Abstract：Objective To research a detection algorithm for the health status of batteries in large medical monitoring equipment, aimed
at detecting the health status of batteries and addressing issues such as time-varying effects and fault diversity caused by temperature
changes, charging and discharging cycles during use. Methods The voltage variation of the battery during charging and discharging
was analyzed, and three health factors such as constant voltage drop discharge time, battery internal resistance and constant interval
discharge time series were extracted. It was trained into a nonlinear regression model based on nonlinear autoregressive with
exogenous inputs model neural network to estimate the battery capacity of large medical monitoring equipment. The backpropagation
neural network was improved by particle swarm optimization algorithm to detect the state of health of the battery. Results The
experimental results showed that the detection error of this method was small; The correlation between the three health factors and
the estimated battery capacity of large medical monitoring equipment was higher than 0.95, and the estimated battery capacity was
accurate. Conclusion Through this method, battery problems can be detected in time, and measures can be taken in advance to
reduce equipment downtime caused by battery failures and reduce the risk of medical errors.
邱筱岷，王志禹，王小花. 大型医疗监护设备电池健康状态检测
算法研究[J]. 中国医疗设备, 2024, 39(3): 46-52.
QIU Xiaomin, WANG Zhiyu, WANG Xiaohua. Research on Battery Health Status Detection Algorithm for Large Medical Monitoring Equipment. China Medical Devices, 2024, 39(3): 46-52.
