Journal
ENERGY
Volume 160, Issue -, Pages 466-477Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2018.06.220
Keywords
Li-ion battery; Health indicator; State-of-Health; Extreme learning machine
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Funding
- National Natural Science Foundation of China [51267002, 51667006]
- Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology [15-140-30S002]
- Innovation Project of Guangxi Graduate Education [YCSW2017038]
- Guangxi Natural Science Foundation [2015GXNSFAA139287]
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Battery health monitoring and management is critically important for electric vehicle performance and economy. This paper presents a multiple health indicators-based and machine learning-enabled state-of-health estimator for prognostics and health management. The multiple online health indicators without the influence of different loading profiles are used as effective signatures of the health estimator for effective quantification of capacity degradation. An extreme learning machine is introduced to capture the underlying correlation between the extracted health indicators and capacity degradation to improve the speed and accuracy of machine learning for online estimation. The proposed estimator is also compared to the traditional BP neural network. The associated results indicate that the maximum estimation error of the proposed health management strategy is less than 2.5%, and it has better performance and faster speed than the BP neural network. (C) 2018 Elsevier Ltd. All rights reserved.
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