A Novel Prognostics Approach Using Shifting Kernel Particle Filter of Li-Ion Batteries Under State Changes

Lithium-ion (Li-ion) batteries are used in various applications as the rechargeable power sources. The batteries undergo capacity fade during the repeated charge-discharge cycles, which eventually leads to the end of life (EOL). For the purpose of timely replacement before reaching the EOL, reliable prediction of the remaining useful life (RUL) during the cycles is of great importance. However, there may exist unhealthy batteries exhibiting the change of state at some cycles from those of normal degradation, which leads to their EOL sooner than expected. In this article, we propose a novel prognostic method using the particle filter (PF) that is capable of detecting the point of state change and adapting its algorithm to the new battery degradation pattern. The performance of the proposed method is demonstrated by the case study of Li-ion battery degradation data, comparing with the original PF algorithm. As a result, the proposed method shows better performance in terms of anomaly detection of degradation and adaptability to the new degradation process, which leads to more accurate and reliable RUL prediction.