Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Shuo Sun, Qianli Zhang, Junzhong Sun, Wei Cai, Zhiyong Zhou, Zhanlu Yang and Zongliang Wang
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Shuo Sun: Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China
Qianli Zhang: College of Engineering, Ocean University of China, Qingdao 266042, China
Junzhong Sun: Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China
Wei Cai: Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China
Zhiyong Zhou: Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China
Zhanlu Yang: Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China
Zongliang Wang: Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China

Energies, 2022, vol. 15, issue 16, 1-20

Abstract: 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.

Keywords: lead–acid battery; state of charge (SOC); AdaBoost algorithm; online sequence extreme learning machine (OSELM); incremental learning (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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