Fault Prediction and Maintenance Decision Support of New Energy Vehicles Based on Big Data Analysis

The increasing reliance on New Energy Vehicles (NEVs) has highlighted the need for efficient fault prediction and maintenance strategies to ensure their optimal performance and longevity. This study introduces the Refined Puffin Algorithm-refined Scalable Random Forest Tree (RPA-SRFT), a novel hybrid model designed to predict faults and optimize maintenance decisions in NEVs. The dataset used fault prediction and optimization in NEV. It covers multiple vehicle models and includes real-time operational data for fault prediction. Data preprocessing includes cleaning and normalization to handle missing values, remove noise, and scale the data to ensure consistency across features. Feature extraction is performed using Principal Component Analysis (PCA), which reduces dimensionality while retaining key information necessary for accurate fault prediction. The Refined Puffin Algorithm (RPA) is applied for fine-tuning the hyperparameters of the Scalable Random Forest Tree (SRFT), ensuring the model is well-adapted to large-scale NEVs data. The implementation of the RPA-SRFT algorithm in Python resulted in a significant improvement in fault prediction, with recall (97.60%), F1-score (98%), accuracy (98.50%), and precision (98%) outperforming conventional models in terms of both predictive performance and scalability. By leveraging advanced machine learning technique and big data analysis model, it can proactively predict faults and optimize maintenance schedules, resulting in reduced downtime and lower maintenance costs.