# A Hybrid Machine Learning Model for Dynamic Level Detection of Lead-Acid Battery Electrolyte Using a Flat-Plate Capacitive Sensor

## TL;DR

A new hybrid machine learning model improves the accuracy of detecting electrolyte levels in lead-acid batteries during rapid changes.

## Contribution

The novel Poly-LSTM model combines polynomial features with LSTM to reduce dynamic measurement errors in capacitive sensors.

## Key findings

- The Poly-LSTM model achieves an average absolute error of 0.5319 mm during rapid electrolyte level drops.
- The model outperforms other methods in terms of accuracy for dynamic level detection.
- It effectively captures nonlinear and temporal dependencies in sensor data.

## Abstract

Abnormal electrolyte levels can lead to failures in lead-acid batteries. The capacitive method, as a non-invasive liquid level inspection technique, can be applied to the nondestructive detection of electrolyte level abnormalities in lead-acid batteries. However, due to the high viscosity of sulfuric acid in lead-acid batteries, residual liquid films are easily adhered to the tube walls during rapid liquid level drops, resulting in significant dynamic measurement errors in capacitive methods. To eliminate dynamic measurement errors caused by residual liquid film adhesion, this study proposes a hybrid deep learning model—Poly-LSTM. This model combines polynomial feature generation with a Long Short-Term Memory (LSTM) network. First, polynomial features are generated to explicitly capture the complex nonlinear and coupling effects in the sensor inputs. Subsequently, the LSTM network processes these features to model their temporal dependencies. Finally, the time information encoded by the LSTM is used to generate accurate liquid level predictions. Experimental results show that this method outperforms other comparative models in terms of liquid level estimation accuracy. At a rapid drop rate of 0.12 mm/s, the average absolute error (MAE) is 0.5319 mm, the root mean square error (RMSE) is 0.7180 mm, and the mean absolute percentage error (MAPE) is 0.1320%.