Deep learning-based state of charge estimation for electric vehicle batteries: Overcoming technological bottlenecks.

This study presents a novel deep learning-based approach for the State of Charge (SOC) estimation of electric vehicle (EV) batteries, addressing critical challenges in battery management and enhancing EV efficiency. Unlike conventional methods, our research leverages a diverse dataset encompassing environmental factors (e.g.

Energy regeneration: A study on dynamic capacitance adjustment technology in piezoelectric shock absorbers for electric vehicles under varied road conditions.

This study investigates the performance of dynamic capacitance regulation technology in electric vehicle piezoelectric shock absorbers for energy recovery under varying road conditions. By simulating a quarter-vehicle suspension system, this paper comprehensively analyzes the energy recovery efficiency of piezoelectric shock absorbers on gravel, speed bumps, and bumpy road conditions, comparing the performance differences between traditional fixed capacitance and dynamic capacitance. The results demonstrate that dynamic capacitance regulation technology can automatically adjust the capacitance value in response to instantaneous voltage changes, thereby enhancing energy recovery efficiency under various road conditions.

Research on tire crack detection using image deep learning method.

Driving can understand the importance of tire tread depth and air pressure, but most people are unaware of the safety risks of tire oxidation. Drivers must maintain vehicle tire quality to ensure performance, efficiency, and safety. In this study, a deep learning tire defect detection method was designed.

Application of empirical mode decomposition to improve deep learning for US GDP data forecasting.

The application of deep learning methods to construct deep neural networks for the prediction of future econometric trends and econometric data has come to receive a lot of research attention. However, it has been found that the long short-term memory (LSTM) model is unstable and overly complex. It also lacks rules for handling econometric data, which can cause errors in prediction and in the actual data.

The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment.

This paper proposes a new method called independent component analysis-variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and data analysis. ICA is a very important method in the field of machine learning.

Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet.

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image.

Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults.

Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101).

Data analysis using a combination of independent component analysis and empirical mode decomposition.

A combination of independent component analysis and empirical mode decomposition (ICA-EMD) is proposed in this paper to analyze low signal-to-noise ratio data. The advantages of ICA-EMD combination are these: ICA needs few sensory clues to separate the original source from unwanted noise and EMD can effectively separate the data into its constituting parts. The case studies reported here involve original sources contaminated by white Gaussian noise.