RUL Prediction of Lithium-Ion Battery Based on Improved DGWO-ELM Method in a Random Discharge Rates Environment

Abstract

Lithium-ion batteries are widely applied in many fields. It is important for predicting battery life (RUL). It is randomly discharged that the lithium-ion battery under random conditions. The experiment of constant current discharge cannot simulate the discharge state under working conditions. Based on the data collection of the NASA dataset, the DGWO-ELM algorithm is proposed to predict lithium-ion battery. The DGWO-ELM is composed of Extreme Learning Machine (ELM), Grey Wolf Optimization (GWO), and Differential Evolution (DE) for the purpose of improving the accuracy of prediction. The algorithm uses GWO algorithm to optimize the weight and threshold of ELM and improves the three deficiencies in the GWO algorithm. The DGWO-ELM algorithm is proved preferably than ELM predictor improved by particle swarm optimization (PSO-ELM) and SVM predictor improved by Grey Wolf Optimization (GWO-SVM). The algorithm is verified by NASA's lithium-ion battery constant current discharge data, and then used to predict the RUL of the lithium-ion battery in a random discharge environment. The results show that the DGWO-ELM performs well on improving the accuracy of prediction.

Keywords

• Lithium-ion batteries
• RUL
• random discharge
• DGWO
• DE
• ELM