State of Charge Prediction of Mine-Used LiFePO 4 Battery Based on PSO-Catboost

Dazhong Wang (), Yinghui Chang, Pengfei Ji, Yanchun Suo and Ning Chen
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Dazhong Wang: CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
Yinghui Chang: CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
Pengfei Ji: CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
Yanchun Suo: CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
Ning Chen: CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China

Energies, 2024, vol. 17, issue 23, 1-13

Abstract: The accurate prediction of battery state of charge (SOC) is one of the critical technologies for the safe operation of a power battery. Aiming at the problem of mine power battery SOC prediction, based on the comparative experiments and analysis of particle swarm optimization (PSO) and Categorical Boosting (Catboost) characteristics, the PSO-Catboost model is proposed to predict the SOC of a power lithium iron phosphate battery. Firstly, the classification model based on Catboost is constructed, and then the particle swarm algorithm is used to optimize the Catboost hyperparameters to build the optimal model. The experiment and comparison show that the optimized model’s prediction accuracy and average precision are superior to other comparative models. Compared with the Catboost model, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of the PSO-Catboost model decreased by 12.4% and 25.4% during charging and decreased by 5.5% and 12.2% during discharging. Finally, the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models, both ensemble learning models, are selected and compared with PSO-Catboost after being optimized via PSO. The experimental results show that the proposed model has a better performance. In this paper, experiments show that the optimization model can select parameters more intelligently, reduce the error caused by artificial experience to adjust parameters, and have a better theoretical value and practical significance.

Keywords: ensemble learning; mining lithium iron phosphate battery; particle swarm optimization; Catboost; SOC (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: 2024
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