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

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.

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State of Charge Estimation of Low-speed Electric Vehicle Battery using Back Propagation Neural Network

2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) ◽

10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00215 ◽

2020 ◽

Author(s):

Menglong Xu ◽

Wenguang Wu ◽

Wenwen Zhou ◽

Yuanyuan Ma ◽

Xiaolei Shi ◽

...

Keyword(s):

Neural Network ◽

Electric Vehicle ◽

Back Propagation ◽

Back Propagation Neural Network ◽

State Of Charge ◽

Low Speed ◽

Electric Vehicle Battery ◽

State Of Charge Estimation

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

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407017701533 ◽

2017 ◽

Vol 232 (3) ◽

pp. 357-366 ◽

Cited By ~ 7

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.

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Intelligent state of charge estimation of lithium-ion batteries based on L-M optimized back-propagation neural network

Journal of Energy Storage ◽

10.1016/j.est.2021.103442 ◽

2021 ◽

Vol 44 ◽

pp. 103442

Author(s):

Guanyong Zhang ◽

Bizhong Xia ◽

Jiamin Wang

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Back Propagation ◽

Lithium Ion ◽

Back Propagation Neural Network ◽

State Of Charge ◽

State Of Charge Estimation

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Modeling of Back-Propagation Neural Network Based State-of-Charge Estimation for Lithium-Ion Batteries with Consideration of Capacity Attenuation

Advances in Electrical and Computer Engineering ◽

10.4316/aece.2019.03001 ◽

2019 ◽

Vol 19 (3) ◽

pp. 3-10 ◽

Cited By ~ 8

Author(s):

S. ZHANG ◽

X. GUO ◽

X. ZHANG

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Back Propagation ◽

Lithium Ion ◽

Back Propagation Neural Network ◽

State Of Charge ◽

State Of Charge Estimation

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Earthquake Prediction Based on Levenberg-Marquardt Algorithm Constrained Back-Propagation Neural Network Using DEMETER Data

Knowledge Science, Engineering and Management - Lecture Notes in Computer Science ◽

10.1007/978-3-642-15280-1_57 ◽

2010 ◽

pp. 591-596 ◽

Cited By ~ 3

Author(s):

Lingling Ma ◽

Fangzhou Xu ◽

Xinhong Wang ◽

Lingli Tang

Keyword(s):

Neural Network ◽

Earthquake Prediction ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Marquardt Algorithm ◽

Levenberg Marquardt

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The State of Charge Estimation of Li-Ion Batteries in Electric Vehicle Based on RBF Neural Network Optimized by Genetic Algorithm

International Journal of Digital Content Technology and its Applications ◽

10.4156/jdcta.vol7.issue5.44 ◽

2013 ◽

Vol 7 (5) ◽

pp. 366-373

Author(s):

Jun Bi ◽

Qiuping Xu ◽

Kai Wang ◽

Dong Zhang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Electric Vehicle ◽

Rbf Neural Network ◽

The State ◽

State Of Charge ◽

Li Ion Batteries ◽

Li Ion ◽

State Of Charge Estimation

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Research of MH-Ni Battery State of Charge Estimation Based on Grey System Neural Network Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.66-68.583 ◽

2011 ◽

Vol 66-68 ◽

pp. 583-587 ◽

Cited By ~ 2

Author(s):

Jian Xiong Long

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Pulse Current ◽

The State ◽

State Of Charge ◽

Current Response ◽

Grey System ◽

Battery State Of Charge ◽

State Of Charge Estimation

In order to effectively achieve MH-Ni battery state of charge estimation, grey system neural network model is put forward to predict battery state of charge by using the parameters of battery pulse current response signal as input for grey system neural network. The state of charge is as the network output and the response parameters of the battery pulse current as the input. The results show that its prediction accuracy of the state of charge can be achieved to requirements of the electric vehicles in applications by this method to predict the state of charge.

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