# Self-Powered Sensing System for Electric Vehicle Drive Condition Monitoring and Driving Condition Identification in Intelligent Electric Vehicles

## TL;DR

This paper introduces a self-powered sensor system for electric vehicles that monitors drive conditions and identifies driving behaviors with high accuracy.

## Contribution

A triboelectric nanogenerator with centrifugal-force-enhanced contact is developed for stable, high-accuracy monitoring in electric vehicles.

## Key findings

- The system achieves 96.1% accuracy in diagnosing transmission component bearing faults.
- It recognizes driving behaviors like sudden acceleration with 84% accuracy and road conditions with 89.9% accuracy.
- The design enables real-time speed perception and stable signal output at high speeds.

## Abstract

It is crucial to monitor the real-time and accurate status of an electric drive system and understand its interaction with driving behavior in order to meet the higher requirements for vehicle safety and reliability in the era of autonomous driving. Traditional wired sensors have limitations in system integration and energy autonomy. This study proposes a triboelectric nanogenerator (TENG) based on the centrifugal-force-enhanced contact mechanism, which can be directly integrated into the transmission shaft of electric vehicles to construct an intelligent self-powered monitoring system. This design effectively overcomes the bottleneck of unstable signals and insufficient durability of traditional rotating TENGs at high speeds by coupling centrifugal force and spring pre-tension and outputs stable and high signal-to-noise ratio sensing signals. On this basis, this study not only achieved high-precision real-time perception of the driving system speed but also further explored the rich information embedded in the centrifugal-force-enhanced contact TENG signal and extended it to the intelligent recognition of driving behavior and road conditions. Based on signal processing and the convolutional neural network and bidirectional long short-term memory model, the system has achieved fault diagnosis of key transmission component bearings in the transmission system with an accuracy rate of up to 96.1%. At the same time, the system can effectively recognize driving behaviors such as sudden acceleration and deceleration (recognition accuracy of 84%), as well as typical road conditions such as flat, slippery, and speed bumps (recognition accuracy of 89.9%), providing key information for automatic driving algorithm calibration and driving safety improvement. The self-powered embedded sensing technology developed in this study provides a new technological path for the efficient energy management and predictive maintenance system of intelligent connected vehicles and is a key sensing node for building future autonomous transportation systems.