The Earth’s changing climate significantly affects the built environment, including thermal comfort and energy demand. This study introduces a code pipeline and a dashboard to create and interpret future climate files (EPW format) used in building simulations, focusing on outdoor thermal comfort. The code pipeline uses the Future Weather Generator (FWG) to generate city climate forecasts. It also integrates the climatic data with thermal comfort indices like the Universal Thermal Climate Index (UTCI) and Discomfort Index (DI). Then, a dashboard is used to assess the country’s outputs and provide a comprehensive tool for researchers, policymakers, and practitioners to assess and mitigate climate change impacts on buildings and cities, ensuring accurate inputs for thermal comfort and energy efficiency modelling. A case study was carried out in Brazil, simulating all 578 available EPW files. Predictions indicate an overall increase in dry bulb temperature and changes in the country’s relative humidity, radiation, and wind speed. Given the specific changes in different bioclimatic zones, a tailored tool is essential for understanding local conditions. The key benefit is the creation of up-to-date validated weather files crucial for climate-adaptive planning.

Digital Twins in battery cell manufacturing require accurate descriptions of the process characteristics. The Washburn equation can estimate the wetting of the jelly roll after electrolyte filling. This paper deals with the potential of a stepwise implementation of a Digital Twin in the wetting step under different process framework conditions. The modelling considers product and process deviations during the manufacturing process and optimises the production scenarios concerning the required processing time and process energy. This approach promotes transparency in the planning and operation of wetting processes in battery cell manufacturing.

In the layout of battery cell manufacturing, the formation process is a cost and area intensive process step. Different process parameters significantly influence the machine utilization, the energy flow, and the output of the cell manufacturing. This usually leads to non-optimally sized and operated formation lines. Therefore, a basic and optimal layout, as well as an optimized production flow, is presented here. The process optimizations of the cell finishing process are carried out using discrete event simulation. In addition to the process parameters, the process costs, the process energy, as well as regulatory framework conditions are also considered. This approach promotes transparency and reliability in the planning and operation of future formation processes in battery cell manufacturing.

Handbook on Smart Battery Cell Manufacturing, pp. xxxv-xlviii (2022) No AccessIntroduction: Toward Sustainable, High-Quality Battery Cell ManufacturingMax Weeber, Julian Grimm, Sabri Baazouzi, Michael Oberle, and Kai Peter BirkeMax WeeberFraunhofer Institute for Manufacturing Engineering and Automation IPA Center for Battery Cell Manufacturing, Nobelstraße 12, 70567 Stuttgart, Germany, Julian GrimmFraunhofer Institute for Manufacturing Engineering and Automation IPA Center for Battery Cell Manufacturing, Nobelstraße 12, 70567 Stuttgart, Germany, Sabri BaazouziFraunhofer Institute for Manufacturing Engineering and Automation IPA Center for Battery Cell Manufacturing, Nobelstraße 12, 70567 Stuttgart, Germany, Michael OberleFraunhofer Institute for Manufacturing Engineering and Automation IPA Center for Battery Cell Manufacturing, Nobelstraße 12, 70567 Stuttgart, Germany, and Kai Peter BirkeFraunhofer Institute for Manufacturing Engineering and Automation IPA Center for Battery Cell Manufacturing, Nobelstraße 12, 70567 Stuttgart, Germanyhttps://doi.org/10.1142/9789811245626_0001Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: The following sections are included: The Background The Challenge The Approach The Solution References FiguresReferencesRelatedDetails Handbook on Smart Battery Cell ManufacturingMetrics History PDF download

A sustainable energy economy implies high shares of volatile renewable energy sources and requires the use of energy storage technologies. Hydrogen is a very flexible energy carrier and can be employed as a large-scale energy storage in electric grids. This paper focuses on the integration of hydrogen production, conversion and storage options in a smart grid environment. A process current source (PCS) that functions as a rectifier for an alkaline electrolysis system and is compared to a conventional rectifier structure that is not specifically designed for dynamic operation. All components of the smart grid are scaled using a staggered algorithm that combines a pattern search algorithm and a genetic algorithm. This smart optimization tool shows high flexibility, accuracy and low computing times. The required computing time has been reduced by 56% in contrast to genetic algorithms without the pattern search method. Furthermore, the energy system optimization reduced the alkaline electrolysis below 30% of the initial scale in order to yield lower costs. Therefore, the difference in rectifier performance was reduced to a minor contribution.

