Lithium Battery State-of-Charge Estimation Based on AdaBoost.Rt-RNN

Ran Li, Hui Sun (), Xue Wei, Weiwen Ta and Haiying Wang
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Ran Li: Automotive Electronic Drive Control and System Integration Engineering Research Center, Ministry of Education, Harbin 150080, China
Hui Sun: Automotive Electronic Drive Control and System Integration Engineering Research Center, Ministry of Education, Harbin 150080, China
Xue Wei: Automotive Electronic Drive Control and System Integration Engineering Research Center, Ministry of Education, Harbin 150080, China
Weiwen Ta: School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China
Haiying Wang: School of Automation, Harbin University of Science and Technology, Harbin 150080, China

Energies, 2022, vol. 15, issue 16, 1-15

Abstract: Real-time and accurate state-of-charge estimation performs an important role in the smooth operation of various electric vehicle battery management systems. Neural network theory represents one of the most effective and commonly used methods of SOC prediction. However, traditional neural network methods are disadvantaged by such issues as the limited range of application, limited generalization ability, and low accuracy, which makes it difficult to meet the increasing safety requirements on electric vehicles. In view of these problems, an ensemble learning algorithm based on the AdaBoost.Rt is proposed in this paper. AdaBoost.Rt recurrent neural network model is purposed to ensure the accurate prediction of lithium battery SOC. Relying on a chain-connected recurrent neural network model, this method enables the correlation adaptability of sample data in the spatio-temporal dimension. The ensemble learning method was adopted to devise a method of multi-RNN model integration, with the RNN model as the base learner, thus constructing the AdaBoost.Rt-RNN strong learner model. According to the results of simulation and experimental comparisons, the integrated algorithm proposed in this paper is applicable to improve the accuracy of SOC prediction and the generalization performance of the model.

Keywords: lithium battery; neural networks; state-of-charge; ensemble learning; AdaBoost.Rt (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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