RESUMO
Investigating the correlation between aging mechanisms and state of charge (SOC) can optimize cycling conditions and prolong the life cycle of lithium-ion batteries (LIBs). A long-term cycle between a certain SOC range is usually employed to study this correlation. However, this method necessitates a lengthy period, running from months to years, prolonging the research duration significantly. The aging mechanisms obtained through this method are a result of the coupling of various SOC levels; the aging mechanisms at a specific SOC level are not accurately decoupled and analyzable. The proposed Zero-sum pulse method, using symmetrical pulses with small SOC amplitude variations on SOC, can explore aging mechanisms of LIBs at a specific SOC level and reduce the time to less than a week, which significantly expedite the research process. The aging mechanisms at 30%, 50%, 70%, and 90% SOC levels are explored to verify the accuracy and timeliness of this method.
RESUMO
The mass transfer in lithium-ion batteries is a low-frequency dynamic that affects their voltage and performance. To find an effective way to describe the mass transfer in lithium-ion batteries, a simplified electrochemical lithium-ion battery model based on ensemble learning is proposed. The proposed model simplifies lithium-ion transfer in electrode particles with ensemble learning which ensembles discrete-time realization algorithm (DRA), fractional-order Padé approximation model (FOM), and three parameters (TPM) parabolic. The lithium-ion transfer in the electrolyte is simplified by the first-order inertial element (FIE). The results show that the proposed model achieves not only accurate lithium-ion concentration prediction in solid and electrolyte phase but also precise voltage prediction with low computational complexity.