Paper Info
Title
Accurate State-of-Charge Estimation Approach for Lithium-Ion Batteries by Gated Recurrent Unit With Ensemble Optimizer
Abstract
State-of-charge (SoC) estimation is indispensable for battery management systems (BMSs). Accurate SoC estimation can improve the efficiency of battery utilization, especially for electric vehicles (EVs). Several kinds of battery SoC estimation approaches have been developed, but a simple and efficient method for battery SoC estimation that can adapt to a variety of lithium-ion batteries is worth exploring. To this end, a recurrent neural network (RNN) model based on a gated recurrent unit (GRU) is presented for battery SoC estimation. The GRU-RNN model can rapidly learn its own parameters by means of an ensemble optimization method based on the Nadam and AdaMax optimizers. The Nadam optimizer is used in the model pre-training phase to find the minimum optimized value as soon as possible, and then the AdaMax optimizer is used in the model fine-tuning phase to further determine the model parameters. To validate the effectiveness and robustness of the proposed method, the GRU-RNN model was trained and tested with three kinds of dynamic loading profiles and compared with existing SoC estimation methods. The experimental results show that the proposed method dramatically reduces the model training time and increases estimation accuracy.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2913078
IEEE ACCESS
Keywords
Field
DocType
Lithium-ion batteries,state of charge,gated recurrent unit,ensemble optimizer
Logic gate,Battery management systems,Computer science,Recurrent neural network,Electronic engineering,Robustness (computer science),Dynamic loading,Battery (electricity),Lithium,Distributed computing,State of charge
Journal
Volume
ISSN
Citations 
7
2169-3536
2
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Bin Xiao175.18
Bin Xiao275.18
Yonggui Liu3458.63