Battery Remaining Useful Life Prediction: Machine Learning-Driven Optimization of Electric Vehicle Maintenance and Energy Management

This project (GitHub open-source project Battery_RUL_Thesis) uses open-source data and machine learning technologies (regression, random forests, neural networks, etc.) to predict battery remaining useful life (RUL), aiming to optimize electric vehicle maintenance, energy storage system management, and sustainable energy management. Its core value lies in improving the accuracy of battery health status assessment through data-driven methods, supporting predictive maintenance and efficient resource utilization.

Battery RUL prediction is a core technology of battery management systems, which is crucial for electric vehicles (user experience, residual value assessment) and energy storage systems (charge-discharge strategy optimization). It faces three major challenges: 1. Complex aging mechanism (influenced by the interaction of multiple factors such as cycle count and temperature); 2. Difficulty in data acquisition (long full-life test cycle, relying on open-source datasets); 3. Prediction uncertainty (individual differences lead to different aging rates).

Explore multiple machine learning methods: 1. Regression analysis (linear/polynomial regression, providing an interpretable baseline); 2. Random forests (ensemble learning, capturing nonlinear relationships, providing feature importance); 3. Neural networks (e.g., LSTM for time series processing, automatic feature extraction). Feature engineering selects features such as capacity, internal resistance, voltage curve, temperature, and cycle count; preprocessing includes missing value handling, normalization, and time window construction.

1. Electric vehicle maintenance: Shift from regular maintenance to predictive maintenance, optimizing operational costs (e.g., high-usage scenarios of shared cars); 2. Energy storage system management: Balance module usage intensity, support echelon utilization assessment; 3. Sustainable energy management: Extend battery life to reduce waste, optimize recycling plans to improve resource recycling rates.

Limitations: Insufficient model generalization ability (poor performance across brands/usage conditions), high real-time requirements for online prediction. Future directions: Hybrid models combining physical and data-driven approaches, transfer learning to improve generalization, uncertainty quantification, and establishment of large-scale open datasets.

Battery RUL prediction is an important application of machine learning in the energy field, reflecting the ability of data-driven approaches to solve engineering problems. With the development of the electric vehicle and energy storage markets, this technology will support the construction of more intelligent and sustainable energy systems.