Research on Battery Health Status Monitoring of New Energy Vehicles Based on Machine Learning

This paper proposes a battery health status monitoring method based on random forest algorithm, which uses machine learning technology to monitor and predict the health status of new energy vehicle batteries in real time. First, the battery performance data is comprehensively collected and preprocessed, including the extraction of key feature variables such as voltage, temperature, internal resistance, and number of charge and discharge cycles to ensure the integrity and quality of the data. These variables can reflect the aging degree and performance degradation of the battery to a certain extent, so they are normalized and denoised in data preprocessing to improve the learning effect of the model. Subsequently, this paper designs a machine learning model based on random forest, which learns and analyzes the feature data by building an integrated model of multiple decision trees to identify and predict the health status of the battery. In the simulation experiment part, this paper conducts a detailed evaluation of the prediction accuracy of the random forest model under different operating conditions. The experimental data comes from multiple operating scenarios, including complex environments such as high temperature, low temperature, and high load. The results show that under these conditions, the random forest model still maintains a high prediction accuracy of more than 95%. This method was compared with other machine learning algorithms. The results showed that random forests showed stronger robustness and adaptability when dealing with high-dimensional features and large amounts of data.