Remaining Life Prediction of Lithium Batteries Based on Bald Eagle Search Algorithm and Optimized ELM
A method for predicting the remaining life of batteries based on the fusion of multiple data-driven methods was proposed.Firstly,the relevant characteristic parameters of battery life were ana-lyzed,and the parameters with higher correlation were selected as indirect health factors.Then,a pre-diction model combining bald eagle search algorithm and optimized extreme learning machine(ELM)was constructed.Finally,the feasibility and accuracy of the prediction model were validated using the NASA battery dataset.The experimental results show that compared to that of the prediction methods based on neural networks and ELM,the root-mean-square errors of the prediction model in this paper are all within 2%,and the prediction accuracy is more reliable and precise.
bald eagle search algorithmextreme learning machinelife predictionlithium battery
万方数据
维普
