Lebesgue-Sampling-Based Diagnosis and Prognosis for Lithium-Ion Batteries

Traditional fault diagnosis and prognosis (FDP) approaches are based on periodic sampling, i.e., samples are taken and algorithms are executed both in a periodic manner. As the volume of sensor data and complexity of algorithms keep increasing, the bottleneck of FDP is mainly the limited computational resources, which is particularly true for distributed applications where FDP functions are deployed on microcontrollers and embedded systems with limited computation resources. This paper introduces the concept of Lebesgue sampling (LS) in FDP and proposes a LS-based FDP (LS-FDP) framework. In the proposed LS-FDP, a novel diagnostic philosophy of “execution only when necessary” is developed in computation cost reduction. For prognosis, different from traditional approaches in which the prognostic horizon is on the time axis, the proposed approach defines the prognostic horizon on the fault state axis. With a reduced prognostic horizon, the LS-FDP naturally benefits the uncertainty management. The goal of this paper is to create the fundamental knowledge for LS-FDP solutions that are cost efficient, capable for the deployment on systems with limited computation sources, and supportive to the trend of distributed FDP schemes in complex systems. The design and implementation of particle-filter-based LS-FDP are presented with experimental results on lithium-ion batteries to verify the effectiveness of the proposed approaches.