A Novel Autoregressive Rainflow-Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries

The accurate estimation and prediction of lithium-ion battery state of health are one of the important core technologies of the battery management system, and are also the key to extending battery life. However, it is difficult to track state of health in real-time to predict and improve accuracy. This article selects the ternary lithium-ion battery as the research object. Based on the cycle method and data-driven idea, the improved rain flow counting algorithm is combined with the autoregressive integrated moving average model prediction model to propose a new prediction for the battery state of health method. Experiments are carried out with dynamic stress test and cycle conditions, and a confidence interval method is proposed to fit the error range. Compared with the actual value, the method proposed in this paper has a maximum error of 5.3160% under dynamic stress test conditions, a maximum error of 5.4517% when the state of charge of the cyclic conditions is used as a sample, and a maximum error of 0.7949% when the state of health under cyclic conditions is used as a sample.

Automated summary

This article proposes a new method for predicting the state of health of lithium-ion batteries based on the cycle method and data-driven idea, combining the improved rain flow counting algorithm with the autoregressive integrated moving average model prediction model. Experimental results show that the method has a maximum error of 5.3160%, 5.4517%, and 0.7949% under different conditions.