RESUMO
Accurately evaluating the health status of lithium-ion batteries (LIBs) is significant to enhance the safety, efficiency, and economy of LIBs deployment. However, the complex degradation processes inside the battery make it a thorny challenge. Data-driven methods are widely used to resolve the problem without exploring the complex aging mechanisms; however, random and incomplete charging-discharging processes in actual applications make the existing methods fail to work. Here, we develop three data-driven methods to estimate battery state of health (SOH) using a short random charging segment (RCS). Four types of commercial LIBs (75 cells), cycled under different temperatures and discharging rates, are employed to validate the methods. Trained on a nominal cycling condition, our models can achieve high-precision SOH estimation under other different conditions. We prove that an RCS with a 10mV voltage window can obtain an average error of less than 5%, and the error plunges as the voltage window increases.
RESUMO
In an ongoing effort to expand knowledge of the Chinese cobweb spider fauna (Theridiidae), the genus Meotipa Simon, 1894 is reviewed. Two new species are described, Meotipapseudopicturata sp. nov., Meotipastriata sp. nov., and five known species are redescribed: Meotipaargyrodiformis (Yaginuma, 1952), Meotipapulcherrima (Mello-Leitão, 1917), Meotipapicturata Simon, 1895, Meotipaspiniventris (O. Pickard-Cambridge, 1869), and Meotipavesiculosa Simon, 1895.
