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

Junhan Huang,Wenhua Xu,Shunli Wang,Carlos Fernandez,Weihao Shi

doi:10.3390/pr9050795

Junhan Huang, Wenhua Xu + Show 3 more

Open Access

https://doi.org/10.3390/pr9050795

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Abstract

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.

Highlights

Lithium-ion batteries are widely used in agriculture, communications, industry, and other fields because of their unique advantages, such as their small size, low cost, and long life
Considering that the ternary lithium-ion battery has strong nonlinear characteristics, but the content volume attenuation during the life cycle [34] is difficult to change suddenly, based on the cycle method and data-driven ideas, the improved Rainflow algorithm is used to calculate the state of health (SOH) and establish the autoregressive integrated moving average (ARIMA) prediction model, and uses the augmented Dickey–Fuller (ADF), KPSS, and Akaike information criterion (AIC) multiple inspection methods to determine the optimal state of the model, and compares the predicted value with the true value in order to get the prediction effect
According to the principle of the cycle method, the SOH attenuation curve of the lithium-ion battery is drawn based on the improved Rainflow counting method