SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise - SUN Ning; WANG Shenghui; YU Tianjian; DAI Yi (机车电传动)
The state of health (SOH) of nickel-cadmium battery used on EMUs influences the operation safety of trains. Due to the complex working conditions of EMUs, the existing SOH monitoring methods are unable to realize good online monitoring. To study the change tendency of battery SOH and realize online prediction, this paper proposed an online SOH prediction method based on the charging time under constant voltage rise, with the assumption that there is no discharge when using charger to charge the battery. This method determined the optimal constant voltage rising range (i.e. the voltage difference between the start and end of charging) through a comprehensive correlation analysis of charging time under constant voltage rise and battery SOH based on moving voltage window, and then extracted the optimal charging time under constant voltage rise from the voltage range as the input for the long short-term memory (LSTM) model. The sparrow search algorithm (SSA) was used to optimize the LSTM parameters, establishing a battery SOH prediction model, and realizing the online prediction of battery SOH. The test results show that, compared with traditional LSTM and back propagation (BP) neural network, the battery SOH prediction model based on SSA-LSTM has higher prediction accuracy.