Deep neural network battery charging curve prediction using 30 points collected in 10 min

Tian, Jinpeng; Xiong, Rui; Shen, Weixiang; Lu, Jiahuan; Yang, Xiao Guang

doi:10.1016/j.joule.2021.05.012

Cited by 211 publications

(53 citation statements)

References 47 publications

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“…Although high current rate reduces the charging time, it increases the temperature rise, energy loss, and performance degradation such as Solid Electrolyte Interphase (SEI) growth and Lithium plating deposition. To control the temperature rise, the Fuzzy Temperature Rise (FTR) controller, 156 Recurrent fuzzy neural network, 157 Generalized Regression Neural Network (GRNN) controller 158 and Deep neural network 159 have been developed to fine tune the current rate with the consideration of charging time and temperature rise.…”

Section: Charging and Discharging Of Batterymentioning

confidence: 99%

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Adaikkappan

Nageswari

2021

Intl J of Energy Research

149

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Extension of driving range and battery run time optimization are necessary key points in the modeling of Electric Vehicle (EV). In this view, Battery Management System (BMS) plays a major role to ensure a safe and trustworthy battery operation, especially when using Lithium-ion (Li-ion) batteries in an electric vehicle. Key function of BMS is State of Charge (SoC) estimation. A well-parameterized battery model is required for accurate state estimation. Consequently, the major factors to be considered in battery modeling are the SoC estimation and charging methodology of an effective BMS development. By focusing on these features, in this paper, the well-known battery models such as the electrochemical model, equivalent circuit model, and data-driven model are comprehensively reviewed along with their strengths and weaknesses. Further, the SoC estimation of a battery is also discussed by using standard methodologies such as direct estimation methods and model-based estimation methods. The comparisons of the three most distinct battery models and the classification of SoC estimation techniques to develop a proper BMS for EV with the focus on accuracy, configuration effort, computational complexity, ease of implementation, and real-time applications are systematically reviewed. In addition to this, convenient battery charging approaches with the consideration of some constraints such as charging time, charging efficiency, state of charge, state of health, charging voltage threshold, capacity fade, power fade, aging effect, capacity utilization, impedance rise, and temperature rise of the battery in EV are presented. Finally, the perspectives of the existing work and the recommended future research work of BMS are summarized.

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Section: Charging and Discharging Of Batterymentioning

confidence: 99%

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Adaikkappan

Nageswari

2021

Intl J of Energy Research

149

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“…For example, incremental capacity (IC) and differential voltage (DV) analysis ( Han et al., 2014 ) are two useful methods to extract features to evaluate battery health, and typical features include the peak values of the IC curves ( Jiang et al., 2020 ; Tang et al., 2021 ), the valley values of the DV curves ( Li et al., 2018 ), and the curve area within a given voltage range. In contrast, sequence-based methods directly use time-series data as the input and employ deep learning methods to achieve automatically feature extraction and nonlinear modeling, e.g., deep neural network ( Roman et al., 2021 ; Tian et al., 2021 ), long short-term memory network ( Deng et al., 2022b ; Li et al., 2020 ), deep convolutional neural network (DCNN) ( Shen et al., 2020 ), and their variants. These techniques usually use time-series data of battery current, temperature, voltage, and accumulated charge under complete or partial charging/discharging conditions as the input.…”

Section: Introductionmentioning

confidence: 99%

“…In many applications, the battery discharge process is a bit dynamic, while the charging process is relatively stable and usually pre-defined, such as in electric vehicles and smartphones. Therefore, many researchers developed health evaluation models based on the charging data ( Jiang et al., 2020 ; Li et al., 2018 ; Shen et al., 2020 ; Tian et al., 2021 ). In these studies, a specific voltage range and a fixed start/endpoint are required to ensure that the curves of different cycles have the same reference points.…”

Section: Introductionmentioning

confidence: 99%

Battery health evaluation using a short random segment of constant current charging

Deng

Xie

et al. 2022

iScience

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“…Our results are helpful to improve the estimation performance of structural variables of Arctic forests by using the concepts of image sampling and input features proposed in this paper [ 12 ]. This method also has the advantage of transfer learning; that is, DNN trained on one battery data set can use less training data to improve the curve estimation of other batteries running in different scenarios [ 13 ]. It will make a simple scientific overview of machine learning [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Planning of Sports Competition Based on Deep Neural Network

Jin

2022

Computational Intelligence and Neuroscience

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Physical education curriculum has been paid more and more attention by teachers and parents, and having a healthy body is the foundation. School sports competition is also more and more concerned by major researchers, and scholars have produced in-depth research and analysis of sports competition results prediction because prediction results can better let teachers carry out appropriate sports training for students, so as to achieve the best learning effect. The construction of the prediction model and whether the performance and universality of the model after construction are suitable for predicting sports competitions have also become a major research point. Deep neural network is a complex network method to analyze the structure of the human brain, which plays a core role in the field of sports planning and performance prediction and can know the future performance of athletes or students in advance in sports competitions. This paper establishes the autoregressive summation model prediction model, the complex neural network prediction model, and the improved complex neural network prediction model. It is concluded that only the improved BP neural network model has a remarkable effect on performance prediction, and the prediction value obtained by this prediction model can reduce the systematic error of prediction, so that it can better infer the performance prediction of sports competitions in China and plan which sports events are suitable for which prediction model.

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Deep neural network battery charging curve prediction using 30 points collected in 10 min

Cited by 211 publications

References 47 publications

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Battery health evaluation using a short random segment of constant current charging

Prediction and Planning of Sports Competition Based on Deep Neural Network

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