The battery cell manufacturing is increasingly being digitalized to improve product quality and achieve a more transparent and efficient manufacturing process. In this paper, we apply the technology management method called "technology assessment" to the processes of electrical filling and formation of pouch cells to identify and evaluate possible digitalization measures. As a result, the digitalization measures in the process technology, logistics, quality control and energy system of the two processes are discussed and evaluated according to their potential and technology readiness level. The combination of digital twins of the battery cell with the digital factory operation is assessed as the most promising measure.

Battery cell manufacturing consists of several energy intensive processes. With the global demand for batteries rising, manufacturing companies continuously seek for new strategies to improve their competitive advantage, reduce costs and boost their environmental performance. This paper assesses high-volume battery cell manufacturing for potentials to increase energy flexibility. The flexible change of energy carriers in the drying of electrodes and the use of advanced production planning and scheduling as well as storing energy in the formation process are identified to achieve highest potentials. Results of this paper contribute to the design and operation of battery cell manufacturing plants.

The concept of Digital Twin is becoming increasingly relevant for the realization of Industry 4.0. Digital Twin has the capability to optimize the product lifecycle stages and support the industries for intelligent decision-making and cost-effective business solutions. As a trending topic, a wide range of literature and a number of implementation approaches have been designed and developed. However, most of the existing implementations do not fully meet the scope and requirements defined for a digital twin, mainly due to lack of consensus about the properties of a digital twin and its corresponding components. To fill this gap, this paper presents a toolbox for realizing a Digital Twin in domain-specific applications by enhancing the associated modelling and simulation practices. The approach is illustrated by implementing the digital twin of a battery system in a robotic minicar.

Electrolytes are key components in electrochemical storage systems, which provide an ion-transport mechanism between the cathode and anode of a cell. As battery technologies are in continuous development, there has been growing demand for more efficient, reliable and environmentally friendly materials. Solid-state lithium ion batteries (SSLIBs) are considered as next-generation energy storage systems and solid electrolytes (SEs) are the key components for these systems. Compared to liquid electrolytes, SEs are thermally stable (safer), less toxic and provide a more compact (lighter) battery design. However, the main issue is the ionic conductivity, especially at low temperatures. So far, there are two popular types of SEs: (1) inorganic solid electrolytes (InSEs) and (2) polymer electrolytes (PEs). Among InSEs, sulfide-based SEs are providing very high ionic conductivities (up to 10−2 S/cm) and they can easily compete with liquid electrolytes (LEs). On the other hand, they are much more expensive than LEs. PEs can be produced at less cost than InSEs but their conductivities are still not sufficient for higher performances. This paper reviews the most efficient SEs and compares them in terms of their performances and costs. The challenges associated with the current state-of-the-art electrolytes and their cost-reduction potentials are described.

Various studies show that electrification, integrated into a circular economy, is crucial to reach sustainable mobility solutions. In this context, the circular use of electric vehicle batteries (EVBs) is particularly relevant because of the resource intensity during manufacturing. After reaching the end-of-life phase, EVBs can be subjected to various circular economy strategies, all of which require the previous disassembly. Today, disassembly is carried out manually and represents a bottleneck process. At the same time, extremely high return volumes have been forecast for the next few years, and manual disassembly is associated with safety risks. That is why automated disassembly is identified as being a key enabler of highly efficient circularity. However, several challenges need to be addressed to ensure secure, economic, and ecological disassembly processes. One of these is ensuring that optimal disassembly strategies are determined, considering the uncertainties during disassembly. This paper introduces our design for an adaptive disassembly planner with an integrated disassembly strategy optimizer. Furthermore, we present our optimization method for obtaining optimal disassembly strategies as a combination of three decisions: (1) the optimal disassembly sequence, (2) the optimal disassembly depth, and (3) the optimal circular economy strategy at the component level. Finally, we apply the proposed method to derive optimal disassembly strategies for one selected battery system for two condition scenarios. The results show that the optimization of disassembly strategies must also be used as a tool in the design phase of battery systems to boost the disassembly automation and thus contribute to achieving profitable circular economy solutions for EVBs.

Electro-mobility is considered a key strategy to reduce GHG emissions in the transport sector and to make individual mobility more sustainable. However, the production of electric vehicles is accompanied by high environmental impacts, mainly due to the resource intensive high-voltage battery systems. Hence, a prerequisite for sustainable electro-mobility – beside the provision of renewable energy for vehicle charging – is a well-functioning and efficient circular use system of electric vehicle battery systems (EVBs). While the production of EVBs has been continuously improved in recent years with high levels of automation to reduce production costs and to increase capacities, end-of-life (EoL) treatment of EVBs is still rather simplistic with rough manual disassembling before in most cases pyro-metallurgical treatment. In this paper, we argue for the need of industrial disassembly systems to reach higher levels of circularity. In the best case, these systems are highly automated and use lifecycle information including production and use-phase data for decision support to enable optimum utilization at a module or even cell level. These pathways include both second-life concepts such as repurposing or reconditioning and high-level direct recycling of active materials. To demonstrate the advantages of an industrial disassembling in EoL battery treatment, we systematically analyze different utilization pathways and we compare state-of-the-art treatment with an advanced disassembly system. The qualitative argumentation is substantiated by quantitative stochastic simulation as well as cost and lifecycle data. We show that only with a well-functioning industrial disassembling, efficient closed-loop-supply-chains (CLSCs) for EVBs can be achieved.

