Topic
State of charge
About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.
Papers published on a yearly basis
Papers
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TL;DR: A recurrent neural network (RNN) model based on a gated recurrent unit (GRU) is presented for battery SoC estimation that dramatically reduces the model training time and increases estimation accuracy.
Abstract: State-of-charge (SoC) estimation is indispensable for battery management systems (BMSs). Accurate SoC estimation can improve the efficiency of battery utilization, especially for electric vehicles (EVs). Several kinds of battery SoC estimation approaches have been developed, but a simple and efficient method for battery SoC estimation that can adapt to a variety of lithium-ion batteries is worth exploring. To this end, a recurrent neural network (RNN) model based on a gated recurrent unit (GRU) is presented for battery SoC estimation. The GRU-RNN model can rapidly learn its own parameters by means of an ensemble optimization method based on the Nadam and AdaMax optimizers. The Nadam optimizer is used in the model pre-training phase to find the minimum optimized value as soon as possible, and then the AdaMax optimizer is used in the model fine-tuning phase to further determine the model parameters. To validate the effectiveness and robustness of the proposed method, the GRU-RNN model was trained and tested with three kinds of dynamic loading profiles and compared with existing SoC estimation methods. The experimental results show that the proposed method dramatically reduces the model training time and increases estimation accuracy.
70 citations
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TL;DR: A scenario-based two-stage stochastic linear programming model for scheduling EV charging processes for different grid requirements in real time using a rolling window approach that is applicable for various grid purposes.
70 citations
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TL;DR: In this paper, it is recommended to avoid charging beyond similar to 80% State-of-Charge (SOC) since topping-off to full capacity disproportionately increases the power consumption of electric vehicles.
Abstract: At electric vehicle fast-charging stations, it is generally recommended to avoid charging beyond similar to 80% State-of-Charge (SOC) since topping-off to full capacity disproportionately increases ...
70 citations
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18 Jun 2008TL;DR: In this article, a charge schedule is drawn up again as a charge re-schedule for a battery in a hybrid vehicle, when a difference between a target SOC (State Of Charge) and a present SOC becomes greater than or equal to a reference range in an estimated route to a destination.
Abstract: In charge-discharge control for a battery in a hybrid vehicle, when a difference between a target SOC (State Of Charge) and a present SOC becomes greater than or equal to a reference range in an estimated route to a destination, a charge schedule is drawn up again as a charge re-schedule. However, if the number of times of the re-schedule becomes greater than or equal to predetermined N times or if a remaining distance to the destination becomes less than a predetermined reference distance, a hybrid control based on the charge schedule is stopped without the charge re-schedule drawn up.
70 citations
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TL;DR: In this article, a multivariate adaptive regression splines (MARS) technique was used to estimate the state of charge (SOC) of a high capacity LiFePO4 battery cell from an experimental dataset obtained in the University of Oviedo Battery Laboratory.
Abstract: State of charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state of charge is thus particularly important for electric vehicles (EVs), hybrid EVs, or portable devices. The aim of this innovative study is to estimate the SOC of a high-capacity lithium iron phosphate (LiFePO4) battery cell from an experimental dataset obtained in the University of Oviedo Battery Laboratory using the multivariate adaptive regression splines (MARS) technique. An accurate predictive model able to forecast the SOC in the short term is obtained and it is a first step using the MARS technique to estimate the SOC of batteries. The agreement of the MARS model with the experimental dataset confirmed the goodness of fit for a limited range of SOC (25-90% SOC) and for a simple dynamic data profile [constant-current (CC) constant-voltage charge-CC discharge].
70 citations