리튬 이온 배터리의 안전 진단을 위한 실시간 내부저항 추정

초록

We proposed a real-time internal resistance (AC-IR) estimation model for monitoring lithium-ion battery safety. As battery usage increases, repeated charge/discharge cycles accelerate degradation, leading to safety risks such as overheating and instability. Battery Management System (BMS) typically monitors the battery’s health using indicators like State of Charge (SoC), State of Health (SoH), and internal resistance. While SoC and SoH can be estimated through BMS, internal resistance is difficult to measure in real time. This study highlighted the correlation between SoC, SoH, and internal resistance, developing a model that estimates AC-IR based on SoC, SoH, and sensor data by using machine learning and deep learning models. Our predicted results with machine learning and deep learning algorithms showed that AC-IR could be effectively estimated using inputs like temperature, voltage, current, and SoC. This approach offers valuable insights for maintaining safe and efficient battery operation, especially in large-scale systems like Energy Storage Systems (ESS) and Electric Vehicles (EVs).

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