High Flashpoint and Eco-Friendly Electrolyte Solvent for Lithium-Ion Batteries

Since Sony launched the commercial lithium-ion cell in 1991, the composition of the liquid electrolytes has changed only slightly. The electrolyte consists of highly flammable solvents and thus poses a safety risk. Solid-state ion conductors, classified as non-combustible and safe, are being researched worldwide. However, they still have a long way to go before being available for commercial cells. As an alternative, this study presents glyceryl tributyrate (GTB) as a flame retardant and eco-friendly solvent for liquid electrolytes for lithium-ion cells. The remarkably high flashpoint ($_{FP}$=174 °C) and the boiling point ($_{BP}$=287 °C) of GTB are approximately 150 K higher than that of conventional linear carbonate components, such as ethyl methyl carbonate (EMC) or diethyl carbonate (DEC). The melting point ($_{MP}$=−75 °C) is more than 100 K lower than that of ethylene carbonate (EC). A life cycle test of graphite/NCM with 1 M LiTFSI dissolved in GTB:EC (85:15 wt) achieved a Coulombic efficiency of above 99.6% and the remaining capacity resulted in 97% after 50 cycles (/4) of testing. The flashpoint of the created electrolyte is $_{FP}$=172 °C and, therefore, more than 130 K higher than that of state-of-the-art liquid electrolytes. Furthermore, no thermal runaway was observed during thermal abuse tests. Compared to the reference electrolyte LP40, the conductivity of the GTB-based is reduced, but the electrochemical stability is highly improved. GTB-based electrolytes are considered an interesting alternative for improving the thermal stability and safety of lithium-ion cells, especially in low power-density applications

Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

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

Autonomous Load Profile Recognition in Industrial DC-Link Using an Audio Search Algorithm

Industrial manufacturing plants, including machine tools, robots, and elevators, perform dynamic acceleration and braking processes. Recuperative braking results in an increased voltage in the machines' direct current (DC) links. In the case of a diode rectifier, a braking resistor turns the surplus of energy into lost heat. In contrast, active rectifiers can feed the braking energy back to the AC grid, though they are more expensive than diode rectifiers. DC link-coupled energy storage systems are one possible solution to downsize the supply infrastructure by peak shaving and to harvest braking energy. However, their control heavily depends on the applied load profiles that are not known in advance. Especially for retrofitted energy storage systems without connection to the machine control unit, load profile recognition imposes a major challenge. A self-tuning framework represents a suitable solution by covering system identification, proof of stability, control design, load profile recognition, and forecasting at the same time. This paper introduces autonomous load profile recognition in industrial DC links using an audio search algorithm. The method generates fingerprints for each measured load profile and saves them in a database. The control of the energy storage system then has to be adapted within a critical time range according to the identified load profile and constraints given by the energy storage system. Three different load profiles in four case studies validate the methodology

Machine Learning Based Lifetime Prediction of Lithium-Ion Cells

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

Erfolgsfaktoren für die Integration von Flüchtlingen

Gegenstand der vorliegenden Kurzexpertise ist die Aufarbeitung der wichtigsten Erfolgsfaktoren für die Integration von Flüchtlingen in Deutschland. Integration wird hierbei verstanden als Teilhabe in zentralen gesellschaftlichen Bereichen. Sie hängt von einer Vielzahl von Faktoren ab. Die vorliegende Studie gibt einen Überblick über individuelle und strukturelle Einflüsse, die Teilhabe fördern oder behindern. Untersucht wird dies für die Dimensionen Arbeitsmarkt, Bildung und Ausbildung, Kinder- und Jugendhilfe, politisches und kulturelles Engagement sowie Unterbringung und Wohnen

Challenges and prospects of automated disassembly of fuel cells for a circular economy

The hydrogen economy is driven by the growing share of renewable energy and electrification of the transportation sector. The essential components of a hydrogen economy are fuel cells and electrolysis systems. The scarcity of the resources to build these components and the negative environmental impact of their mining requires a circular economy. Concerning disassembly, economical, ergonomic, and safety reasons make a higher degree of automation necessary. Our work outlines the challenges and prospects on automated disassembly of fuel cell stacks. This is carried out by summarizing the state-of-the-art approaches in disassembly and conducting manual non-/destructive disassembly experiments of end-of-life fuel cell stacks. Based on that, a chemical and mechanical analysis of the fuel cell components is performed. From this, an automation potential for the disassembly processes is derived and possible disassembly process routes are modeled. Moreover, recommendations are given regarding disassembly system requirements using a morphological box

Feasible Energy Density Pushes of Li-Metal vs. Li-Ion Cells

Li-metal batteries are attracting a lot of attention nowadays. However, they are merely an attempt to enhance energy densities by employing a negative Li-metal electrode. Usually, when a Li-metal cell is charged, a certain amount of sacrificial lithium must be added, because irreversible losses per cycle add up much more unfavourably compared to conventional Li-ion cells. When liquid electrolytes instead of solid ones are used, additional electrolyte must also be added because both the lithium of the positive electrode and the liquid electrolyte are consumed during each cycle. Solid electrolytes may present a clever solution to the issue of saving sacrificial lithium and electrolyte, but their additional intrinsic weight and volume must be considered. This poses the important question of if and how much energy density can be gained in realistic scenarios if a switch from Li-ion to rechargeable Li-metal cells is anticipated. This paper calculates various scenarios assuming typical losses per cycle and reveals future e-mobility as a potential application of Li-metal cells. The paper discusses the trade-off if, considering only the push for energy density, liquid electrolytes can become a feasible option in large Li-metal batteries vs. the solid-state approach. This also includes the important aspect of cost

Implementation of Battery Digital Twin: Approach, Functionalities and Benefits

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

A Performance and Cost Overview of Selected Solid-State Electrolytes: Race between Polymer Electrolytes and Inorganic Sulfide Electrolytes

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

Cycling of Double-Layered Graphite Anodes in Pouch-Cells

Incremental improvement to the current state-of-the-art lithium-ion technology, for example regarding the physical or electrochemical design, can bridge the gap until the next generation of cells are ready to take Li-ions place. Previously designed two-layered porosity-graded graphite anodes, together with LixNi0.6Mn0.2Co0.2O2 cathodes, were analysed in small pouch-cells with a capacity of around 1 Ah. For comparison, custom-made reference cells with the average properties of two-layered anodes were tested. Ten cells of each type were examined in total. Each cell pair, consisting of one double-layer and one single-layer (reference) cell, underwent the same test procedure. Besides regular charge and discharge cycles, electrochemical impedance spectroscopy, incremental capacity analysis, differential voltage analysis and current-pulse measurement are used to identify the differences in ageing behaviour between the two cell types. The results show similar behaviour and properties at beginning-of-life, but an astonishing improvement in capacity retention for the double-layer cells regardless of the cycling conditions. Additionally, the lifetime of the single-layer cells was strongly influenced by the cycling conditions, and the double-layer cells showed less difference in ageing behaviour