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

• Published in: Energies (Basel) Vol. 16; no. 10; p. 4243
• Main Authors: An, Jiakun, Guo, Wei, Lv, Tingyan, Zhao, Ziheng, He, Chunguang, Zhao, Hongshan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2023
• Subjects: Accuracy; Aging; Algorithms; Analysis; Batteries; Deep learning; Electric charge; Energy; Energy storage; joint prediction; Kalman filters; Lithium; Lithium batteries; Lithium-ion batteries; lithium-ion battery; Long short-term memory; Mathematical optimization; Methods; Neural networks; Optimization; Power plants; Prediction models; PSO-XGBoost; Spectrum analysis; State of charge; state of health; United Kingdom
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16104243

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.