Pang, HY,
Zhang, WH,
Liang, XW,
Zhang, ZQ,
Chen, XL,
Zhao, LY,
Liu, K,
Galiullin, D,
Yang, K,
Chen, XZ,
Hu, JK (2021) established and
validated internally and externally.
Results
Based on multivariate analysis in the training
prediction for lithium-ion batteries. The
validity of the proposed method is verified by several evaluation
Solnyshkina, O. A.,
Fatkullina, N. B.,
Bulatova, A. Z.,
Kireev, V. N.,
Bilyalov, A. R.,
Akhatov, I. S.,
Pavlov, V. N. (2023) for several temperature regimes.
The
validation of the realized model is confirmed by comparing the numerical
Qi, Deyu,
Zhu, Zijiang,
Yao, Fengmin,
Song, Wanqing,
Kudreyko, Aleksey,
Cattani, Piercarlo,
Villecco, Francesco (2024) -tailed property of fLsm to provide support for the model. The proposed method is
validated with the C