Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence ext...

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Bibliographic Details
Published in:Energies (Basel) Vol. 15; no. 16; p. 5842
Main Authors: Sun, Shuo, Zhang, Qianli, Sun, Junzhong, Cai, Wei, Zhou, Zhiyong, Yang, Zhanlu, Wang, Zongliang
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2022
Subjects:
Accuracy
Acids
AdaBoost algorithm
Algorithms
Efficiency
Electric charge and distribution
Electrolytes
incremental learning
lead–acid battery
Lithium
Machine learning
Mathematical models
Measurement
Methods
Neural networks
online sequence extreme learning machine (OSELM)
Parameter identification
state of charge (SOC)
China
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en15165842