Joint Prediction of the State of Charge and the State of Health of Lithium-Ion Batteries Based on the PSO-XGBoost Algorithm

Jiakun An, Wei Guo, Tingyan Lv (), Ziheng Zhao, Chunguang He and Hongshan Zhao
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Jiakun An: State Grid Hebei Economic Research Institute, Shijiazhuang 050000, China
Wei Guo: State Grid Hebei Economic Research Institute, Shijiazhuang 050000, China
Tingyan Lv: Department of Electric Power Engineering, North China Electric Power University, Baoding 071003, China
Ziheng Zhao: State Grid Hebei Economic Research Institute, Shijiazhuang 050000, China
Chunguang He: State Grid Hebei Economic Research Institute, Shijiazhuang 050000, China
Hongshan Zhao: Department of Electric Power Engineering, North China Electric Power University, Baoding 071003, China

Energies, 2023, vol. 16, issue 10, 1-14

Abstract: Lithium-ion batteries are widely used in power grids as a common form of energy storage in power stations. The state of charge (SOC) and state of health (SOH) reflect the capacity and lifetime variation in the Li-ion batteries, and they are important state parameters of Li-ion batteries. Therefore, the establishment of accurate SOC and SOH prediction models is an essential prerequisite for the correct assessment of the status of lithium batteries, the improvement of the operational accuracy of energy-storage stations, and the development of maintenance plans for energy-storage stations. This paper first analyzes the correlation between SOC and SOH, and then proposes a joint SOC and SOH prediction model using the particle swarm optimization (PSO) algorithm to optimize the extreme gradient boosting algorithm (XGBoost), which takes into account the dynamic correlation between SOC and SOH dynamics, thus enabling more accurate SOC and SOH prediction. Finally, the prediction model is validated using the Oxford battery aging dataset. The correlation between SOC and SOH is verified by comparing the joint prediction results with the SOC individual prediction results. Then, the prediction results of the PSO-XGBoost model, the traditional XGBoost model, and the long short-term memory neural network are compared to verify the effectiveness and accuracy of the PSO-XGBoost model.

Keywords: lithium-ion battery; PSO-XGBoost; state of charge; state of health; joint prediction (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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