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:	Angela Caliwag, Wansu Lim
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Battery Forecasting State-of-charge Optimization VARMA Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/34d2210a5f6a4612bdb164dc328b7514
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spelling	oai:doaj.org-article:34d2210a5f6a4612bdb164dc328b75142021-11-30T04:16:39ZOptimal least square vector autoregressive moving average for battery state of charge estimation and forecasting2405-959510.1016/j.icte.2021.03.008https://doaj.org/article/34d2210a5f6a4612bdb164dc328b75142021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405959521000412https://doaj.org/toc/2405-9595The 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%.Angela CaliwagWansu LimElsevierarticleBatteryForecastingState-of-chargeOptimizationVARMAInformation technologyT58.5-58.64ENICT Express, Vol 7, Iss 4, Pp 493-496 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Battery Forecasting State-of-charge Optimization VARMA Information technology T58.5-58.64
spellingShingle	Battery Forecasting State-of-charge Optimization VARMA Information technology T58.5-58.64 Angela Caliwag Wansu Lim Optimal least square vector autoregressive moving average for battery state of charge estimation and forecasting
description	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%.
format	article
author	Angela Caliwag Wansu Lim
author_facet	Angela Caliwag Wansu Lim
author_sort	Angela Caliwag
title	Optimal least square vector autoregressive moving average for battery state of charge estimation and forecasting
title_short	Optimal least square vector autoregressive moving average for battery state of charge estimation and forecasting
title_full	Optimal least square vector autoregressive moving average for battery state of charge estimation and forecasting
title_fullStr	Optimal least square vector autoregressive moving average for battery state of charge estimation and forecasting
title_full_unstemmed	Optimal least square vector autoregressive moving average for battery state of charge estimation and forecasting
title_sort	optimal least square vector autoregressive moving average for battery state of charge estimation and forecasting
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/34d2210a5f6a4612bdb164dc328b7514
work_keys_str_mv	AT angelacaliwag optimalleastsquarevectorautoregressivemovingaverageforbatterystateofchargeestimationandforecasting AT wansulim optimalleastsquarevectorautoregressivemovingaverageforbatterystateofchargeestimationandforecasting
_version_	1718406795905466368

Optimal least square vector autoregressive moving average for battery state of charge estimation and forecasting

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