approach has a few gotchas too:
That being said, if you’re just trying to measure battery level, voltage is a fairly safe method, but be aware that adding anything to your battery can be a risk, and may drain it more than not having monitoring. The easy way is to measure voltage and compare against expected. A 2.4V battery may measure 2.46 at full, and 2.12 at empty, for example.
In summary, the MAE of all batteries is between 3 and 6 cycles, and the errors are within a reasonable range, which proves that the model established by fusing the CNN and LSTM in this paper can accurately predict the remaining life of batteries. 4.2. Life prediction model interpretation and analysis
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