Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future.publishedVersio

Eco-Efficiency of a Lithium-Ion Battery for Electric Vehicles: Influence of Manufacturing Country and Commodity Prices on GHG Emissions and Costs

Lithium-ion battery packs inside electric vehicles represents a high share of the final price. Nevertheless, with technology advances and the growth of the market, the price of the battery is getting more competitive. The greenhouse gas emissions and the battery cost have been studied previously, but coherent boundaries between environmental and economic assessments are needed to assess the eco-efficiency of batteries. In this research, a detailed study is presented, providing an environmental and economic assessment of the manufacturing of one specific lithium-ion battery chemistry. The relevance of parameters is pointed out, including the manufacturing place, the production volume, the commodity prices, and the energy density. The inventory is obtained by dismantling commercial cells. The correlation between the battery cost and the commodity price is much lower than the correlation between the battery cost and the production volume. The developed life cycle assessment concludes that the electricity mix that is used to power the battery factory is a key parameter for the impact of the battery manufacturing on climate change. To improve the battery manufacturing eco-efficiency, a high production capacity and an electricity mix with low carbon intensity are suggested. Optimizing the process by reducing the electricity consumption during the manufacturing is also suggested, and combined with higher pack energy density, the impact on climate change of the pack manufacturing is as low as 39.5 kg CO2 eq/kWh. Document type: Articl

Methodology for the characterization and understanding of longitudinal wrinkling during calendering of lithium-ion and sodium-ion battery electrodes

"The manufacturing of lithium-ion battery (LIB) cells is following a complex process chain in which the individual process steps influence the subsequent ones. Meanwhile, increasing requirements especially concerning the battery performance, sustainability and costs are forcing the development of innovative battery materials, production technologies and battery designs. The calendering process directly affects the volumetric energy density of an electrode and therefore of a battery cell. Calendering is still challenging as it causes high stresses in the electrode that lead to defects and thus increased rejection rates. The interaction between electrode material and process as well as the formation of defects is still not fully understood, especially when new material systems are used. In this context, the sodium-ion battery (SIB) is one post-lithium battery system that is a promising option to overcome the limitations of conventional LIBs. Therefore, this paper presents a first material and machine independent methodology to describe and understand the defect type longitudinal wrinkle, which mostly appears at the uncoated current collector edge of an electrode and in running direction. The aim is to systematically characterize the longitudinal wrinkles according to their geometry. The automatic data acquisition is carried out with a laser triangulation system and a 3D scanning system. The geometry values are calculated from the raw data and correlated to selected process parameters. The methodology is applicable regardless of the material as shown by exemplary results of NMC811 cathodes for LIB and hard carbon anodes for SIB. By using two different pilot calenders it is shown, that the data acquisition can be carried out independently of the machine. The presented methodology contributes to finding solutions for the avoidance of longitudinal wrinkling in any battery electrode and therefore to reducing the rejection rate.

Accelerated multiscale & multiphysics modelling tools for battery cell manufacturing improvement

The recent launch of battery factories in Europe, motivates intense efforts to achieve cost-effective, scalable and sustainable battery manufacturing processes. Within DEFACTO project, multiscale multiphysics modelling tools are developed to increase lithium-ion battery (LIB) cell manufacturing process productivity and performance. A novel workflow framework that mimics the main cell manufacturing steps such as the electrode processing and electrolyte filling and later predicts cell performance and ageing is presented to turbocharge the development of next-generation LIBs. In addition, taking advantage of the characterization and manufacturing data to feed and validate the computational tools, the resulting workflow aims at providing deep understanding and therefore guidance to reduce the production process time and cost while increasing the overall efficiency of battery cells

Battery performance analysis and parameterisation through advanced modelling techniques

