Batteries, especially lithium-ion, offer a large range of applications, including mobile consumer devices, energy storage systems, and electric vehicles. Although being a well-established technology, the complex processes and parameter interdependencies along the battery process chain are still not fully understood. In this context, the cross-sectional research topic “Digitalization & Simulation” provides the digitalization of battery cell production and recycling to achieve a knowledge-based improvement of quality as well as economic and environmentally sustainable batteries. “Digitalization & Simulation” comprehends data acquisition, storage, and management from material preparation up to formation and recycling. The acquired data is applied in the development and validation of multilevel model-based methods. With that, knowledge-based decision support is achieved, leading to improvements in the product, processes, battery cell production, and recycling.
I. Data acquisition and management
Standardization
In the BLB, data is generated along the process chain by a variety of analytical methods and test routines. It is crucial to define and implement quality control as well as measures to assure data quality and reliability. For that, best practice guidelines for frequently used procedures and processes are developed which allows the creation of a baseline to benchmark different data streams.
Automated and manual data acquisition
Production and product data are the basis of simulation and data-based approaches as well as further analyses. Within this context, requirements for data acquisition in battery cell production and strategies for automated and manual data acquisition are developed. The automated data acquisition includes sensors, programmable logic controllers (PLCs), machines, technical building services (TBS), and measurement of environmental conditions through weather stations. The manual data acquisition comprehends off-line acquired data from intermediate product analytics, final product analytics, and operational data.
Tracking & Tracing
Each individual process along the battery cell process chain produces an intermediate product whose quality is characterized by the interactions between process parameters, product structure, and production conditions. Therefore, tracking and tracing is mandatory to provide an understanding of the interdependencies between processes as well as intermediate and final products. For that, strategies and technologies are developed to track an object and its corresponding acquired production data as well as to trace this object throughout its life cycle.
Data management
Data management comprehends the storage, structuring, and maintenance of acquired data. Therefore, it is a mandatory element of digitalization strategies and the basis for models and further analyses. Based on the challenges and requirements of battery cell production and policies (e.g. battery passport), concepts for data management from production up to recycling based on new technologies (e.g. blockchain) are developed and implemented in the BLB.
II. Multilevel model-based methods
Model-based methods are developed in the BLB to digitally reproduce complex systems and investigate parameter interdependencies from molecule up to process chain levels. Moreover, these methods are an economic and timely efficient alternative to experimental investigations when studying new scenarios (e.g. materials and processes). This allows, for example, the optimization of battery performance from both a material or process chain perspective, considering their influence on the desired final product properties.
III. Decision-support and visualization
Data acquisition and management as well as multilevel model-based methods aim to increase product and process quality. Decision-support and visualization focus on presenting the gained insights to improve battery cell production and recycling. As an example, recommendations to achieve higher quality as well as economic and environmentally sustainable production are displayed based on the results of methods to identify critical parameters considering product, process, and process chain.
Contact:
Aleksandra Naumann
al.naumann(at)iwf.ing.tu-bs.de
James Fitz
james.fitz(at)tu-braunschweig.de