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

Abstract

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.

Citation

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
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 70
• Article Number: 108038
• DOI: https://doi.org/10.1016/j.est.2023.108038
• Keywords: Lithium-ion battery; Sliding window; Long short-term memory; Capacity estimation; Differential intergration; Moving average autoregressive model; Multiple time-scale factors
• Public URL: https://rgu-repository.worktribe.com/output/1992884