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

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.

Battery Energy Storage Capacity Estimation for Microgrids Using Digital Twin Concept

“…The Rainflow cycle counting method is employed in this study, as it has been extensively utilized in material fatigue analysis [48,49], as well as in battery cycle counting applications [50,51]. The Rainflow counting method is a well-established technique for monitoring the health of Li-ion batteries [52,53]. Shi et al [54] demonstrated the efficacy of the Rainflow algorithm in accurately identifying battery cycles in their study.…”

A storage degradation model of Li-ion batteries to integrate ageing effects in the optimal management and design of an isolated microgrid

“…A first very simple solution is to consider constraints that are known to limit battery ageing, but that do not constitute an ageing model per see -for example, limiting the battery SoC boundary values [12], the maximum power [17] or the number of cycles in a time period [17], [18]. Another approach is to concentrate on simplifying the classification of cycles: some authors consider discrete depth of discharge intervals and the notion of cycles to failure to estimate the battery life [19], [20], whereas other works propose simplified versions of the Rainflow algorithm [21], [22]. These approaches are often limited in the sense that the only battery stress factor considered is the depth of discharge.…”

Computer Sales Forecasting System Application Using Web-Based Single Moving Average Method

“…The main problem The store manager has not been able to predict the sales of wholesale and retail goods based on the past 3 years. so it will be difficult to make decisions on the production of new goods every year [1]. From the explanation above, it can be seen that the Head of the Computer Store has difficulty in predicting the sale of computer inventory production so that the store has difficulty making decisions.…”