scholarly journals Towards an Optimal Deep Neural Network for SOC Estimation of Electric-Vehicle Lithium-Ion Battery Cells

2021 ◽  
pp. 11-18
Author(s):  
Muhammad Anjum ◽  
Moizzah Asif ◽  
Jonathan Williams
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Computational Cost ◽  
Lithium Ion ◽  
Test Phase ◽  
Soc Estimation ◽  
Error Metrics ◽  
Minimal Configuration ◽  
Estimation Models ◽  
Battery Cells

AbstractThis paper has identified a minimal configuration of a DNN architecture and hyperparameter settings to effectively estimate SOC of EV battery cells. The results from the experimental work has shown that a minimal configuration of hidden layers and neurons can reduce the computational cost and resources required without compromising the performance. This is further supported by the number of epochs taken to train the best DNN SOC estimations model. Hence, demonstrating that, the risk of overfitting estimation models to training datasets, can also be subsided. This is further supported by the generalisation capability of the best model demonstrated through the decrease in error metrics values from test phase to those in validation phase.

scholarly journals Estimation of State of Charge of Lithium-Ion Batteries Based on Wide and Deep Neural Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Di Mu ◽  
Shuning Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Estimation Error ◽  
Lithium Ion ◽  
Soc Estimation ◽  
Deep Model ◽  
Proposed Model ◽  
Bpnn Model

It is important to accurately estimate the SOC to ensure that the lithium-ion battery is within a safe working range, prevent over-charging and over-discharging, and ultimately improve battery life. However, SOC is an internal state of the battery and cannot be directly measured. This paper proposes a SOC estimation method based on the wide and deep neural network model, which combines the linear regression (LR) model and the backpropagation neural network (BPNN) model. This article uses the dataset provided by the Advanced Energy Storage and Applications (AESA) group to verify the performance of the model. The performance of the proposed model is compared with the common BPNN model in terms of root mean square error (RMSE), average absolute proportional error (MAPE), and SOC estimation error. The validation results prove that the effect of the proposed model in estimating SOC is better than that of the ordinary BPNN model. Compared with the BPNN model, the RMSE values of the SOC predicted value of the wide and deep model in the charging and discharging stages were reduced by 10.2% and 15.4%, respectively. Experimental results show that the maximum SOC estimation error of the model in predicting the SOC during charging and discharging is 0.42% and 0.86%, respectively.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

A method for predicting the capacity of a lithium-ion battery based on deep neural network

2021 ◽  
Author(s):  
JamesChan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Lithium Ion ◽  
Chemical Conversion ◽  
Absolute Error ◽  
Production Costs ◽  
Percentage Error ◽  
Average Percentage ◽  
True Value ◽  
Battery Capacity

This paper proposes a solution to predict the capacity of the lithium-ion battery's capacity division process using deep learning methods. This solution extracts the physical observation records of part of the process steps from the chemical conversion and volumetric processes as features, and trains a Deep Neural Network (DNN) to achieve accurate prediction of battery capacity. According to the test, the average percentage absolute error (Mean Absolute Percentage Error, MAPE) of the battery capacity predicted by this model is only 0.78% compared with the true value. Combining this model with the production line can greatly reduce production time and energy consumption, and reduce battery production costs.

Web application to support evidence of individual emotional impact evoked by COVID-19 pandemic restrictions (Preprint)

2021 ◽  
Author(s):  
Hugo Mitre-Hernandez ◽  
Rodolfo Ferro-Perez ◽  
Francisco Gonzalez-Hernandez
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Web Application ◽  
Deep Neural Network ◽  
Data Transfer ◽  
Computational Cost ◽  
Low Computational Cost ◽  
The Web

