Analysis of State-of-Charge Assessment of Automotive Lithium Batteries Based on a Two-Layer ELM Model
The state of charge of lithium battery directly affects the economic benefits in the field of new energy vehicles,and a two-layer integrated extreme learning machine(ELM)model is used to evaluate the state of charge(SOC)and state of health(SOH)of lithium battery.The iterative calculation of the state of charge is realized by analyzing the battery health characteristics.The results show that the deviation of the estimated parameters of SOC and aging SOC obtained from the two-layer integrated ELM model by estimating the health characteristics of equal voltage intervals is no more than 1.4%,and the type achieves high precision estimation effect and shows excellent robustness.Compared with other algorithms,the integrated ELM prediction accuracy,training and testing accuracy are the highest,and the ELM model is integrated in a shorter time.This research helps to improve the efficient operation and reduce the cost in the new energy vehicle industry.
lithium batterystate of chargehealth characteristicsintegrated extreme learning machine