The concept of Digital Twin (DT) is widely explored in literature for different application fields because it promises to reduce design time, enable design and operation optimization, improve after-sales services and reduce overall expenses. While the perceived benefits strongly encourage the use of DT, in the battery industry a consistent implementation approach and quantitative assessment of adapting a battery DT is missing. This paper is a part of an ongoing study that investigates the DT functionalities and quantifies the DT-attributes across the life cycles phases of a battery system. The critical question is whether battery DT is a practical and realistic solution to meeting the growing challenges of the battery industry, such as degradation evaluation, usage optimization, manufacturing inconsistencies or second-life application possibility. Within the scope of this paper, a consistent approach of DT implementation for battery cells is presented, and the main functions of the approach are tested on a Doyle-Fuller-Newman model. In essence, a battery DT can offer improved representation, performance estimation, and behavioral predictions based on real-world data along with the integration of battery life cycle attributes. Hence, this paper identifies the efforts for implementing a battery DT and provides the quantification attribute for future academic or industrial research.

Advanced battery cells and modules are increasingly used in a variety of applications. Tracking the state of a cell along the product life cycle and in the consecutive life cycles poses a big challenge to the current battery manufacturing industry and OEMs. This is due to diverse types of influencing factors coming from raw materials, manufacturing, usage, product integration and end of life. The goal of this paper is to develop a framework that provides the capacity to survey and assess relevant data at different stages of a battery life cycle. Here we show a data driven framework for data acquisition of relevant product life cycle information. The acquired data is then handled within a data architecture for application of effective data analytics concepts. In our results, we demonstrate how the framework can be implemented for end of life of battery management. The framework focuses on making the information from each life cycle stage available to decision makers for application of possible data analytics concepts. Such organized processing of battery life cycle data can assist in the development of new business models and improvement of existing battery technologies and services.

The trend towards electrification in the energy and transportation sector requires the build-up of new production capacities for the next generation of high-end battery cells and packs. Consistent high quality products, minimum manufacturing costs and modularity of the production system are considered central planning targets. Here we present a multi-level-simulation approach that is able to complement conventional planning targets while assessing the usage of energy within battery cell manufacturing considering machine availability and site locations. The energy assessment combines simulation models at machine and process level with a model that specifies technical building systems and the building shell. The simulation model was parameterized based on insights from scientific literature, equipment manufacturer data sheets and was checked for plausability through expert interviews. Our results show that improving the energy efficiency of the coating / drying process can reduce total energy consumption of the battery manufacturing system by 13 to 30 % depending on machine availability. Considering the location of the reference factory, Germany performed best in terms of primary energy demand, Sweden best in terms of energy related CO2 emissions and China achieved superior results in terms of energy related costs.

Till 2020 the predominant key success factors of battery development have been overwhelmingly energy density, power density, lifetime, safety, and costs per kWh. That is why there is a high expectation on energy storage systems such as lithium-air (Li-O2) and lithium-sulfur (Li-S) systems, especially for mobile applications. These systems have high theoretical specific energy densities compared to conventional Li-ion systems. If the challenges such as practical implementation, low energy efficiency, and cycle life are handled, these systems could provide an interesting energy source for EVs. However, various raw materials are increasingly under critical discussion. Though only 3 wt% of metallic lithium is present in a modern Li-ion cell, absolute high amounts of lithium demand will rise due to the fast-growing market for traction and stationary batteries. Moreover, many lithium sources are not available without compromising environmental aspects. Therefore, there is a growing focus on alternative technologies such as Na-ion and Zn-ion batteries. On a view of Na-ion batteries, especially the combination with carbons derived from food waste as negative electrodes may generate a promising overall cost structure, though energy densities are not as favorable as for Li-ion batteries. Within the scope of this work, the future potential of sodium-based batteries will be discussed in view of sustainability and abundance vs. maximization of electric performance. The major directions of cathode materials development are reviewed and the tendency towards designing high-performance systems is discussed. This paper provides an outlook on the potential of sodium-based batteries in the future battery market of mobile and stationary applications.