LYWSDCGQ Sensor: Fixing Incorrect Voltage Readings

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LYWSDCGQ Sensor: Fixing Incorrect Voltage Readings

Hey everyone! Today, we're diving deep into a common issue with the LYWSDCGQ temperature/humidity sensor – the incorrect voltage readings. Many users have reported that the sensor displays a voltage around 3V, which is odd considering it runs on AAA batteries that are supposed to be 1.5V each. Let's figure out why this is happening and what we can do about it.

Understanding the Problem

So, you've got your LYWSDCGQ sensor all set up, ready to monitor the temperature and humidity in your room. But when you check the battery voltage, it's showing around 3V. What's going on? This discrepancy has been a headache for many, and it stems from how the sensor reports its battery level. The sensor doesn't directly measure the voltage; instead, it infers it from the battery percentage. This inference is where things get a bit wonky.

Why does this matter? Well, accurate voltage readings are crucial for knowing when to replace the batteries. If the sensor consistently shows an inflated voltage, you might not realize the batteries are dying until it's too late, leading to unexpected downtime. Plus, it's just plain annoying to see incorrect data! The issue was initially raised in a Home Assistant bug report, highlighting that the reported voltage doesn't align with the actual battery voltage. This inconsistency points to a potential problem in the firmware or the way the data is being interpreted.

The core of the problem appears to lie in the xiaomi_ble library, specifically in the parser.py file. A particular section of the code is responsible for translating the battery level into a voltage reading. If this translation is off, it can lead to the inflated voltage readings we're seeing. Now, you might be wondering, "Why not just measure the voltage directly?" That's a great question! It's possible that the sensor's design prioritizes power efficiency and simplicity, opting for an inferred voltage reading rather than a direct measurement. Direct voltage measurement requires additional circuitry, which could increase the sensor's power consumption and cost. However, the trade-off is the potential for inaccurate readings. A potential solution could involve calibrating the voltage inference based on empirical data. By measuring the actual battery voltage at different battery levels, we can create a more accurate mapping between the battery percentage and the corresponding voltage. This calibration could be implemented in the software that interprets the sensor's data, such as Home Assistant or other home automation platforms.

Diving into the Code

Let's get a bit technical and peek under the hood. The problematic code snippet is located in the xiaomi_ble/parser.py file, specifically around line 910. This part of the code is responsible for converting the battery level (a percentage) into a voltage reading. It's here that the conversion might be introducing the discrepancy. The code might be using a linear or non-linear function to map the battery percentage to voltage, and if this function isn't calibrated correctly, it can lead to inaccurate results. It's also possible that the code assumes a certain battery discharge curve that doesn't match the actual behavior of the AAA batteries used in the sensor. Battery discharge curves can vary depending on the battery chemistry, brand, and usage conditions. If the code assumes a different discharge curve, it can lead to errors in the voltage estimation. To address this issue, it might be necessary to create a more sophisticated model that takes into account the specific characteristics of the batteries used in the sensor. This could involve using a lookup table or a more complex mathematical function to map the battery percentage to voltage.

Here's the relevant snippet from the parser.py file:

# This is a placeholder, the actual code might differ
def battery_level_to_voltage(battery_level):
    # Incorrect conversion leading to ~3V
    voltage = 2.0 + (battery_level / 100.0) * 1.0  # Example
    return voltage

In this example, the voltage is calculated based on the battery level, but the formula used might not be accurate for the specific batteries used in the LYWSDCGQ sensor. The base voltage of 2.0 and the scaling factor of 1.0 could be incorrect, leading to the inflated voltage readings. To fix this, the formula needs to be recalibrated based on empirical data. By measuring the actual battery voltage at different battery levels, we can determine the correct base voltage and scaling factor to use. This recalibration can be done by analyzing the relationship between the battery percentage and the corresponding voltage using statistical methods. Once the correct formula is determined, it can be implemented in the code to provide more accurate voltage readings. Additionally, it might be beneficial to provide a configuration option that allows users to select the type of batteries they are using. This would allow the code to use a more accurate discharge curve for the selected battery type, further improving the accuracy of the voltage readings.

