Optimal least square vector autoregressive moving average for battery state of charge estimation and forecasting
The error in state of charge estimation using the combined models is usually attributable to the statistical model. In this study, a least square algorithm is utilized to optimize and increase the state of charge estimation accuracy. Specifically, the vector autoregressive moving average statistical...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/34d2210a5f6a4612bdb164dc328b7514 |
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| Sumario: | The error in state of charge estimation using the combined models is usually attributable to the statistical model. In this study, a least square algorithm is utilized to optimize and increase the state of charge estimation accuracy. Specifically, the vector autoregressive moving average statistical model is optimized using the least square algorithm. The results presented in this paper show that the proposed method is effective in eliminating the estimation and measurement noise using the conventional statistical method and in optimizing the SoC estimation and forecasting. The optimization of the statistical model increases the SoC estimation and forecasting accuracy by 59.11%. |
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