基于改进粒子滤波算法的医疗锂电池PHM系统设计

Electromedical Lithium Battery PHM System Based on Improved Particle Filter Algorithm

摘要:

针对维持生命的医疗电子设备的锂离子电池维修问题,设计了一套故障预测与健康管理系统(Prognostics and Health Management-PHM),提出了PHM系统的实现框架。通过搭建一套电池控制应力水平实验平台并将故障注入锂离子电池中,来进行数据采集。建立基于阿列纽斯模型(Arrhenius Model)的医疗电子设备的锂离子电池模型,通过无迹粒子滤波(Unscented Particle Filter-UPF)算法和粒子滤波(Particle Filter-PF)算法计算出实时故障的概率并给出剩余寿命预测以及健康管理维护方法。通过Matlab对比UPF和PF的预测剩余寿命的仿真结果与实验所测数据的吻合度,选出UPF算法为最优算法并及时诊断故障,为后续维护提供建议。

Abstract:

Aiming at the maintenance of Li-ion battery for life-supporting medical electronic equipment, a set of Prognostics and Health Management (PHM) was designed and the implementation framework of the PHM system was proposed. A data-acquisition process was conducted by constructing a battery-controlled stress level test platform and injecting faults into a lithium ion battery. A lithium-ion battery model based on the Arrhenius model of a medical electronic device was established. The probability of real-time failures was calculated by using the UPF and PF algorithms, and the remaining life prediction and health management maintenance methods was given. Comparing the simulation results of the predicted remaining life of Unscented Particle Filter and Particle Filter with the experimentally measured data through Matlab, the UPF algorithm was selected as the optimal algorithm and the faults were diagnosed in time to provide suggestions for subsequent maintenance.