“The market potential of ‘Beyond Lithium Ion’ batteries is enormous, in particular with regard to electromobility: their theoretically higher energy and power density means that electric cars achieve a higher range,” says Lars Pescara, research associate at Fraunhofer IEE. “With our modeling, we help battery manufacturers and automotive suppliers unlock this potential.”

Dendrites can cause short circuits

However, gaining further energy and power density from metallic lithium electrodes is challenging. First, the reaction of lithium with the electrolyte grows a layer on the electrode that protects it (Solid Electrolyte Interphase, SEI). This process also occurs in conventional lithium-ion batteries. However, when metallic lithium electrodes are used (without a stabilizing grid), the SEI is repeatedly ruptured, reformed and thickened over time due to the mechanical stress when the battery is charged and discharged. This increases the internal resistance and reduces cell performance.

Furthermore, the lithium grows as needle-shaped dendrites during deposition. These can penetrate the separator within the battery and make direct electrical contact with the counter electrode. As a result, a short circuit occurs. Dendrite formation therefore represents a significant safety risk.

Battery management systems (BMSs) are intended to prevent these effects: their task is to control the operation of the battery packs such that SEI growth is minimized and dendrite formation is avoided. Meanwhile, should a defect occur, BMSs are required to identify it at an early stage. In this way, affected batteries can be replaced before they become a safety risk.

To perform these tasks, the BMSs use complex algorithms that determine the state of the battery from macroscopic measured variables such as current, voltage and temperature.

Models replace complex measurements with real batteries

In order to optimize the operation and detect defective batteries, the algorithms of the BMS must be carefully trained and verified. This can be done using real batteries that operate under different conditions. This includes different temperatures, as well as different loads that represent the various climatic and weather conditions or usage scenarios.

However, experimental measurements come at a high price, both literally, as in the cost of the laboratory infrastructure, specialized personnel and safety technology, and in terms of the time required for the measurements, and the limited reproducibility and thus reliability of the results.

For this reason, on the “metaLit” project, Fraunhofer IEE is developing an alternative: mathematical models that can simulate any battery state and pass it on to the BMS via a hardware component. The models thus serve as battery emulators in hardware-in-the-loop test benches, eliminating the need for time-consuming measurements with real batteries. “This saves time and money—and provides more reliable results due to the reproducibility of the data,” says Pescara.

In emulating the physical and electrochemical processes within the batteries, the researchers can draw on lithium-ion battery models that have been developed for the Fraunhofer IEE “BaSiS” simulation software.

Experimental analysis for characterization

A detailed understanding of the processes that take place within the battery is required to implement the model. For example, the temporal development of SEI growth and dendrite formation must be knownn

For this reason, Fraunhofer IEE is collaborating on “metaLit” with the Forschungsinstitut Edelmetalle + Metallchemie (fem) research institute: in an experimental analysis, experts at fem are characterizing the dynamic properties of the metallic lithium electrodes within the battery. In addition to conventional electrochemical methods, research methods are used that have been adjusted for the analysis of metallic lithium, which is difficult to access.

The “metaLit” project will run until September 30, 2022 and is funded by the German Federal Ministry for Economic Affairs and Energy via the AiF as part of the program to promote joint industrial research (IGF) on the basis of a resolution passed by the German Bundestag.