Intelligent Monitoring Technology Based on Lithium-Ion Battery Energy Storage System

In response to safety issues such as overcharging, temperature anomalies, and capacity degradation in the operation of lithium-ion battery energy storage systems, this article introduces advanced sensor technologies such as temperature sensors, voltage sensors, and internal resistance detection sensors, combined with big data analysis and an artificial intelligence prediction model based on DNN (Deep Neural Networks) deep neural networks, to achieve comprehensive monitoring and fault prediction of battery status. Firstly, the article utilizes multi-dimensional sensors to collect real-time core data such as battery temperature, voltage, and capacity, ensuring the comprehensiveness of monitoring information; then, the article uses big data analysis methods to process and analyze the collected data, extracting key features of parameters such as voltage, internal resistance, temperature, number of charge and discharge cycles, capacity decay rate, and current of the battery’s health status; finally, the article introduces an algorithm model based on DNN deep neural network, which extracts features and recognizes patterns from historical data through a multi-layer neural structure, thereby achieving accurate prediction of battery operating status and fault warning. The test results show that from the first to the ninth cycle, the actual capacity of the battery is 3.98 Ah in the first cycle and decreases to 3.82 Ah in the ninth cycle, showing a capacity decay trend. The system predicts future capacity through the DNN model, with a first prediction of 3.99 Ah and a ninth prediction of 3.83 Ah. The application of intelligent monitoring technology in lithium-ion battery energy storage systems in this article has significant advantages, not only enhancing the safety of the system, but also improving the utilization efficiency and operational stability of the battery.