A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries
Abstract
:1. Introduction
2. Mathematical Analysis
2.1. Forecasting Process
2.2. Improved Rainflow Algorithm
2.3. Second-Order Stationarity Test
2.4. ARIMA Model Establishment
2.5. Residual Test
3. Experimental Analysis
3.1. Construction of the Experimental Platform
3.2. Rainflow Algorithm to Calculate SOH
3.3. ADF and KPSS Jointly Verify the Differential Sequence
3.4. Complex Condition Analysis
3.5. Residual Error Test
3.6. Predictive Verification
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Huang, J.; Wang, S.; Xu, W.; Shi, W.; Fernandez, C. A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries. Processes 2021, 9, 795. https://doi.org/10.3390/pr9050795
Huang J, Wang S, Xu W, Shi W, Fernandez C. A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries. Processes. 2021; 9(5):795. https://doi.org/10.3390/pr9050795
Chicago/Turabian StyleHuang, Junhan, Shunli Wang, Wenhua Xu, Weihao Shi, and Carlos Fernandez. 2021. "A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries" Processes 9, no. 5: 795. https://doi.org/10.3390/pr9050795