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DBpia AI
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
학술저널
DBpia Top 10%
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
초록·키워드
We explored real-time internal resistance (rt-IR) and its trends as a critical diagnostic parameter for monitoring the safety of lithium-ion batteries. With growing demand for battery devices, repeated charge and discharge cycles accelerate performance degradation, leading to potential safety concerns. By monitoring rt-IR, this research aims to detect early signs of battery degradation.
The rt-IR was evaluated by testing several time intervals using current and voltage data under constant current state-of-charge (CC-SoC), obtained from the Oxford battery degradation dataset. Our results showed that the rt-IR values were relatively higher at lower SoC level, particularly below 30% of CC-SoC, indicating elevated safety risks. Based on these results, we proposed that rt-IR and its trends can be an important indicator for diagnosing lithium-ion battery safety. This real-time monitoring approach with rt-IR provides valuable insights into maintaining safety and efficient battery operation in energy storage systems (ESS).
The rt-IR was evaluated by testing several time intervals using current and voltage data under constant current state-of-charge (CC-SoC), obtained from the Oxford battery degradation dataset. Our results showed that the rt-IR values were relatively higher at lower SoC level, particularly below 30% of CC-SoC, indicating elevated safety risks. Based on these results, we proposed that rt-IR and its trends can be an important indicator for diagnosing lithium-ion battery safety. This real-time monitoring approach with rt-IR provides valuable insights into maintaining safety and efficient battery operation in energy storage systems (ESS).
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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