Data‐driven battery degradation prediction: Forecasting voltage‐capacity curves using one‐cycle data. Issue 5 (20th April 2022) - Tian, Jinpeng; Xiong, Rui; Shen, Weixiang; Lu, Jiahuan
Abstract: With the wide deployment of rechargeable batteries, battery degradation prediction has emerged as a challenging issue. However, battery life defined by capacity loss provides limited information regarding battery degradation. In this article, we explore the prediction of voltage‐capacity curves over battery lifetime based on a sequence to sequence (seq2seq) model. We demonstrate that the data of one present voltage‐capacity curve can be used as the input of the seq2seq model to accurately predict the voltage‐capacity curves at 100, 200, and 300 cycles ahead. This offers an opportunity to update battery management strategies in response to the predicted consequences. Besides, the model avoids feature engineering and is flexible to incorporate different numbers of input and output cycles. Therefore, it can be easily transplanted to other battery systems or electrochemical components. Furthermore, the model features data generation, that is, we can use the data of only one cycle to generate a large spectrum of aging data at the future cycles for developing other battery diagnosis or prognosis methods. In this way, the time and energy consuming battery degradation tests can be sharply reduced. Abstract : Accurate prediction of battery degradation is a prerequisite to advanced battery design and management. This article proposes a sequence to sequence deep‐learning model to predict future voltage‐capacity curves. The prediction results can more comprehensively reflect battery degradation than scalar degradation states, such as the capacity loss. The developed model can also be utilised to generate synthetic degradation data.