Research Advances on Lithium-Ion Batteries Calendar Life Prognostic Models

Abstract

In military reserve power supplies, there is an urgent need for superior secondary batteries to replace conventional primary batteries, and lithium-ion batteries (LIBs) emerge as one of the best choices due to their exceptional performance. The life of LIBs includes cycle life and calendar life, with calendar life spanning from years to decades. Accurate prediction of calendar life is crucial for optimizing the deployment and maintenance of LIBs in military applications. Model-based prognostics are usually established to estimate calendar life using accelerated aging methods under various storage conditions. This review firstly outlines the general prognostic workflow for calendar life of LIBs, analyzes degradation mechanisms, and summarizes influencing factors; then, we introduce calendar life prognostic models, evolving from simplistic empirical models (EMs) to nonempirical mechanistic models (MMs) based on LIB calendar aging knowledge and then to traditional hybrid empirical-mechanistic models (trad-EMMs). Finally, the data-driven models (DDMs) based on machine learning (ML) are discussed due to the limitation of the traditional methods, evolving from pure data-driven to knowledge-integrated models and establishing a comprehensive framework for calendar life assessment. To the best of our knowledge, this paper presents the first comprehensive review in this field, summarizing calendar life prognostic models of LIBs and offering some insights into future model development directions. Model-based prognostics can facilitate researchers in calendar aging analysis and calendar life prolongation, thereby better serving the application of LIBs in national economic life.

Keywords

calendar life prognostic / lithium-ion batteries / machine learning / traditional models

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