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

Jianfang Jia; Shufang Yuan; Yuanhao Shi; Jie Wen; Xiaoqiong Pang; Jianchao Zeng

doi:10.1016/j.isci.2022.103988

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

iScience. 2022 Feb 26;25(4):103988. doi: 10.1016/j.isci.2022.103988. eCollection 2022 Apr 15.

Authors

Jianfang Jia¹, Shufang Yuan¹, Yuanhao Shi¹, Jie Wen¹, Xiaoqiong Pang², Jianchao Zeng²

Affiliations

¹ School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China.
² School of Data Science and Technology, North University of China, Taiyuan 030051, China.

Abstract

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.

Keywords: Energy management; Machine learning.