Research on Lithium Battery SOH Prediction Methods Based on CNN-LSTM in the Context of Integrated Energy

With the rapid development of integrated energy system and energy storage technology, lithium-ion battery as the core energy storage element, the accurate prediction of its State Of Health (SOH) is very important to optimize the Battery Management System (BMS), improve the system operation efficiency and ensure safety. Aiming at the complex nonlinear and time-varying characteristics in the battery aging process, this paper proposes a SOH prediction method based on a hybrid architecture of Convolutional Neural Network (CNN) and Long Short-Term Memory network (LSTM). In this method, CNN is used to extract the local features of charging and discharging data such as voltage, current, and temperature, and LSTM is used to capture the longterm dependence of SOH evolution over time to achieve high-precision regression prediction. Experiments on the NASA public battery data set show that the CNN-LSTM model is significantly superior to single CNN, LSTM and Transformer models in terms of prediction accuracy, robustness and multi-condition adaptability. The experimental results verify the effectiveness of the proposed method in the energy storage scenario of comprehensive energy systems, which provides theoretical support and technical reference for battery health management, optimal scheduling and system reliability improvement.