Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
Source abstract
Abstract
In this paper, a novel state of health (SOH) estimation method based on partial charge voltage and current data is proposed. The extraction of feature variables, which are energy signal, the Ah-throughput, and the charge duration, is discussed and analyzed. The support vector machine (SVM) with radial basis function (RBF) as kernel function is applied for the SOH estimation. The predictive performance of the SOH by the SVM are performed with full and partial charging data. Experiment results show that the addressed approach enables estimating the SOH accurately for practical application.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
References (34)
Show source-provided references
1. Online state of charge estimation of lithium-ion batteries: A moving horizon estimation approach10.1016/j.ces.2016.06.061 ↗
2. A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy10.1016/j.apenergy.2014.10.034 ↗
3. A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter10.1016/j.jpowsour.2013.06.076 ↗
4. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking10.1016/j.apenergy.2016.07.126 ↗
5. Battery SOH Management Research in the US-China Clean Energy Research Center-Clean Vehicle Consortium10.1016/j.ifacol.2015.10.064 ↗
6. Moura, S.J., Krstic, M., and Chaturvedi, N.A. (2012). Adaptive PDE observer for battery SOC/SOH estimation. Am. Soc. Mech. Eng., 101–110.10.1115/DSCC2012-MOVIC2012-8800 ↗
7. A review on prognostics and health monitoring of Li-ion battery10.1016/j.jpowsour.2011.03.101 ↗
8. Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility10.1016/j.jpowsour.2014.01.085 ↗
9. A naive Bayes model for robust remaining useful life prediction of lithium-ion battery10.1016/j.apenergy.2013.12.020 ↗
10. A lead-acid battery’s remaining useful life prediction by using electrochemical model in the Particle Filtering framework10.1016/j.energy.2016.12.004 ↗
11. A comparative study of commercial lithium ion battery cycle life in electric vehicle: Capacity loss estimation10.1016/j.jpowsour.2014.06.111 ↗
12. Modeling the capacity degradation of LiFePO4/graphite batteries based on stress coupling analysis10.1016/j.jpowsour.2011.07.080 ↗
13. Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications10.1016/j.jpowsour.2013.03.158 ↗
14. Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction10.1007/s10845-014-0933-4 ↗
15. A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles10.1016/j.apenergy.2013.07.061 ↗
16. A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles10.1016/j.apenergy.2013.09.006 ↗
17. Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model10.1016/j.microrel.2016.07.151 ↗
18. Awan, M.A.A. (2014). Remaining Useful life Prediction of Lithium-Ion Batteries Using Particle Filter Framework. [Master’s Thesis, Karlstad University, Faculty of Technology and Science].
19. Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter10.1016/j.jpowsour.2014.07.176 ↗
20. Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model10.1016/j.jpowsour.2013.03.129 ↗
21. Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression10.1016/j.microrel.2013.03.010 ↗
22. Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method10.1016/j.jpowsour.2011.08.040 ↗
23. Michel, P.H., and Heiries, V. (2015, January 11–14). Book An Adaptive Sigma Point Kalman Filter Hybridized by Support Vector Machine Algorithm for Battery SoC and SoH Estimation. Proceedings of the 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), Glasgow, UK.10.1109/VTCSpring.2015.7145678 ↗
24. A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation10.1016/j.apenergy.2015.08.119 ↗
25. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods10.1016/j.jpowsour.2012.11.146 ↗
26. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation10.1016/j.jpowsour.2014.07.116 ↗
27. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression10.1016/j.jpowsour.2013.02.012 ↗
28. Saha, B., Goebel, K., and Battery data set (2018, May 22). NASA Ames Prognostics Data Repository, Available online: http://ti.arc.nasa.gov/project/prognostic-data-repository.
29. Eker, Ö., Camci, F., and Jennions, I.K. (2012, December 31). Major Challenges in Prognostics: Study on Benchmarking Prognostics Datasets. Available online: http://dspace.lib.cranfield.ac.uk/handle/1826/9994.10.36001/phme.2012.v1i1.1409 ↗
30. A Novel Feature Selection Approach Based on FODPSO and SVM10.1109/TGRS.2014.2367010 ↗
31. Cycle-life model for graphite-LiFePO4 cells10.1016/j.jpowsour.2010.11.134 ↗
32. Evaluation of simple performance measures for tuning SVM hyperparameters10.1016/S0925-2312(02)00601-X ↗
33. Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)?
34. Chang, C.-C., and Lin, C.-J. (2007). LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2.10.1145/1961189.1961199 ↗