Engineering insight

Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectoryDigital Discovery (2023) · Laura Rieger, Erik Frank, Oleksandr V. Wodyński, Alpha A. Lee, Tejs Vegge, Anja B. BizerayDOI: https://doi.org/10.1039/d2dd00067aHTML 본문: https://pubs.rsc.org/en/content/articlehtml/2023/dd/d2dd00067a Uncertainty-aware and explainable machine learning for early pred..

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.sciencedirect.com/science/article/abs/pii/S2352152X23024088[출처]  Guangfeng Wang, Naxin Cui, Changlong Li, Zhongrui Cui, Haitao Yuan, A state-of-health estimation method based on incremental capacity analysis for Li-ion battery considering charging/discharging rate, Journal of Energy Storage, Volume 73, Part B, 2023, 109010, ISSN 2352-152X, https://doi.org/10.1016/j.est.2023..

논문 전문 : https://pubs.acs.org/doi/10.1021/acsenergylett.3c00695[출처] ACS Energy Lett. 2023, 8, 7, 2946–2953 ※ 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 요약]Lifetime prediction of Lithium Ion batteries by using the heterogeneity of graphite anodes 본 논문은 Open Access가 아니..

논문 전문 : https://www.sciencedirect.com/science/article/pii/S0378775324003926?dgcid=rss_sd_all[출처] Davide Cavaliere, Atsunori Ikezawa, Takeyoshi Okajima, Hajime Arai,Diagnosis of Liion battery degradation based on resistive and diffusion-related transient voltage changes at early stage of discharge,Journal of Power Sources,Volume 603,2024,234441,ISSN 0378-7753,https://doi.org/10.1016/j.jpowsour.20..