A hybrid system for Lithium-ion battery State-of-Charge univariate forecasting

Medina, Marie Chantelle Cruz; Oliveira, João Fausto Lorenzato de

doi:10.1109/ictai52525.2021.00158

Cited by 3 publications

(2 citation statements)

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“…In the specific application of SoC forecasting, some models also follow a similar idea, as is the case of Khalid [9], combining a Minimized Akaike Information Criterion tuned ARIMA, and a unified Multilayer Perceptron (MLP) and then with Nonlinear Autoregressive Neural Network with external input (NARX). Cruz and Oliveira [16] with a weighted average combination of an ARIMA+SVR and a LSTM using R2 as a base metric to compute both static weights. In this model, current and voltage time series are first predicted and then coulomb counting technique is used to compute SoC values through time by integration.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, other architectures have been proposed to improve prediction stability, reduce variance, and find the best model combinations. For example, Cruz and Oliveira [16] proposed a model with a weighted average combination of an ARIMA+SVR and a LSTM using R2 as a base metric to compute static weights to predict current and voltage time series. Models like this have been shown to perform adequately in SoC prediction.…”

Section: Introductionmentioning

confidence: 99%

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A selective hybrid system for state-of-charge forecasting of lithium–ion batteries

Medina

Oliveira

2023

J Supercomput

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As research in Lithium-ion batteries field has extended, the need for better management systems also increases. An important part of them is the proper estimation of battery status over time with indirect metrics such as State-of-Charge (SoC). In the machine learning environment, different simple techniques have been tested showing good performance and being surpassed by hybrid systems. In this study, a static selection model is proposed to choose the best non-linear predictor to work with and ARIMA model and combination function for a specific database considering how the perform in a validation set. This architecture allows to consider linear and non-linear components of the time series using residual forecasting and a selection step to reduce the chances of choosing the wrong combination in each specific database. SoC values for five different databases where forecasted, allowing to compare the results of six models relevant in literature to the proposed one. The results showed a superior performance for the proposed model in four out of the five databases, with gains of 5.27%, 13.51%, 56.67%, and 38.71%. There was only one database where the proposed model scored in second place. To determine whether the obtained results were statistically significant enough to make a conclusion, a Nemenyi test was conducted using MSE and MAE values to rank the performance of all models in all databases. The critical distance and rank achieved by the proposed model allowed to conclude that this, in fact, delivered the best performance amongst all models tested.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A selective hybrid system for state-of-charge forecasting of lithium–ion batteries

Medina

Oliveira

2023

J Supercomput

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A Hybrid System Based on Dynamic Selection for Wind Speed Forecasting

da Silva,

Lorenzato de Oliveira

2023

2023 Workshop on Communication Networks and Power Systems (WCNPS)

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A selective hybrid system for State-of-Charge forecasting of Lithium-ion batteries

Medina

Oliveira

2022

Preprint

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As research in Lithium-ion batteries field has extended, the need for better management systems also increases. An important part of them is the proper estimation of battery status over time with indirect metrics such as State-of-Charge (SoC). In the machine learning environment, different simple techniques have been tested showing good performance and being surpassed by hybrid systems.In this study, a static selection model is proposed to choose the best non-linear predictor to work with and ARIMA model and combination function for a specific database considering how the perform in a validation set. This architecture allows to consider linear and non-linear components of the time series using residual forecasting and a selection step to reduce the chances of choosing the wrong combination in each specific database.SoC values for five different databases where forecasted, allowing to compare the results of six models relevant in literature to the proposed one.The results showed a superior performance for the proposed model in four out of the five databases, with gains of 5.27%, 13.51%, 56.67%, and 38.71%. There was only one database where the proposed model scored in second place. To determine whether the obtained results were statistically significant enough to make a conclusion, a Nemenyi test was conducted using MSE and MAE values to rank the performance of all models in all databases. The critical distance and rank achieved by the proposed model allowed to conclude that this, in fact, delivered the best performance amongst all models tested.

show abstract

A hybrid system for Lithium-ion battery State-of-Charge univariate forecasting

Cited by 3 publications

References 24 publications

A selective hybrid system for state-of-charge forecasting of lithium–ion batteries

A selective hybrid system for state-of-charge forecasting of lithium–ion batteries

A Hybrid System Based on Dynamic Selection for Wind Speed Forecasting

A selective hybrid system for State-of-Charge forecasting of Lithium-ion batteries

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