scholarly journals Joint Dynamic Strategy of Bayesian Regularized Back Propagation Neural Network with Strong Robustness - Extended Kalman Filtering for the Battery State-of-Charge Prediction

2021 ◽  
pp. ArticleID:21118
Author(s):  
Yifen Hu ◽  
Keyword(s):  
Neural Network ◽  
Kalman Filtering ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
State Of Charge ◽  
Extended Kalman Filtering ◽  
Dynamic Strategy ◽  
Strong Robustness ◽  
Battery State Of Charge

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.

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.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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