Thermal runaway of single cells within a large scale lithium-ion battery is a well-known risk that can lead to critical situations if no counter measures are taken in today’s lithium-ion traction batteries for battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEV) and hybrid electric vehicles (HEVs). The United Nations have published a draft global technical regulation on electric vehicle safety (GTR EVS) describing a safety feature to warn passengers in case of a thermal runaway. Fast and reliable detection of faulty cells undergoing thermal runaway within the lithium-ion battery is therefore a key factor in battery designs for comprehensive passenger safety. A set of various possible sensors has been chosen based on the determined cell thermal runaway impact. These sensors have been tested in different sized battery setups and compared with respect to their ability of fast and reliable thermal runaway detection and their feasibility for traction batteries.

Accurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations.

Battery cells are the main components of a battery system for electric vehicle batteries. Depending on the manufacturer, three different cell formats are used in the automotive sector (pouch, prismatic, and cylindrical). In the last 3 years, cylindrical cells have gained strong relevance and popularity among automotive manufacturers, mainly driven by innovative cell designs, such as the Tesla tabless design. This paper investigates 19 Li-ion cylindrical battery cells from four cell manufacturers in four formats (18650, 20700, 21700, and 4680). We aim to systematically capture the design features, such as tab design and quality parameters, such as manufacturing tolerances and generically describe cylindrical cells. We identified the basic designs and assigned example cells to them. In addition, we show a comprehensive definition of a tabless design considering the current and heat transport paths. Our findings show that the Tesla 4680 design is quasi-tabless. In addition, we found that 25% of the cathode and 30% of the anode are not notched, resulting in long electrical and thermal transport paths. Based on CT and post-mortem analyses, we show that jelly rolls can be approximated very well with the Archimedean spiral. Furthermore, we compare the gravimetric and volumetric energy density, the impedance, and the heating behavior at the surface and in the center of the jelly rolls. From the generic description, we present and discuss production processes focusing on format and design flexible manufacturing of jelly rolls.

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.

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.