Power prediction method of lithium-ion battery for unmanned vehicles based on combined constraint intelligence algorithm

Abstract

For the inaccurate data predicting problem of the actual attainable maximum charge and discharge power of the lithium-ion battery in practice for unmanned vehicles in the process of running, a combined constraint intelligence algorithm is proposed based on the result of SOC, the battery voltage and the battery current. In view of the hysteresis characteristics of lithium-ion battery presented in the charge and discharge process, a second-order RC hysteresis model was proposed in this paper. In addition, we used the cubature Kalman filter algorithm to estimate the state of charge (SOC) of the battery based on this model, which reduced both the model error and algorithmic error in the SOC estimation significantly. It is also of great importance to accurately predict the state of power (SOP). Then the result of SOC, the battery voltage and the battery current were settled as constraints to predict the actual attainable maximum charge and discharge power of the lithium-ion battery in practice. Compared with the pulse discharge/charge test method and he hybrid pulse power characterization (HPPC) method, the combined constraint intelligence algorithm proposed in this paper is of high accuracy in the process of predicting the battery power.