Battery phase space warping: A novel method for lithium-ion battery degradation tracking under variable discharges.

Effective reliability management of lithium-ion batteries (LIBs) depends on accurately assessing the State of Health (SOH). Monitoring SOH requires characterizing health indicators (HIs) derived from LIB signals. However, the extraction of HIs using signal processing techniques is susceptible to interference from time-varying operating conditions. To address this issue, the Battery Phase Space Warping (BPSW) algorithm is proposed. This algorithm aims to distinguish slow aging from fast-varying signals by reconstructing discharge voltage signals into a high-dimensional phase space (PS), enabling a qualitative analysis of LIB dynamics. Slow battery aging manifests as trajectory warping in the reconstructed PS, and the BPSW algorithm applies a local polynomial model to describe these warped trajectories and predict reference states. Furthermore, the BPSW algorithm separates slow-varying HIs from fast-varying signals by extracting the warpage for different PS states, enabling accurate tracking of the hidden aging of LIBs. The effectiveness of the BPSW algorithm is validated through degradation experiments on LIBs under constant current and square wave loads, demonstrating its superior capability to track battery aging. Additionally, the BPSW algorithm proves robust in monitoring battery degradation under variable discharge conditions, significantly improving the monitoring of LIBs. • BPSW monitors battery aging by reconstructing voltage signals into phase space. • Trajectory warping in phase space reflects changes in battery parameters. • BPSW uses local polynomial regression for reference trajectories of health. • BPSW separates fast and slow variables, minimizing interference from discharge. • BPSW aids in battery health management and predicting remaining useful life. [ABSTRACT FROM AUTHOR]