Random Load Pattern Recognition of Test Road Based on a Laser Direct Writing Carbon-Based Strain Sensor and a Deep Neural Network

The practical application of hydrogen-based fuel cell vehicles (HFCVs) requires the design of advanced stress monitoring system on the hydrogen storage devices, relying on accurate feedback and recognition from the vehicles load conditions. Herein, we proposed an intelligent monitoring method for continuous monitoring of dynamic load parameters and load recognition by combining carbon-based strain sensor, data acquisition and wireless transmission embedded unit, and machine learning (ML). The carbon-based strain sensor, prepared by laser direct writing (LDW) on polyimide (PI) film, exhibited a sensitivity of 6.08 and a frequency response of 1 Hz for effectively measuring the strain status under varying road conditions. A miniaturized and highly integrated data acquisition and wireless transmission unit was adopted to implement edge computing and wireless communication. Wavelet packet decomposition (WPD) and Hilbert-Huang transform (HHT) were fused to extract intrinsic features in random load spectrum, and the distance-based feature evaluation method was used to evaluate sensitive features to optimize the structure of the deep neural network (DNN). The DNN model could effectively recognize six types of test road conditions, with a test accuracy over 90%. This research effectively advances the design of the loading condition monitoring system of the HFCVs hydrogen storage device.

Automated summary

This paper presents an intelligent monitoring method for continuous monitoring of dynamic load parameters and load recognition in hydrogen-based fuel cell vehicles (HFCVs) using carbon-based strain sensor, data acquisition and wireless transmission embedded unit, and machine learning. The research shows that this method can effectively recognize different road conditions and optimize the structure of the deep neural network.