Data driven analysis of lithium-ion battery internal resistance towards reliable state of health prediction

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.

Automated summary

The paper uses a public dataset to characterize battery internal resistance behavior; develops battery health prediction models for different operating conditions based on internal resistance dynamics; and demonstrates that instantaneous voltage drops due to multiple pulse discharge loads can characterize battery heterogeneity.