Scientific interest in improving and also accelerating high-performance batteries has greatly increased over the past decade. Despite their immense promise, lithium-ion batteries (LIBs) and solid-state batteries (SSBs) continue to face multiple challenges that must be overcome before their successful commercialisation in Electric Vehicles (EVs). To this end, it is believed that efficient models (or even fast models) are needed to further assist designers in the battery development phase. In addition, to ensure good model fidelity, the sensitivity of the parameters and their dependence on internal parameters need to be carefully addressed and properly quantified. The aim of this thesis is to contribute to the battery development phase, speeding up the procedure and reducing the number of experimental work required by developing an efficient tool based on dimensionless analysis and the appropriate design of experiments to ensure accuracy in the parameterisation activities. To begin with, the dimensionless analysis methodology to identify the limiting mechanisms in the battery cell performance is argued. The dimensionless parameters are obtained by condensing the physical dimensional parameters involved in the description of the electrochemical phenomena taking place in the battery cell. In the following, two application cases are considered to show the potentiality of the dimensionless analysis approach. First, to demonstrate the effectiveness of the proposed procedure, the analysis of graphite electrodes performed by Malifarge et al. [Malifarge et al 2018 J. Electrochem. Soc. 165 A1275] is reviewed to confirm that dimensionless parameters (of the graphite half-coin cells analysed in the study) are able to anticipate the results in [Malifarge et al. 2018 J. Electrochem. Soc. 165 A1275] with negligible cost (in temporal, economic or computational terms). Second, the characteristic properties of a solid hybrid polymer battery presented by Park et al. [Park et al. 2018 Solid State Ionics 315 65–70] are calculated. By analysing the resulting dimensionless parameters, the identification of the main physical phenomena limiting the battery performance is carried out. As a demonstration of the proposed methodology, the limiting mechanisms identified and predicted by the dimensionless analysis are numerically verified. Then, the limiting mechanisms of a prototype SSB cell manufactured at CIDETEC have been analysed in detail. As demonstrated, the experimental results confirm the predictions made by the dimensionless analysis. First, CIDETEC manufactures a reference PEO-based SSB cell. After the complete parameterisation of the battery, performed experimentally, the dimensionless parameters are calculated. With the resulting values, new cell design configurations are proposed and validated against numerical codes first and experimentally by manufacturing the new cell components and testing them in the laboratory. Finally, the identifiability of the diffusion coefficient in the electrolytic phase is analysed in detail and an improved parameterisation methodology compared to the state of the art is derived. Thus, the novel methodology developed exploits the non-linear behaviour of the Li-Li cell dynamics by solving the model numerically and order reduction techniques are applied to reduce the computational cost. For this purpose, ROM and DEIM techniques have been implemented to guarantee a high accuracy of the model. The resulting identification methodology is able to significantly reduce the experimental effort required to characterise the electrolyte concentration-dependent transport properties compared to the state-of-the-art approach, since the model retains nonlinearities and, therefore, broader pulse conditions can be applied, ensuring identifiability. RESUMEN El interés científico por mejorar y también acelerar el desarrollo de las baterías de altas prestaciones ha crecido exponencialmente durante la última década. A pesar de su inmensa promesa, las baterías de iones de litio y las baterías de estado sólido siguen enfrentándose a múltiples retos que deben superarse antes de su comercialización con éxito en los vehículos eléctricos. Con respecto a esto, se espera que los modelos multifísicos eficientes ayuden a los diseñadores en la fase de desarrollo de las baterías. Además, para garantizar una buena precisión de dichos modelos, es necesario abordar detenidamente la sensibilidad de los parámetros, retener su dependencia con los parámetros internos y cuantificarlos adecuadamente. El objetivo de esta tesis es contribuir a la fase de desarrollo de las baterías, acelerando el procedimiento y reduciendo la carga experimental mediante el desarrollo de una herramienta eficiente basada en el análisis adimensional y en el diseño adecuado de los experimentos para asegurar la precisión en las actividades de parametrización. En primer lugar, se define la metodología de análisis adimensional para identificar los mecanismos limitantes que harán mermar las prestaciones de las baterías. Los parámetros adimensionales se obtienen agrupando los parámetros físicos dimensionales que intervienen en la descripción de los fenómenos electroquímicos que tienen lugar en la celda de la batería. Después, como prueba de concepto de la metodología, se consideran dos casos de aplicación. En primer lugar, para demostrar la eficacia del procedimiento propuesto, se revisa el análisis de electrodos de grafito realizado por Malifarge y col. [Malifarge et al. 2018 J. Electrochem. Soc. 165 A1275] para confirmar que los parámetros adimensionales (de las semiceldas de grafito analizadas en el estudio) son capaces de anticipar los resultados anunciados en su trabajo [Malifarge et al. 2018 J. Electrochem. Soc. 165 A1275] con un coste insignificante (en términos temporales, económicos y computacionales). En segundo lugar, se calculan las propiedades características de una batería de polímero híbrido sólido presentada por Park y col. [Park et al. 2018 Solid State Ionics 315 65–70]. Mediante el análisis de los parámetros adimensionales resultantes, se realiza la identificación de los principales fenómenos físicos que limitan la batería. Como demostración de la metodología propuesta, se verifican numéricamente los mecanismos limitantes identificados y predichos por el análisis adimensional. Después, se analizan detenidamente los mecanismos limitantes de un prototipo de celda de electrolito sólido desarrollado y manufacturado en CIDETEC. Los resultados experimentales confirman las predicciones realizadas por el análisis adimensional. Para ello, se fabrica en CIDETEC una tecnología de electrolito sólido de referencia basada en óxido de polietileno. Tras la parametrización completa de la celda, se calculan los parámetros adimensionales. Con los valores resultantes, se proponen nuevas configuraciones de diseño de las celdas, que se validan contra los códigos numéricos primero y experimentalmente mediante la fabricación de los nuevos componentes de las celdas y su ensayo posterior en el laboratorio. Finalmente, se analiza en detalle la identificabilidad del coeficiente de difusión en la fase electrolítica y se deriva una metodología de parametrización mejorada respecto al estado del arte. Así, la novedosa metodología desarrollada explota el comportamiento no lineal de la dinámica de la celda de Li-Li resolviendo el modelo numéricamente y se aplican técnicas de reducción de orden para reducir el coste computacional del mismo. Para ello, se han implementado técnicas de Descomposición Ortogonal Propia y Método de interpolación empírica discreta que garantizan una elevada precisión del modelo. La metodología de identificación resultante es capaz de reducir significativamente el esfuerzo experimental necesario para caracterizar las propiedades de transporte dependientes de la concentración del electrolito en comparación con el enfoque más avanzado, ya que el modelo conserva las no linealidades y, por tanto, se pueden aplicar condiciones de pulso más amplias, garantizando la identificabilidad

