Lithium Thionyl Chloride Battery RP Estimation Mechanism Based on Wavelet Framework and Neural Networks

To address the issue of online monitoring of the remaining power (RP) in disposable lithium thionyl chloride batteries in smart meters, a voltage signal acquisition circuit has been added inside the meter. This setup utilizes a wavelet framework function for the analysis and processing of the sam-pled signal, obtaining two health factors that are highly correlated with the remaining battery power. The data is then transmitted to the main station through the meter's communication module. At the main station, a remaining battery power estimation algorithm has been designed using a backpropagation neural network (BPNN). Experimental results demonstrate that the battery RP estimation algorithm offers high precision, assisting management personnel in implementing on-demand replacement of meter clock batteries and effectively reducing the average number of clock battery replacements over the lifecycle of the meter.