Proposed Solutions

So, what can we do to fix this? Here are a few ideas:

  1. Configuration Options: Introduce different voltage configurations. This would allow users to select the correct voltage range for their batteries.
  2. Calibration: Implement a calibration routine. Users could manually calibrate the voltage reading against a multimeter.
  3. Firmware Update: If possible, push a firmware update with a corrected voltage calculation.

Diving Deeper into Configuration Options

Allowing users to select different voltage configurations is a straightforward approach. The idea is to provide a setting where users can specify the type of batteries they are using, such as alkaline or lithium. Each battery type has a different discharge curve, so the voltage reading can be adjusted accordingly. For example, alkaline batteries typically have a voltage range of 1.5V to 0.9V, while lithium batteries have a voltage range of 1.5V to 1.2V. By knowing the battery type, the software can use the appropriate discharge curve to estimate the voltage more accurately. In addition to battery type, users could also specify the number of batteries used in the sensor. This would allow the software to calculate the total voltage of the battery pack. For example, if the sensor uses two AAA batteries in series, the total voltage would be twice the voltage of a single battery. By taking into account the battery type and number of batteries, the software can provide a more accurate voltage reading. The configuration options could be implemented as a drop-down menu or a text field where users can enter the battery type and number of batteries. The selected configuration would then be stored in the sensor's memory and used to calculate the voltage reading. To make the configuration process easier, the software could provide a list of commonly used battery types with their corresponding voltage ranges. Users could simply select the battery type from the list, and the software would automatically adjust the voltage reading accordingly.

Manual Calibration for the Win!

Implementing a manual calibration routine is another viable solution. This would allow users to fine-tune the voltage reading to match the actual voltage of the batteries. The calibration process would involve measuring the voltage of the batteries using a multimeter and then entering the measured voltage into the sensor's configuration settings. The sensor would then use this calibration value to adjust the voltage reading. The calibration routine could be implemented as a simple wizard that guides users through the process. The wizard would first instruct users to measure the voltage of the batteries using a multimeter. Then, it would prompt users to enter the measured voltage into a text field. Finally, it would calculate the calibration offset and store it in the sensor's memory. To ensure accuracy, the calibration routine could include a validation step. The validation step would involve measuring the voltage of the batteries again and comparing it to the calibrated voltage reading. If the difference between the two voltages is too large, the wizard would prompt users to repeat the calibration process. The calibration routine could also provide a visual representation of the calibration offset. This would allow users to see how much the voltage reading is being adjusted. For example, the wizard could display a graph showing the original voltage reading and the calibrated voltage reading. By providing a visual representation of the calibration offset, users can gain a better understanding of the calibration process.

The Dream: A Firmware Update

The most elegant solution would be a firmware update from the manufacturer. This would involve correcting the voltage calculation algorithm in the sensor's firmware. The corrected algorithm would take into account the battery discharge curve and provide a more accurate voltage reading. The firmware update could be delivered over the air (OTA), allowing users to update their sensors without having to physically connect them to a computer. The OTA update process would involve downloading the firmware update from the manufacturer's server and then installing it on the sensor. The installation process would typically take a few minutes and would require the sensor to be connected to a power source. To ensure a smooth update process, the firmware update could include a validation step. The validation step would involve checking the integrity of the downloaded firmware before installing it on the sensor. If the firmware is corrupted, the update process would be aborted. The firmware update could also include a rollback mechanism. The rollback mechanism would allow users to revert to the previous firmware version if the update is unsuccessful. This would prevent the sensor from becoming unusable if the update process fails. In addition to correcting the voltage calculation algorithm, the firmware update could also include other improvements and bug fixes. This would make the sensor more reliable and easier to use. The firmware update could also add new features to the sensor, such as support for new battery types or improved data logging capabilities.

Next Steps

For now, the best course of action is to monitor your battery levels closely and consider replacing the batteries when the reported voltage drops below 2.5V. Hopefully, the developers of the xiaomi_ble library will address this issue in a future update. In the meantime, let's keep the discussion going and share any workarounds or solutions we find!