On the Use of Dimensionless Parameters for Fast Battery Performance Analysis

Dimensionless analysis arises as an appropriate methodology to study battery cells behavior since it brings the opportunity to identify limiting mechanisms in the battery cell performance by analysing a reduced number of dimensionless parameters. These parameters are obtained by condensating the physical dimensional parameters involved in the description of the electrochemical phenomena taking place in the cell. Here, based on the well known Doyle, Fuller and Newman model [M. Doyle, T.F. Fuller, J. Newman, J. Electrochem. Soc., 140(6) 1526–1533 (1993)] (DFN model, in short), a comprehensive set of dimensionless parameters is derived and the information arising from each dimensionless parameter is explained. Application to the design of a solid polymer electrolyte cell is considered, where limiting transport mechanisms are identified through dimensionless parameters analysis and guidelines for a cell redesign are also drawn from this analysis. Predictions made by inspection of the derived comprehensive set of dimensionless parameters are validated both numerically (using the DFN model) and experimentally

Toward high-performance energy and power battery cells with machine learning-based optimization of electrode manufacturing

International audienceThe optimization of the electrode manufacturing process is important for upscaling the application of Lithium -Ion Batteries (LIBs) to cater for growing energy demand. LIB manufacturing is important to be optimized because it determines the practical performance of the cells when the latter are being used in applications such as electric vehicles. In this study, we tackled the issue of high-performance electrodes for desired battery applications by proposing a data-driven approach supported by a deterministic machine learning-assisted pipeline for bi-objective optimization of the electrochemical performances. This pipeline allows the inverse design of the process parameters to adopt to manufacture electrodes for energy or power applications. This work is an analogy to our previous work that addressed the optimization of the electrode microstructures for kinetic, ionic, and electronic transport properties improvement. An electrochemical model is fed with the electrode properties characterizing the electrode microstructures generated by manufacturing simulations, and used to simulate the electrochemical performances. Secondly, the resulting dataset was used to train a deterministic model to implement fast optimizations to identify optimal electrodes. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes

New Insights on Tortuosity Determination by EIS for Battery Electrodes: Effect of Electrolyte Concentration and Temperature

International audienceNowadays, lithium-ion battery design and manufacturing are supported more and more by modelling and simulation. In this context, tortuosity has become a critical parameter characterizing porous battery electrode, in particular for those phenomena involving microstructure-performance relationships. The modelling and simulation of such processes, including also performance characterization, requires an accurate and reliable estimation of tortuosity values to obtain meaningful results, but taking into account the experimental framework. In this work, we present our findings on the determination of tortuosity for a porous commercial-based NMC622 cathode using an intercalating electrolyte, LiPF6 in commonly used EC:EMC solvents, at different temperatures (-10 to 45 degrees C) and electrolyte concentrations (0.50 to 1.5 M), similar to those that can be found in a commercial lithium-ion battery. The approach followed, based on electrochemical impedance spectroscopy measurements in symmetric cells, reveals that it is possible to obtain reliable tortuosity values at practical operating experimental conditions found in commercial lithium-ion batteries, like different temperatures and electrolyte concentrations. In this sense, the use of modified ionic resistance vs conductivity logarithmic plots provides a way to consolidate the tortuosity measurement compared to single points (c, T), for which large point to point variations have been observed. Furthermore, the comparison of the tortuosity values obtained with those obtained using a non-intercalating electrolyte, TBAPF(6) in EC:EMC, which are of the same order, attests for the validity of our approach

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future

Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity

Electrode manufacturing process strongly impacts lithium-ion battery characteristics. The electrode slurry properties and the coating parameters are among the main factors influencing the electrode heterogeneity which impacts the battery cell performance and lifetime. However, the analysis of the impact of electrode manufacturing parameters on the electrode heterogeneity is difficult to be quantified and automatized due to the large number of parameters that can be adjusted in the process. In this work, a data-driven methodology was developed for automatic assessment of the impact of parameters such as the formulation and liquid-to-solid ratio in the slurry, and the gap used for its coating on the current collector, on the electrodes heterogeneity. A dataset generated by experimental measurements was used for training and testing a Machine Learning (ML) classifier namely Gaussian Naives Bayes algorithm, for predicting if an electrode is homogeneous or heterogeneous depending on the manufacturing parameters. Lastly, through a 2D representation, the impact of the manufacturing parameters on the electrode heterogeneity was assessed in detail, paving the way towards a powerful tool for the optimization of next generation of battery electrodes