Engineering insight

Data-driven prediction of battery cycle life before capacity degradation카테고리: Battery-AIDOI: 10.1038/s41560-019-0356-8원문: https://www.nature.com/articles/s41560-019-0356-8공개 PDF: https://web.mit.edu/braatzgroup/Severson_NatureEnergy_2019.pdfDense 5-line summary이 논문은 LFP/graphite 상용 원통형 셀 124개를 72개의 고속충전 조건에서 시험해, 초기 100사이클의 방전 전압 곡선만으로 전체 수명(80% 용량 도달 시점)을 예측할 수 있음을 보였다.핵심은 단순 용량 감소량이 아니라 서로 다른 ..

Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learningNature Communications (2020) · Yunwei Zhang, Qiaochu Tang, Yao Zhang, Jiabin Wang, Ulrich Stimming, Alpha A. Lee원문: https://www.nature.com/articles/s41467-020-15235-7DOI: https://doi.org/10.1038/s41467-020-15235-7 한 줄 핵심이 논문은 배터리 EIS 전체 스펙트럼을 그대로 machine learning에 넣어 capacity와 RUL을 추정하면서, ..

Multi-modal framework for battery state of health evaluation using open-source electric vehicle dataNature Communications (2025) · Hongao Liu et al. · DOI: 10.1038/s41467-025-56485-7원문: https://www.nature.com/articles/s41467-025-56485-7 · PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC11779878/한 줄 평가이 논문은 실차 EV SOH 추정에서 “모델”보다 “입력 표현(representation)”이 더 중요할 수 있다는 점을 설득력 있게 보여준 연구입니다. 가장 큰 기여는 딥러닝..

[논문 전문] : https://www.nature.com/articles/s41560-024-01675-8[출처] Geslin, A., Xu, L., Ganapathi, D. et al. Dynamic cycling enhances battery lifetime. Nat Energy 10, 172–180 (2025). https://doi.org/10.1038/s41560-024-01675-8 ※ The picture and content of this article are from the original paper.All picture and figures used in this article are sourced from publicily available on the internet. [논문 요..

[논문 전문] : https://www.mdpi.com/2079-9292/12/3/657[출처] Wong, K.L.; Chou, K.S.; Tse, R.; Tang, S.-K.; Pau, G. A Novel Fusion Approach Consisting of GAN and State-of Charge Estimator for Synthetic Battery Operation Data Generation. Electronics 2023, 12, 657. https://doi.org/10.3390/electronics12030657 ※ The picture and content of this article are from the original paper.All picture and figures use..