Robust empirical aging model generation methodology: Description and validation

Abstract

The aging behavior of each lithium-ion battery is unique, and its observation requires expensive tests. Consequently, it does not exist a standardized aging model and, in most cases, the available aging data is insufficient to describe completely the aging behavior. This paper proposes a robust aging model generation methodology applicable to any lithium-ion battery with any kind of lab-level aging data availability. This methodology has four phases: firstly, an expert aging study; secondly, the main trend model; thirdly, the integration of the effect of the aging stress factors on the main trend model; fourthly, the verification of the resultant aging model. The robustness of the aging model is provided to the model thanks to the verification process where the accuracy of describing the testing cases, the correctness of describing interpolated operating conditions, and the correctness of describing the aging at extrapolated time periods and cycle numbers are checked. This methodology has been applied to an example aging data set. The result is a customized robust aging model that describes the aging behavior observed on the provided aging data that validates the proposed methodology on a real use case. The proposal exploits the universality of the empirical aging model while minimizing the inherent uncertainty levels, obtaining like this a robust and universal aging modeling methodology.