The source domain battery dataset used in this study was obtained from the lithium-ion battery dataset provided by the Center for Advanced Life Cycle Engineering Research (CALCE) at the University of Maryland, USA . Four battery charge and discharge experimental datasets, CS2_35, CS2_36, CS2_37, and CS2_38, were selected as the source domain At very low temperatures, the performance can be greatly reduced. But battery discharge curves are only one part of the story regarding battery performance. For example, the further the operating temperature of a Li-ion battery is from room temperature (both higher temperatures and lower temperatures), the more the cycle life degrades. Majeau-Bettez G, Hawkins TR, Strømman AH (2011) Life cycle environmental assessment of lithium-ion and nickel metal hydride batteries for plug-in hybrid and battery electric vehicles. Environ Sci Technol 45:4548–4554. The Li-ion battery has clear fundamental advantages and decades of research which have developed it into the high energy density, high cycle life, high efficiency battery that it is today. Yet research continues on new electrode materials to push the boundaries of cost, energy density, power density, cycle life, and safety. Advances and critical aspects in the life-cycle assessment of battery electric cars : 2017: Journal paper: Ioakimidis, C, S; Murillo-Marrodàn, A; Bagheri, A; Thomas, D; Genikomaskis, K: Life Cycle Assessment of a Lithium Iron Phosphate (LFP) Electric Vehicle Battery in Second Life Application Scenarios : 2019: Journal paper batteries Article Life Cycle Analysis of Lithium-Ion Batteries for Automotive Applications Qiang Dai *, Jarod C. Kelly , Linda Gaines and Michael Wang Systems Assessment Group, Energy Systems Division, Argonne National Laboratory, DuPage County, Argonne, IL 60439, USA; jckelly@anl.gov (J.C.K.); lgaines@anl.gov (L.G.); mqwang@anl.gov (M.W.) * Correspondence: qdai@anl.gov; Tel.: +1-630-252-8428 Many prior publications have attempted to early predict the lithium-ion battery cycle life. Summarizing these studies, it is not difficult to find that methods for early prediction of lithium-ion battery's cycle life can be categorized into two main types: model-based method and data-driven method [5]. Model-based methods rely on models that Results show that the proposed method significantly outperforms the others. For example, the novel data-driven method of early prediction of lithium-ion battery cycle life [3] was recently published on the journal of Nature Energy. Based on the same dataset used above, the constant-current (CC) discharge data of the first 100 cycles are wTCtw.