Journal
APPLIED ENERGY
Volume 266, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2020.114817
Keywords
Lithium-ion battery; Capacity estimation; Prediction; Ensemble prognostics; Time-varying weight; Induced ordered weighted averaging operator
Categories
Funding
- National Natural Science Foundation of China, China [61803013, 61973011, 61903015]
- Civil Aircraft Special Research Project, China [MJZ-2018-Y-58]
- Science & Technology on Reliability and Environmental Engineering Laboratory Fund, China [6142004190501]
- Capital Science & Technology Leading Talent Program, China [Z191100006119029]
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Capacity estimation is of great significance to help assess the performance degradation of lithium-ion batteries, so as to take actions to extend their lifetime. Traditional capacity estimation methods for Lithium-ion batteries are usually based on individual model-based or data-driven prognostic approaches. However, no single prognostic method performs appropriately for all possible situations as each individual method presents particular assumptions and application limitations. Therefore, this paper presents an ensemble prognostic framework that combines multiple individual prognostic algorithms to improve the accuracy and robustness of battery capacity estimation. In the proposed ensemble prognostic framework, the degraded capacity data of the full battery life cycles are divided into three parts: a training dataset, a validation dataset, and a test dataset, among which the training and validation datasets are employed for member prognostic model training, the validation dataset is utilized for weight calculation, and the test dataset is used for prognostic performance assessment. A validationdata based induced ordered weighted averaging (IOWA) operator, i.e. V-IOWA operator, is proposed to realize time-varying weight assignment. By summing the weighted prognostic results of each member prognostic algorithm, the ensemble prognostic results are finally obtained. Effectiveness of the proposed approach was validated based on datasets provided by NASA Ames Prognostics Center of Excellence. The experiment results indicated that the proposed ensemble prognostic approach outperforms individual prognostic algorithms with a higher accuracy.
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