scholarly journals A Neural Network Application for a Lithium-Ion Battery Pack State-of-Charge Estimator with Enhanced Accuracy

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 33
Author(s):  
Gabriel C. S. Almeida ◽  
A. C. Zambroni de Souza ◽  
Paulo F. Ribeiro
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Mean Squared Error ◽  
Lithium Ion ◽  
Time Estimation ◽  
State Of Charge ◽  
Battery Pack ◽  
Squared Error ◽  
Real Time Estimation ◽  
Artificial Neural Network Ann

A State-of-Charge (SOC) real-time estimation plays an essential role in effective energy management. This paper proposes the use of an Artificial Neural Network (ANN) to design a state-of-charge estimator for a Graphite/LiCoO2 lithium-ion battery pack. The software MATLAB was used to develop and test several network configurations to find the ideal weights for the ANN. The results demonstrate that the Mean Squared Error (MSE) achieved renders the ANN as an effective technique. Thus, it predicted the battery bank’s SOC values with accuracy using only voltage, current, and charge/discharge time as inputs.

Design and Simulation of an Accurate Neural Network State-of-Charge Estimator for Lithium Ion Battery Pack

2017 ◽  
Vol 10 (2) ◽  
pp. 186 ◽  
Author(s):  
Youssef Cheddadi ◽  
Omar Diouri ◽  
Ahmed Gaga ◽  
Fatima Errahimi ◽  
Najia Es-Sbai
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery Pack ◽  
Network State

scholarly journals State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 306
Author(s):  
Shuqing Li ◽  
Chuankun Ju ◽  
Jianliang Li ◽  
Ri Fang ◽  
Zhifei Tao ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
High Efficiency ◽  
Lithium Ion ◽  
Clean Energy ◽  
Degradation Process ◽  
State Of Charge ◽  
Average Error ◽  
Estimation Model ◽  
Battery Degradation

Due to the rapidly increasing energy demand and the more serious environmental pollution problems, lithium-ion battery is more and more widely used as high-efficiency clean energy. State of Charge (SOC) representing the physical quantity of battery remaining energy is the most critical factor to ensure the stability and safety of lithium-ion battery. The novelty SOC estimation model, which is two recurrent neural networks with gated recurrent units combined with Coulomb counting method is proposed in this paper. The estimation model not only takes voltage, current, and temperature as input feature but also takes into account the influence of battery degradation process, including charging and discharging times, as well as the last discharge charge. The SOC of the battery is estimated by the network under three different working conditions, and the results show that the average error of the proposed neural network is less than 3%. Compared with other neural network structures, the proposed network estimation results are more stable and accurate.

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