A feedforward deep neural network for predicting the state-of-charge of lithium-ion battery in electric vehicles

Adedeji, Bukola Peter; Kabir, Golam

doi:10.1016/j.dajour.2023.100255

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…The results found in [35] indicate that the feed-forward backpropagation neural network can predict not only the SOC of the batteries of HEV and EV vehicles; you can also predict, with a value of 0.1478 RMSE, variables such as current, voltage, mileage and speed. For this reason, it opens a gap to implement more variables that allow the state of charge to be calculated more accurately, coinciding with what is proposed in this work by increasing the temperature variable as an input parameter to better predict the SOC of the batteries, obtaining an RMSE of 0.0154.…”

Section: Performance Comparison Of Fbnn and Rbnnmentioning

confidence: 96%

“…Finally, [34] used hybrid long-short-term memory (LSTM) neural networks to predict multistep electric vehicle charging station occupancy, for which the results show that the proposed method has a high degree of accuracy. This gives greater support to the research of [35] where the authors used the feed-forward backpropagation regressive network, whose approach is calculated using a virtual function to input the model output variable. In this work, various neural network architectures and 10 training scenarios were used to predict the SOC, voltage, current, speed and mileage but only for lithium-ion batteries.…”

Section: Disadvantagesmentioning

confidence: 97%

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Electric Vehicle NiMH Battery State of Charge Estimation Using Artificial Neural Networks of Backpropagation and Radial Basis

Hernández,

Fernández,

Torres

2023

WEVJ

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The state of charge of a battery depends on many magnitudes, but only voltage and intensity are included in mathematical equations because other variables are complex to integrate into. The contribution of this work was to obtain a model to determine the state of charge with these complex variables. This method was developed considering four models, the multilayer feed-forward backpropagation models of two and three input variables used supervised training, with the variable-learning-rate backpropagation training function, five and seven neurons in the hidden layer, respectively, achieving an optimal training. Meanwhile, the radial basis neural network models of two and three input variables were trained with the hybrid method, the propagation constant with a value of 1 and 80 neurons in the hidden layer. As a result, the radial basis neural network with the variable-learning-rate training function, considering the discharge temperature, was the one with the best performance, with a correlation coefficient of 0.99182 and a confidence interval of 95% (0.98849; 0.99516). It is then concluded that artificial neural networks have high performance when modeling nonlinear systems, whose parameters are difficult to measure with time variation, so estimating them in formulas where they are omitted is no longer necessary, which means an accurate SOC.

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Section: Performance Comparison Of Fbnn and Rbnnmentioning

confidence: 96%

Section: Disadvantagesmentioning

confidence: 97%

Electric Vehicle NiMH Battery State of Charge Estimation Using Artificial Neural Networks of Backpropagation and Radial Basis

Hernández,

Fernández,

Torres

2023

WEVJ

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show abstract

“…For datadriven method, the future battery SOC is predicted from the battery's historical operating data through machine learning methods to avoid complex modeling process, such as support vector machine [7]. Deep learning methods have been successful in several fields, especially fault diagnosis [8]− [10], where their data-driven nature enables them to learn complex patterns of battery SOC from large amounts of data. Feedforward neural network (FNN) [11] and recursive neural network (RNN) are the most common neural networks used in SOC prediction.…”

Section: Dear Editormentioning

confidence: 99%

State of charge prediction of lithium-ion batteries for electric aircraft with swin transformer

Zhang,

Hao,

Zhang

2024

IEEE/CAA J. Autom. Sinica

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“…The neural networks learn graphical representation graph by making the insertion node graph in the lower-dimensional space [7]. Training to support this representation studied to end reflect the properties of the structural chart of interest for the problems encountered [8]. Representation of nodes iteratively embedding created by combining information from the environment each node [9].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm

Pratama,

Haiyudi

2024

ITJRD

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Analysis of social networks or online communities can be very difficult when working on large networks, as many measurements require expensive hardware. For example, identifying the community structure of a network is a very computationally expensive task. Embedded graph is a way to represent graphs with vectors, so that further analysis becomes easier. The purpose of this research is to analyze the knowledge graph from the wikipedia article data. This research aims to implement web scraping techniques on the wikipedia article search engine and display similar wikipedia pages and analyze them using a predetermined deep learning algorithm. Data collection in this research used scraping techniques to retrieve data from the unstructured wikipedia website and then processed it into structured data. The method used in this research is a standard cross-industry process for data mining by performing phases of data collection, data processing, proposed algorithms, testing and evaluation. The algorithm applied is deepwalk, kmeans, girvan newman. By doing this research, it is expected to provide knowledge about the deep learning approach for data representation of the wikipedia pages knowledge graph and can help users find similar wikipedia pages and enrich literacy on knowledge graph analysis.

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A feedforward deep neural network for predicting the state-of-charge of lithium-ion battery in electric vehicles

Cited by 12 publications

References 36 publications

Electric Vehicle NiMH Battery State of Charge Estimation Using Artificial Neural Networks of Backpropagation and Radial Basis

Electric Vehicle NiMH Battery State of Charge Estimation Using Artificial Neural Networks of Backpropagation and Radial Basis

State of charge prediction of lithium-ion batteries for electric aircraft with swin transformer

Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm

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