Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction

SOH (state of health) estimation is important for battery management. Since the electrochemical reaction inside LIBS (lithium-ion battery system) is extremely complex and the external working environment is uncertain, it is difficult to achieve accurate determination of SOH. To improve the accuracy of SOH estimation, we propose a SOH estimation method for lithium-ion battery based on XGBoost algorithm with accuracy correction. We extract several features, including average voltage, voltage difference, current difference, and temperature difference, to describe the aging process of batteries. Due to the higher prediction accuracy and generalization ability of ensemble learning algorithm, the XGBoost model is established to estimate the SOH of lithium-ion battery. Then, the estimation values are corrected by Markov chain. Compared with the methods by XGBoost, random forest, k-nearest neighbor algorithm (KNN), SVM, linear regression, our proposed method shows an accuracy improvement by 10% to 20%. Additionally, the errors of our method are also superior to the others in terms of the average absolute error, root mean square error, and root mean square error.

[1]  Yuanyuan Liu,et al.  Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model , 2013 .

[2]  H. Gasteiger,et al.  Aging behavior of lithium iron phosphate based 18650-type cells studied by in situ neutron diffraction , 2017 .

[3]  Xu Chen,et al.  Ensemble Learning for Short-Term Traffic Prediction Based on Gradient Boosting Machine , 2017, J. Sensors.

[4]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

[5]  Bo-Suk Yang,et al.  Intelligent prognostics for battery health monitoring based on sample entropy , 2011, Expert Syst. Appl..

[6]  Jun Xu,et al.  Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications , 2013 .

[7]  Ralph E. White,et al.  Mathematical modeling of the capacity fade of Li-ion cells , 2003 .

[8]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .

[9]  Yu Peng,et al.  Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression , 2013, Microelectron. Reliab..

[10]  Inés Couso,et al.  A design methodology for semi-physical fuzzy models applied to the dynamic characterization of LiFePO4 batteries , 2014, Appl. Soft Comput..

[11]  Miaohua Huang,et al.  Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model , 2016, Microelectron. Reliab..

[12]  Xuning Feng,et al.  State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking , 2016 .

[13]  Göran Lindbergh,et al.  A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation , 2014 .

[14]  Michael Buchholz,et al.  Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods , 2013 .

[15]  Taejung Yeo,et al.  A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation , 2015 .