Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction

iScience

Shufang Yuan¹  Jianchao Zeng²  Xiaoqiong Pang³  Jianfang Jia³  Yuanhao Shi³  Jie Wen³ 

[1] Corresponding author;School of Data Science and Technology, North University of China, Taiyuan 030051, China;School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China;

关键词: Machine learning;    Energy management;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods.

【 授权许可】

Unknown