Quantifying the state of the art of electric powertrains in battery electric vehicles: Range, efficiency, and lifetime from component to system level of the Volkswagen ID.3

Modeling the Inhomogeneous Lithium Plating in Lithium-ion Batteries Induced by Non-uniform Temperature Distribution

Potential utilization of battery energy storage systems (BESS) in the major European electricity markets

Lithium-ion (Li-ion) batteries have become the preferred power source for electric vehicles (EVs) due to their high energy density, low self-discharge rate, and long cycle life. Over the past decade, technological enhancements accompanied by massive cost reductions have enabled the growing market diffusion of EVs. This diffusion has resulted in customized and cost-effective Li-ion battery cell designs tailored to automotive requirements. This study describes design trends in Li-ion batteries from the pack to the electrode level based on empirical data, including pack energy, cell capacity, outer cell dimensions and formats, energy density, specific energy, and electrode properties, such as active material selection, porosities, and component thicknesses. Market share-weighted findings imply several trends, such as (1) increasing cell dimensions, with the longest cells reaching 500 mm (pouch) and almost 1000 mm (prismatic) in 2021, (2) increasing differentiation between either high-energy or low-cost cathode and anode materials, and (3) increasing cell energy, equivalent to gaining about 100% (energy density) and 70% (specific energy) compared to the 2010 and 2021 averages. Despite these improvements, this study finds that the widespread market diffusion of the latest cell technologies proceeds slower than industry announcements suggest and that several well-known, literature-proofed potentials are not yet fully exploited.

/pdf/trends-in-automotive-battery-cell-design-a-statistical-1tvr1k09.pdf

Trends in Automotive Battery Cell Design: A Statistical Analysis of Empirical Data

Precise lifetime predictions for lithium‐ion cells are crucial for efficient battery development and thus enable profitable electric vehicles and a sustainable transformation towards zero‐emission mobility. However, limitations remain due to the complex degradation of lithium‐ion cells, strongly influenced by cell design as well as operating and storage conditions. To overcome them, a machine learning framework is developed based on symbolic regression via genetic programming. This evolutionary algorithm is capable of inferring physically interpretable models from cell aging data without requiring domain knowledge. This novel approach is compared against established approaches in case studies, which represent common tasks of lifetime prediction based on cycle and calendar aging data of 104 automotive lithium‐ion pouch‐cells. On average, predictive accuracy for extrapolations over storage time and energy throughput is increased by 38% and 13%, respectively. For predictions over other stress factors, error reductions of up to 77% are achieved. Furthermore, the evolutionary generated aging models meet requirements regarding applicability, generalizability, and interpretability. This highlights the potential of evolutionary algorithms to enhance cell aging predictions as well as insights.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202200630

Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells

Although lithium plating is one of the most-investigated degradation mechanisms occurring in lithium-ion batteries, many degradation studies still view inhomogeneous lithium plating as an artifact. It is often caused by mechanical stress, gas formation and encapsulation, or temperature and current gradients on account of the cell size. In this work, large-format lithium-ion cells are cycled at conditions harmless on the material level, but cause heavy lithium plating in the center of the commercial cell. Failure analysis requires visual inspection and further material evaluation to identify the reason for malfunction. A combination of temperature gradient and current density gradient evoke a lithium concentration gradient, which equalizes by a lateral lithium-ion flow. The inhomogeneous distribution of lithium again distorts the operating potential window, resulting in local violation of the critical lithium plating condition. Thus, our study shows that inhomogeneity in a large-format lithium-ion cell has to be taken into account when designing the operating window. In addition, accelerated testing of large-format lithium-ion cells raises degradation mechanisms, which do not appear during regular usage.

Temperature and Lithium Concentration Gradient Caused Inhomogeneous Plating in Large-format Lithium-ion Cells

Cycle parameter dependent degradation analysis in automotive lithium-ion cells

Corrigendum to “Temperature and Lithium Concentration Gradient Caused Inhomogeneous Plating in Large-format Lithium-ion Cells” [J. Energy Storage, volume 41, September 2021, 102887]

Investigation and modeling of cyclic aging using a design of experiment with automotive grade lithium-ion cells

Corrigendum to “Cycle parameter dependent degradation analysis in automotive lithium-ion cells”. Journal of Power Sources, Volume 506, 15 September 2021, 230227

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