scholarly journals State of Charge estimation of lithium-ion power battery based on online parameter identification method and BP neural network

2022 ◽  
pp. ArticleID:220138
Author(s):  
Shuai Qin ◽  
Keyword(s):  
Neural Network ◽  
Parameter Identification ◽  
Bp Neural Network ◽  
Lithium Ion ◽  
State Of Charge ◽  
Identification Method ◽  
Power Battery ◽  
State Of Charge Estimation

State of charge estimation of lithium-ion batteries based on an improved parameter identification method

Energy ◽  
2015 ◽  
Vol 90 ◽  
pp. 1426-1434 ◽  
Author(s):  
Bizhong Xia ◽  
Chaoren Chen ◽  
Yong Tian ◽  
Mingwang Wang ◽  
Wei Sun ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
State Of Charge ◽  
Identification Method ◽  
State Of Charge Estimation

scholarly journals State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network

Batteries ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 69 ◽  
Author(s):  
Chuan-Wei Zhang ◽  
Shang-Rui Chen ◽  
Huai-Bin Gao ◽  
Ke-Jun Xu ◽  
Meng-Yue Yang
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Training Data ◽  
Particle Swarm Algorithm ◽  
Power Battery

Accurately estimating the state of charge (SOC) of power batteries in electric vehicles is of great significance to the measurement of the endurance mileage of electric vehicles, as well as the safety protection of the power battery. In view of lithium ion batteries’ nonlinear relation between SOC estimation and current, voltage, and temperature, the improved Back Propagation (BP) neural network method is proposed to accurately estimate the SOC of power batteries. To address the inherent limitations of BP neural network, particle swarm algorithm is adopted to modify the relevant weighting coefficients. In this paper, the lithium iron phosphate battery (3.2 V/20 Amper-Hour) was studied. Charge and discharge experiments were conducted under a constant temperature. The training data were used to construct the surrogate model using the improved BP neural network. It is noted that the accuracy of the developed algorithm is increased by 2% as compared to that of conventional BP. Finally, an actual vehicle condition experiment was designed to further verify the accuracy of these two algorithms. The experimental results show that the improved algorithm is more suitable for real vehicle operating conditions than the traditional algorithm, and the estimation accuracy can meet the industry standards to a greater extent.

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