Journal
MICROELECTRONICS RELIABILITY
Volume 55, Issue 9-10, Pages 1280-1284Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.microrel.2015.06.133
Keywords
Lithium-ion battery; Prognostic; State of health; Support vector regression; Particle swarm optimization
Funding
- National Natural Science Foundation of China (NSFC) [61304218]
- Beijing Higher Education Young Elite Teacher Project [YETP1123]
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State of health (SOH) estimation of lithium-ion batteries is significant for safe and lifetime-optimized operation. In this study, support vector regression (SVR) is employed in battery SOH prognostics, and particle swarm optimization (PSO) is employed in obtaining the SVR kernel parameter. Through a new validation method, the proposed PSO-SVR model in this paper can well grasp the global degradation trend of-SOH and is little affected by local regeneration and fluctuations. The case study shows that compared with the eight published methods, the proposed model can obtain more accurate SOH prediction results. Even SOH prediction starts from the cycle near capacity regeneration, the proposed model still can grasp the global degradation trend. Furthermore, the improved PSO-SVR model has great robustness when the training data contain noise and measurement outliers, which makes it possible to get satisfactory prediction performance without pre-processing the data manually. (C) 2015 Elsevier Ltd. All rights reserved.
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