基于曲线压缩和极限梯度提升算法的锂离子电池健康状态估计

作者：Liu Xing-Tao; Liu Xiao-Jian; Wu Ji^*; He Yao; Liu Xin-Tian

来源：Journal of Jilin University (Engineering and Technology Edition), 2022, 52(6): 1273-1280.

DOI：10.13229/j.cnki.jdxbgxb20210020

• Automotive engineering
• Douglas-Pucker algorithm
• Extreme gradient boosting algorithm
• Lithium-ion battery
• State of health estimation

In order to accurately estimate the State of Health (SOH) of the lithium-ion battery, a method based on Douglas-Puck algorithm and Extreme Gradient Boosting (XGBoost) algorithm is proposed. Firstly, each set of voltage data is preprocessed, and the Douglas-Puck algorithm is used to vectorize the constant current charging voltage curve of each cycle. On the basis of this data, the XGBoost algorithm is applied to establish a lithium-ion battery degradation model and estimate the SOH. The results of comparative experiments show that the proposed method can effectively compress the battery voltage curve and reduce the dimension of network training data. At the same time, the developed method also has a higher prediction accuracy and faster running speed, and can realize the fast and accurate estimation of the lithium-ion battery SOH. ? 2022, Jilin University Press. All right reserved.

• 单位
  合肥工业大学