An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation

•An ensemble prognostic method for estimating Li-ion battery capacity is developed.•A time-varying weight assignment approach based on V-IOWA operator is proposed.•The V-IOWA can realize dynamic weight computing using only verification dataset.•Overall, the ensemble prognostic method outperforms the individual algorithms.•The prognostic accuracy is highly improved based on the ensemble prognostic method. 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 validation-data 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.