Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries

This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application to the case of state-of-charge prediction in lithium-ion batteries. The proposed risk measure not only incorporates the risk of battery failure but also is a measure for the confidence on the prognosis algorithm itself. In addition, a novel simplified PF-based prognostic method is proposed to estimate the battery discharge time, while providing a computationally inexpensive solution. Computing times for both the novel prognosis routine and the associated risk measure are fast enough to allow their implementation in real-time applications, such as decision-making systems or path-planning algorithms.