A New Generation of Aging Models for Lithium-ion Batteries

This article is contributed by Dr. Jan Singer, Head of Battery Research and Modeling at TWAICE

How physics-motivated semi-empirical aging models can change the way we design batteries

Over the last years, scientists have invested a lot of time and resources to optimize semi-empirical, physico-chemical, and data-driven simulation models. All of them show different challenges and limitations. To overcome this dead end, scientists and engineers combine physicochemical-mechanical degradation effects and integrate them into (semi)-empirical as well as data-driven approaches.

To optimize battery simulation models, battery engineers at TWAICE have developed so-called physics-motivated semi-empirical aging models. The latest version includes cutting edge features such as Open Circuit Voltage (OCV)-aging, degradation modes, as well as the pioneering concept of swelling force modeling. This article summarizes the features and briefly explains their importance.

OCV aging

In electric models, OCV is a key element. However, OCV changes over a battery’s lifetime. This change, unfortunately, is often neglected in battery models, making them inaccurate. By modeling the electrode potentials and their balances, the OCV can be reconstructed. Furthermore, the anodic and cathodic potentials can be separately given as outputs. This results on the one hand in a higher accuracy of the electric model, especially for degraded cells. On the other hand, the updated OCV can be used e.g., for the parametrization of state estimation filters such as Kalman filters for State of Charge and State of Health estimation.

Figure 1: Aging curves for Anode Potential, Cathode Potential and the full cell OCV

Degradation modes

Batteries undergo complex degradation processes as they are subjected to different operating conditions over time. Loss of lithium inventory, loss of active anode material, and loss of active cathode material are the three main degradation modes. Degradation modes simulation provides a better understanding of how components of a cell are degrading. For example, loss of active material can be understood as the separation of the anodic and cathodic capacity. The user receives an idea about how much the single electrodes have already aged. This knowledge is instrumental in designing optimized battery systems, such as sizing different elements for specific applications, like fast charging.

Figure 2: Degradation mode curves

Examining OCV aging and degradation modes of battery systems is integral in understanding a battery’s overall performance and longevity.

Next to these two features the concept of swelling force modeling provides a new perspective on the mechanical stress batteries undergo during their lifespan.

Simulation of swelling force

Batteries breathe — they mechanically expand when cycled and this effect increases with progressing degradation of the battery. Pouch and prismatic cells in particular are subject to constraints due to this volume change. Swelling force can be added as an additional model output. This is particularly relevant when developing the mechanical design of the battery system.

All of these innovative techniques have the potential to further enhance the safety, performance, and lifetime of batteries in electric vehicles and energy storage systems.

Figure 3: Modeling of swelling force

A whitepaper explains the new features in more detail. Get access to it here!

This article is contributed by TWAICE, a member company of the Volta Foundation.

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