scholarly journals ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER MENGGUNAKAN BACK PROPAGATION NEURAL NETWORK

2020 ◽  
Vol 12 (2) ◽  
pp. 140-149
Author(s):  
Mohammad Imron Dwi Prasetyo ◽  
Hasnira Hasnira ◽  
Novie Ayub Windarko ◽  
Anang Tjahjono
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Open Circuit Voltage ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Open Circuit ◽  
State Of Charge ◽  
Input Layer

Baterai merupakan salah satu komponen yang penting dalam konteks implementasi renewable energy. Jenis Baterai yang memiliki kepadatan dalam penyimpanan energy adalah lithium polymer. Parameter dalam baterai yang harus diperhatikan adalah estimasi State Of Charge (SOC). Pada umumnya estimasi SOC baterai menggunakan metode coloumb counting karena tingkat kesulitanya rendah. Namun terdapat kelemahan dari sisi ketergantungan terhadap utilitas sensor arus yang digunakan sebagai akumulasi dari integral arus yang masuk maupun arus yang keluar terhadap waktu. Dalam penelitian ini menyajikan Back Propagation Neural Network (BPNN) sebagai algoritma untuk estimasi SOC berdasarkan kurva karakteristik OCV – SOC. Kurva karakteristik OCV – SOC baterai didapatkan dari pengujian pulsa baterai. Tegangan, arus, dan waktu discharging baterai digunakan sebagai input layer BPNN pertama untuk estimasi Open Circuit Voltage (OCV). OCV akan dilearning sebagai input layer BPNN kedua untuk estimasi SOC baterai. Hasil dari simulasi estimasi SOC didapatkan galat rata-rata sebesar 0.479% terhadap SOC riil berdasarkan kurva karakteristik OCV – SOC.

scholarly journals Perancangan Model Jaringan Syaraf Tiruan Propagasi Balik Untuk Mengestimasi Curah Hujan

2012 ◽  
Vol 9 (2) ◽  
Author(s):  
Elohansen Padang
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Back Propagation ◽  
Activation Function ◽  
Back Propagation Neural Network ◽  
Rainfall Data ◽  
Training Method ◽  
Output Layer ◽  
Input Layer ◽  
Hidden Layer

This research was conducted to investigate the ability of backpropagation artificial neural network in estimating rainfall. Neural network used consists of input layer, 2 hidden layers and output layer. Input layer consists of 12 neurons that represent each input; first hidden layer consists of 12 neurons with activation function tansig, while the second hidden layer consists of 24 neurons with activation function logsig. Output layer consists of 1 neuron with activation function purelin. Training method used is the method of gradient descent with momentum. Training method used is the method of gradient descent with momentum. Learning rate and momentum parameters defined respectively by 0.1 and 0.5. To evaluate the performance of the network model to recognize patterns of rainfall data is used in Biak city rainfall data from January 1997 - December 2008 (12 years). This data is divided into 2 parts, namely training and testing data using rainfall data from January 1997-December 2005 and data estimation using rainfall data from January 2006-December 2008. From the results of this study concluded that rainfall patterns Biak town can be recognized quite well by the model of back propagation neural network. The test results and estimates of the model results testing the value of R = 0.8119, R estimate = 0.53801, MAPE test = 0.1629, and MAPE estimate = 0.6813.

Combined estimation of the state of charge of a lithium battery based on a back-propagation– adaptive Kalman filter algorithm

2017 ◽  
Vol 232 (3) ◽  
pp. 357-366 ◽  
Author(s):  
Wanzhong Zhao ◽  
Xiangchuang Kong ◽  
Chunyan Wang
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
The State ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Adaptive Kalman Filter ◽  
Precise Estimation ◽  
Kalman Filter Algorithm

The precise estimation of the battery’s state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation–adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge.

scholarly journals Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system

2019 ◽  
Vol 15 (12) ◽  
pp. 155014771989452
Author(s):  
Shuo Li ◽  
Song Li ◽  
Haifeng Zhao ◽  
Yuan An
Keyword(s):  
Neural Network ◽  
Power System ◽  
Lithium Battery ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
The State ◽  
State Of Charge ◽  
Marquardt Algorithm ◽  
Uninterruptible Power ◽  
State Of Charge Estimation

In this article, a method for estimating the state of charge of lithium battery based on back-propagation neural network is proposed and implemented for uninterruptible power system. First, back-propagation neural network model is established with voltage, temperature, and charge–discharge current as input parameters, and state of charge of lithium battery as output parameter. Then, the back-propagation neural network is trained by Levenberg–Marquardt algorithm and gradient descent method; and the state of charge of batteries in uninterruptible power system is estimated by the trained back-propagation neural network. Finally, we build a state-of-charge estimation test platform and connect it to host computer by Ethernet. The performance of state-of-charge estimation based on back-propagation neural network is tested by connecting to uninterruptible power system and compared with the ampere-hour counting method and the actual test data. The results show that the state-of-charge estimation based on back-propagation neural network can achieve high accuracy in estimating state of charge of uninterruptible power system and can reduce the error accumulation caused in long-term operation.

scholarly journals FEED FORWARD NEURAL NETWORK SEBAGAI ALGORITMA ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER

2020 ◽  
Vol 7 (1) ◽  
pp. 13
Author(s):  
Mohammad Imron Dwi Prasetyo ◽  
Anang Tjahjono ◽  
Novie Ayub Windarko
Keyword(s):  
Neural Network ◽  
Smart Grid ◽  
Management System ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Feed Forward Neural Network ◽  
Feed Forward

<p><em>Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus,  dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.</em></p><p><strong>Keywords:</strong><em> </em><em>SOC, BMS, Coloumb Counting, OCV, FFNN</em></p><p><em>Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus,  dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.</em></p><p><strong>Kata kunci</strong><em>: </em><em>SOC, BMS, Coloumb Counting, OCV, FFNN</em></p><p><em><br /></em></p><p><em><br /></em></p>

Modelling of the Parameters of EDM in Gas Based on Back Propagation Neural Network

2018 ◽  
Vol 926 ◽  
pp. 11-16
Author(s):  
Yan Cherng Lin ◽  
Han Ming Chow ◽  
Hsin Min Lee ◽  
Jia Feng Liu
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Machining Parameters ◽  
Ann Model ◽  
Output Layer ◽  
Input Layer ◽  
Hidden Layer ◽  
Machining Characteristics

The aim of this study is to develop a predicted model of the machining parameters with relation to material removal rate (MRR) and surface roughness (SR) of electrical discharge machining (EDM) in gas. The experimental tasks were implemented by a specific design of experimental method named central composite design (CCD) method. The mathematical prediction models between operating parameters and machining characteristics based on artificial neural network (ANN) were established. The back propagation neural network (BPNN) was employed to construct the architecture of the input layer, the hidden layer and the output layer to build the ANN model. Moreover, the weight and the bias values were examined by the steepest descent method (SDM) with the training data. Thus, the suitable ANN models were established with the acquired weight and bias values. The essential parameters of the EDM in gas such as peak current (Ip), pulse duration (tp), gas pressure (GP), servo reference voltage (Sv) were chosen to investigate the effects on MRR and SR. The developed ANN model with 4 input variables on the input layer, one hidden layer with 5 neurons, and 2 response variables on the output layer was obtained by the training with 30 experimental data. Moreover, as the prediction values obtained from the ANN compared with the 5 testing data, the error falls in the rage of 5% indicating the developed ANN is appropriate and predictable. Moreover, the developed ANN model can be used to predict the machining characteristics such as MRR and SR for the EDM in gas with various parameter settings.

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