An improved sliding window: long short-term memory modeling method for real-world capacity estimation of lithium-ion batteries considering strong random charging characteristics.
Wang, Shunli; Takyi-Aninakwa, Paul; Jin, Siyu; Liu, Ke; Fernandez, Carlos
Dr Carlos Fernandez email@example.com
Capacity estimation plays a significant role in ensuring safe and acceptable energy delivery, especially under real-time complex working conditions for whole-life-cycle lithium-ion batteries. For high-precision and robust capacity estimation, an improved sliding window-long short-term memory (SW-LSTM) modeling method is proposed by introducing multiple time-scale charging characteristic factors. The optimized feature information set is extracted by constructing an optimized differential integration-moving average autoregressive (DI-MAA) model, which is introduced as the input matrices of the whole-life-cycle capacity estimation model. With the constructed DI-MAA model, the relevant features are effectively extracted, overcoming the data limitation problem of the long-term dependence capacity estimation. For the experimental test, the maximum capacity estimation error is 3.56%, and the average relative error is 0.032 under the complex Beijing bus dynamic stress test working condition. The proposed SW-LSTM estimation model with optimized DI-MAA-based data pre-processing treatment has high stability and robust advantages, serving an effective safety assurance for lithium-ion batteries with real-world complex working condition adaptation advantages.
WANG, S., TAKYI-ANINAKWA, P., JIN, S., LIU, K. and FERNANDEZ, C. 2023. An improved sliding window: long short-term memory modeling method for real-world capacity estimation of lithium-ion batteries considering strong random charging characteristics. Journal of energy storage [online], 70, article 108038. Available from: https://doi.org/10.1016/j.est.2023.108038
|Journal Article Type||Article|
|Acceptance Date||Jun 10, 2023|
|Online Publication Date||Jun 17, 2023|
|Publication Date||Oct 15, 2023|
|Deposit Date||Jun 19, 2023|
|Publicly Available Date||Jun 18, 2024|
|Journal||Journal of energy storage|
|Peer Reviewed||Peer Reviewed|
|Keywords||Lithium-ion battery; Sliding window; Long short-term memory; Capacity estimation; Differential intergration; Moving average autoregressive model; Multiple time-scale factors|
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