Prediction of SOC and SOH of EMU Battery Based on DAUKF

In order to improve the prediction accuracy of state of charge (SOC) and state of health (SOH) of Ni-Cd alkaline battery in Electric Multiple Units (EMU) auxiliary power system, a Dual Adaptive Unscented Kalman Filter (DAUKF) algorithm was proposed by improving the unscented Kalman algorithm. Two algorithm filters, AUKF1 and AUKF2, are established. Based on the second-order battery model, the SOC and SOH of the battery are calculated simultaneously. The simulation results show that DAUKF can get accurate SOC and SOH, the prediction accuracy is less than 2%, which verifies the feasibility and accuracy of the algorithm. It can provide real-time prediction data of Ni-Cd alkaline battery of EMU, which is of great significance to the safe operation of EMU.