Mohammad A. Hoque1, Petteri Nurmi1, Arjun Kumar2, Samu Varjonen1, Junehwa Song2, Sasu Tarkoma3 and Michael Pecht1
1Nodes Lab, Deaprtment of Computer Science, Exactum, P.O. Box 68 (Pietari Kalmin katu 5) 00014, University of Helsinki, Finland
2School of Computing Korea Advanced Institute of Science and Technology (KAIST) 335 Gwahangno, Yuseong-gu, Daejeon 34141, Republic of Korea
3CALCE Center for Advanced Life Cycle Engineering, 1103 Engineering Lab Building, University of Maryland, College Park, MD 20742, USA
Accurately predicting the lifetime of lithium-ion batteries in the early stage is critical for faster battery production, tuning the production line, and predictive maintenance of energy storage systems and battery-powered devices. Diverse usage patterns, variability in the devices housing the batteries, and diversity in their operating conditions pose significant challenges for this task. The contributions of this paper are three-fold. First, a public dataset is used to characterize the behavior of battery internal resistance. Internal resistance has non-linear dynamics as the battery ages, making it an excellent candidate for reliable battery health prediction during early cycles. Second, using these findings, battery health prediction models for different operating conditions are developed. The best models are more than 95% accurate in predicting battery health using the internal resistance dynamics of 100 cycles at room temperature. Thirdly, instantaneous voltage drops due to multiple pulse discharge loads are shown to be capable of characterizing battery heterogeneity in as few as five cycles. The results pave the way toward improved battery models and better efficiency within the production and use of lithium-ion batteries.