Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery

Lithium-ion batteries may suffer an abnormal degradation defined by a significantly accelerated performance drop after a period of linear and low-rate degradation, resulting in severe danger to operational safety and reliability. Existing supervised data-driven prognostics for abnormal degradation rely heavily on adequate high-quality training samples, thus hindering their real-world utilization. Therefore, this paper develops an unsupervised dynamic prognostics framework that is dynamically executed as the cycle number increases to provide accurate and timely warnings for abnormal degradation. In the framework, an extended asymmetric quantum clustering (EAQC) can preliminarily identify the risky battery with the abnormal degradation trend. Its use of multi-dimensional feature degradation rates and potential function for clustering makes it outperform other density-based clustering methods. Then, for the identified battery, a time-varying double-layer autoregression (TVDLAR) can accurately predict its knee point of abnormal degradation. TVDLAR produces much earlier warning than other autoregression methods, which owes to its modeling ability of the time-varying correlation in battery historical degradation data. Through applying to two experimental datasets consisting of 174 lithium-ion batteries of different types under various working conditions, the framework is proven highly effective and shows significant superiority over some alternative approaches; all abnormal degradation can be timely warned before the corresponding knee points, and the average earlier warning cycles are 86 and 116, respectively. Compared with supervised data-driven methods, the proposed unsupervised dynamic prognostics framework has the advantages of low data requirement, low computational consumption, and good interpretability, indicating its substantial potential for application.