Research on Battery Health Status Detection Algorithm for Large Medical Monitoring Equipment
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.
large medical monitoring equipmentbattery health statushealth factorNARX neural network
国家科技期刊平台
NSTL
万方数据
