An improved exponential model for predicting the remaining useful life of lithium-ion batteries

Prognostics and health management has become a subject of great interest to many electrical systems. However, the lithium-ion batteries are a core component of many machines and critical to system's functional capabilities. Remaining useful life prediction is central to the PHM of the lithium-ion batteries. The remaining useful life of lithium-ion batteries is defined as length of time from current time to the end of available life. An efficient method for the lithium-ion batteries monitoring would greatly improve the reliability of these machines and systems. For the lithium-ion batteries, the capacity induced by the charge-discharge operational cycle is suitable feature to represent battery degradation trend. The main challenges in battery remaining useful life prediction are to improve predicting accuracy and narrow the probability distribution function of the uncertainty. A novel data-driven approach for lithium-ion batteries remaining useful life using an improved exponential model by particle filter is proposed. To validate our proposed prognostic approach high prediction accuracy and small uncertainty, four case studies were conducted. We compared the remaining useful life prediction results associated with the original exponential model using the particle filter method. The experimental results show the following: 1) the improved exponential model needs fewer parameters than the original model; 2) the proposed prognostic method has stable and high prediction accuracy; 3) the proposed method has small uncertainty.