Multi-Parameter Optimization Method for Remaining Useful Life Prediction of Lithium-Ion Batteries

• Published in: IEEE access 2020, Vol.8, p.142557-142570
• Main Authors: Long, Bing, Gao, Xiaoyu, Li, Pengcheng, Liu, Zhen
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Data models; Degradation; Entropy (Information theory); Filtering; improved particle swarm optimization(PSO); Life prediction; Lithium; Lithium-ion batteries; Low pass filters; Low temperature; moving average filter(MAF); Parameters; Particle swarm optimization; Predictive models; Rechargeable batteries; Regeneration; remaining useful life (RUL); Resistance; System effectiveness; technique for order Preference by similarity to ideal solution(TOPSIS) method; Useful life
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3011625

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries can ensure the normal and effective operation of power systems using lithium-ion batteries. However, how to select battery prediction parameters through scientific methods and how to accurately predict battery RUL values under high and low temperature conditions are still a huge challenge. Thus according to the technique for order preference by similarity to ideal solution (TOPSIS) based on information entropy, improved particle swarm optimization (PSO) and moving average filter(MAF), a novel data-driven method for predict lithium-ion batteries' RUL is proposed. The TOPSIS method based on information entropy is proposed to select the best degradation parameters; a sliding average low-pass filter is used to solve the capacity regeneration and noise problem of the battery experimental data; the improved PSO algorithm is presented to predict the battery RUL accurately. Based on the batteries experimental data from NASA and University of Maryland, we have done many simulation experiments on parameters selection and RUL accuracy comparisons among several data-driven methods. The experimental results shows:(1) compared with the other prediction methods without degradation parameters selection, the proposed method with TOPSIS and MAF filtering is more accurate;(2) our proposed algorithm has higher prediction accuracy and use less training data than other data-driven algorithms;(3) this method has high prediction accuracy under both the high and low temperature conditions.