BACKGROUND Mental health effects during COVID-19 quarantine need to be handled because patients, relatives, and healthcare workers are living with negative emotional behaviors. The clinical disorders of depression and anxiety are evoking anger, fear, sadness, disgust, and reducing happiness. Therefore, track emotions with the help of psychologists on online consultations –to reduce the risk of contagion– will go a long way in assisting with mental health. The human micro-expressions can describe genuine emotions of people and can be captured by Deep Neural Networks (DNNs) models. But the challenge is to implement it under the poor performance of a part of society's computers and the low speed of internet connection. OBJECTIVE This study aimed to create a useful and usable web application to record emotions in a patient’s card in real-time, achieving a small data transfer, and a Convolutional Neural Networks (CNN) model with a low computational cost. METHODS To validate the low computational cost premise, firstly, we compare DNN architectures results, collecting the floating-point operations per second (FLOPS), the Number of Parameters (NP) and accuracy from the MobileNet, PeleeNet, Extended Deep Neural Network (EDNN), Inception- Based Deep Neural Network (IDNN) and our proposed Residual mobile-based Network (ResmoNet) model. Secondly, we compare the trained models' results in terms of Main Memory Utilization (MMU) and Response Time to complete the Emotion recognition (RTE). Finally, we design a data transfer that includes the raw data of emotions and the basic text information of the patient. The web application was evaluated with the System Usability Scale (SUS) and a utility questionnaire by psychologists and psychiatrists (experts). RESULTS All CNN models were set up using 150 epochs for training and testing comparing the results for each variable in ResmoNet with the best model. It was obtained that ResmoNet has 115,976 NP less than MobileNet, 243,901 FLOPS less than MobileNet, and 5% less accuracy than EDNN (95%). Moreover, ResmoNet used less MMU than any model, only EDNN overcomes ResmoNet in 0.01 seconds for RTE. Finally, with our model, we develop a web application to collect emotions in real-time during a psychological consultation. For data transfer, the patient’s card and raw emotional data have 2 kb with a UTF-8 encoding approximately. Finally, according to the experts, the web application has good usability (73.8 of 100) and utility (3.94 of 5). CONCLUSIONS A usable and useful web application for psychologists and psychiatrists is presented. This tool includes an efficient and light facial emotion recognition model. Its purpose is to be a complementary tool for diagnostic processes.

scholarly journals State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1546
Author(s):  
M. S. Hossain Lipu ◽  
M. A. Hannan ◽  
Aini Hussain ◽  
Afida Ayob ◽  
Mohamad H. M. Saad ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Optimal Number ◽  
State Of Charge ◽  
Optimization Approach ◽  
Safe Driving ◽  
Soc Estimation ◽  
Improved Firefly Algorithm

The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences.

scholarly journals SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1620 ◽  
Author(s):  
Ngoc-Tham Tran ◽  
Abdul Khan ◽  
Thanh-Tung Nguyen ◽  
Dae-Wook Kim ◽  
Woojin Choi
Keyword(s):  
Parameter Estimation ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Observer ◽  
Soc Estimation ◽  
Nonlinear State ◽  
Battery Cells

scholarly journals Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1262 ◽  
Author(s):  
Sahar Khaleghi ◽  
Yousef Firouz ◽  
Maitane Berecibar ◽  
Joeri Van Mierlo ◽  
Peter Van Den Bossche
Keyword(s):  
Electric Vehicles ◽  
Computational Cost ◽  
Real Life ◽  
Lithium Ion ◽  
Stress Factors ◽  
State Of Health ◽  
Current Profile ◽  
Diagnostic Features ◽  
Wide Range ◽  
Battery Cells

The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

scholarly journals ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

2017 ◽  
Vol 8 (4) ◽  
pp. 3192-3203 ◽  
Author(s):  
J. S. Smith ◽  
O. Isayev ◽  
A. E. Roitberg
Keyword(s):  
Neural Network ◽  
Force Field ◽  
Deep Neural Network ◽  
Organic Molecules ◽  
Computational Cost ◽  
Quantum Mechanical ◽  
Data Set

We demonstrate how a deep neural network (NN) trained on a data set of quantum mechanical (QM) DFT calculated energies can learn an accurate and transferable atomistic potential for organic molecules containing H, C, N, and O atoms.

scholarly journals State of Charge Estimation of Lithium-ion Battery Based on Improved Recurrent Neural Network

2021 ◽  
Vol 2109 (1) ◽  
pp. 012005
Author(s):  
Zhenya Wang ◽  
Xiaodong Li ◽  
Yahui Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Lithium Iron Phosphate ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Maximum Error ◽  
State Of Charge ◽  
Equivalent Model ◽  
Estimation Model ◽  
Soc Estimation

Abstract Aiming at the problem of gradient disappearance and gradient explosion in the recurrent neural network (RNN) algorithm in the battery estimation model, this paper proposes a state of charge (SOC) estimation model developed using an independent recurrent neural network (IndRNN). Firstly, the Thevenin equivalent model of the battery is established, and the parameters of the equivalent model are identified through experiments. Then, the Relu activation function is introduced into the RNN to separate the neurons in each layer. Finally, the model was trained on various experimental data sets collected from the lithium iron phosphate battery experimental platform under different discharge conditions. Without any prior knowledge about the battery interior, the proposed battery model successfully characterizes the non-linear behavior of the battery. The results show that under different discharge conditions, IndRNN is better than RNN in terms of maximum error, the mean error, and the root mean square error (RMSE), which greatly improves the accuracy of SOC estimation.

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