Unified Univariate-Neural Network Models for Lithium-Ion Battery State-of-Charge Forecasting Using Minimized Akaike Information Criterion Algorithm

Khalid, Asadullah; Sarwat, Arif I.

doi:10.1109/access.2021.3061478

Cited by 34 publications

(19 citation statements)

References 47 publications

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“…This method needs three components: the trend, level, and seasonality. This method can adapt to trends and patterns changes, but it is limited to the seasonality component [24]. However, the Holt-Winters regression model employs the previous direct set of values or all historical values in the training process; therefore, training data may include data patterns that are irrelevant to the current situation, reducing the forecasting accuracy.…”

Section: Previous Studiesmentioning

confidence: 99%

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Sawalha

Al‐Naymat

2023

Preprint

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In business and industry, forecasting the future values of a time series is an age-old and widely utilized data analysis strategy. IoT temporal data, used in various applications, is one of the most well-known examples of such a time series. The main problem is to forecast a future window of the data using the provided IoT temporal data. Many forecasting models that deal with such data were proposed, such as Rolling Window, SVR-RBF, ARIMA, etc. However, in the training process, these models use all available data or a previous direct window of data. As a result, the training data may contain irrelevant data patterns to the current situation, lowering the total forecasting accuracy. This paper proposes a new adaptive forecasting model called AHW-SWA by creating two models based on the Exponential Smoothing method. The first model is trained using the previous direct window of data. The second model uses an extracted data window from the far historical data with a pattern most similar to the current situation. Then an adaptive process is used to balance the predicted values from both models to achieve higher accuracy. AHW-SWA model reduces the overall forecasting error by comparing the extracted and the latest data patterns. Our model outperforms well-known models, including Rolling Window, SVR-RBF, and ARIMA.

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Section: Previous Studiesmentioning

confidence: 99%

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Sawalha

Al‐Naymat

2023

Preprint

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“…To obtain the best prediction model, the Akaike information criterion (AIC) [26][27][28] can be used to determine the parameters p and q of the model. Different AIC values can be obtained when fitting the data by selecting different p and q.…”

Section: Arma Modelmentioning

confidence: 99%

Predicting Spare Parts Inventory of Hydropower Stations and Substations Based on Combined Model

Tang

Zhang

et al. 2022

Mathematical Problems in Engineering

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In this paper, a combined model is proposed to predict spare parts inventory in accordance with equipment characteristics and defect elimination records. Fourier series is employed to process the periodicity of the data, autoregressive moving average (ARMA) is used to deal with the linear autocorrelation of the data, and backpropagation (BP) neural network is used to settle the nonlinearity of the data. The prediction results, comparisons, and error analyses show that the combined model is accurate and meets the practical requirements. The combined model not only fully utilizes the information contained in the data but also provides a reasonable decision basis for the procurement of spare parts, making the inventory in a safe state and saving holding costs.

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“…In contrast to the model-based methods, data-driven methods take the battery as a black box without paying attention to the physical essence of it. These methods estimate the battery’s SOC directly by learning non-linear relationships between the SOC and the battery measurements (such as current and voltage) [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. In particular, more and more neural networks have been developed for SOC estimation due to their strong ability for nonlinear fitting [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime

Qian

Qin

et al. 2022

Materials

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Online state-of-charge (SOC) estimation for lithium-ion batteries is one of the most important tasks of the battery management system in ensuring its operation safety and reliability. Due to the advantages of learning the long-term dependencies in between the sequential data, recurrent neural networks (RNNs) have been developed and have shown their superiority over SOC estimation. However, only time-series measurements (e.g., voltage and current) are taken as inputs in these RNNs. Considering that the mapping relationship between the SOC and the time-series measurements evolves along with the battery degradation, there still remains a challenge for RNNs to estimate the SOC accurately throughout the battery’s lifetime. In this paper, a dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) is developed to overcome the above-mentioned challenge. Its most important characteristic is the adoption of the state of health (SOH) of the battery as the network input, in addition to time-series measurements. According to the experimental data from a batch of LiCoO2 batteries, it is validated that the proposed DIGF network is capable of providing more accurate SOC estimations throughout the battery’s lifetime compared to the existing RNN counterparts. Moreover, it also shows greater robustness against different initial SOCs, making it more applicable for online SOC estimations in practical situations. Based on these verification results, it is concluded that the proposed DIGF network is feasible for estimating the battery’s SOC accurately throughout the battery’s lifetime against varying initial SOCs.

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Unified Univariate-Neural Network Models for Lithium-Ion Battery State-of-Charge Forecasting Using Minimized Akaike Information Criterion Algorithm

Cited by 34 publications

References 47 publications

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Predicting Spare Parts Inventory of Hydropower Stations and Substations Based on Combined Model

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime

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