The project analyzes battery cycling data to predict degradation patterns and performance metrics using both deep learning (LSTM) and traditional machine learning (XGBoost) approaches. The implementation enables accurate estimation of battery health, which is crucial for battery management systems in various applications.

BattSense is a machine learning project focused on predicting the State of Health (SOH) of lithium-ion batteries using operational parameters such as voltage, current, temperature, and capacity. The model enables accurate, data-driven diagnostics for battery performance monitoring in electric vehicles and portable devices.

苏州科技大学《人工智能开发实训 II》（2026.05）。实践二：MSTAR SAR半监督学习（FixMatch完整网格搜索+报告分节素材）；实践三：锂离子电池SOH预测（三种划分+20次独立实验+消融）；ResNet32 GA+PSO通道宽度搜索（H100/L40S）。附完整工具链、实验结果与答辩素材。