Abstract
Battery aging is one of the critical problems to be tackled in battery research, as it limits the power and energy capacity during the battery’s life. Therefore, optimizing the design of battery systems requires a good understanding of aging behavior. Due to their simplicity, empirical and semiempirical models (EMs) are frequently used in smart charging studies, feasibility studies, and cost analyses studies, among other uses. Unfortunately, these models are prone to significant estimation errors without appropriate knowledge of their inherent limitations and the interdependence between stress factors. This article presents a review of empirical and semiempirical modeling techniques and aging studies, focusing on the trends observed between different studies and highlighting the limitations and challenges of the various models. First, we summarize the main aging mechanisms in lithium-ion batteries. Next, empirical modeling techniques are reviewed, followed by the current challenges and future trends, and a conclusion. Our results indicate that the effect of stress factors is easily oversimplified, and their correlations are often not taken into account. The provided knowledge in this article can be used to evaluate the limitations of aging models and improve their accuracy for various applications.
Highlights
S INCE they were first commercialized by Sony in 1991 [1], lithium-ion battery (LIB) technology has been widely adopted due to its relatively high energy and power density, high efficiency, and rather long lifetime [2]
Despite its growing market and relatively good performance, climate change, and electric vehicle (EV) applications, push for lower costs and higher energy densities over a long lifetime. These metrics are generally trade-offs, and understanding battery ageing and modelling is critical for optimizing LIB performance
This review focuses on the irreversible part of calendar ageing
Summary
S INCE they were first commercialized by Sony in 1991 [1], lithium-ion battery (LIB) technology has been widely adopted due to its relatively high energy and power density, high efficiency, and rather long lifetime [2]. LIBs are widely used in the mobile device industry, aerospace & aviation industry, and defence industry [3], [4]. All of this contributes to a rapidly increasing LIB market. Despite its growing market and relatively good performance, climate change, and electric vehicle (EV) applications, push for lower costs and higher energy densities over a long lifetime. These metrics are generally trade-offs, and understanding battery ageing and modelling is critical for optimizing LIB performance
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