Tracking the cell temperature is critical for battery safety and cell durability. It is not feasible to equip every cell with a temperature sensor in large battery systems such as those in electric vehicles. Apart from this, temperature sensors are usually mounted on the cell surface and do not detect the core temperature, which can mean detecting an offset due to the temperature gradient. Many sensorless methods require great computational effort for solving partial differential equations or require error-prone parameterization. This paper presents a sensorless temperature estimation method for lithium ion cells using data from electrochemical impedance spectroscopy in combination with artificial neural networks (ANNs). By training an ANN with data of 28 cells and estimating the cell temperatures of eight more cells of the same cell type, the neural network (a simple feed forward ANN with only one hidden layer) was able to achieve an estimation accuracy of ΔT= 1 K (10 ∘C <T< 60 ∘C) with low computational effort. The temperature estimations were investigated for different cell types at various states of charge (SoCs) with different superimposed direct currents. Our method is easy to use and can be completely automated, since there is no significant offset in monitoring temperature. In addition, the prospect of using the above mentioned approach to estimate additional battery states such as SoC and state of health (SoH) is discussed.

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 (TFP=174°C) and the boiling point (TBP=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 (TMP=−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 (C/4) of testing. The flashpoint of the created electrolyte is TFP=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.

The energy transition is only feasible by using household or large photovoltaic powerplants. However, efficient use of photovoltaic power independently of other energy sources can only be accomplished employing batteries. The ever-growing demand for the stationary storage of volatile renewable energy poses new challenges in terms of cost, resource availability and safety. The development of Lithium-Ion Batteries (LIB) has been tremendously pushed by the mobile phone industry and the current need for high-voltage traction batteries. This path of global success is primarily based on its high energy density. Due to changing requirements, other aspects come to the fore that require a rebalancing of different technologies in the “Battery Ecosystem”. In this paper we discuss the evolution of zinc and manganese dioxide-based aqueous battery technologies and identify why recent findings in the field of the reaction mechanism and the electrolyte make rechargeable Zn-MnO2 batteries (ZMB), commonly known as so-called Zinc-Ion batteries (ZIB), competitive for stationary applications. Finally, a perspective on current challenges for practical application and concepts for future research is provided. This work is intended to classify the current state of research on ZMB and to highlight the further potential on its way to the market within the “Battery Ecosystem”, discussing key parameters such as safety, cost, cycle life, energy and power density, material abundancy, sustainability, modelling and cell/module development.

The temperature in each cell of a battery system should be monitored to correctly track aging behavior and ensure safety requirements. To eliminate the need for additional hardware components, a software based prediction model is needed to track the temperature behavior. This study looks at machine learning algorithms that learn physical behavior of non-linear systems based on sample data. Here, it is shown how to improve the prediction accuracy using a new method called “artificial feature extraction” compared to classical time series approaches. We show its effectiveness on tracking the temperature behavior of a Li-ion cell with limited training data at one defined ambient temperature. A custom measuring system was created capable of tracking the cell temperature, by installing a temperature sensor into the cell wrap instead of attaching it to the cell housing. Additionally, a custom early stopping algorithm was developed to eliminate the need for further hyperparameters. This study manifests that artificially training sub models that extract features with high accuracy aids models in predicting more complex physical behavior. On average, the prediction accuracy has been improved by △Tcell=0.01∘C for the training data and by △Tcell=0.007∘C for the validation data compared to the base model. In the field of electrical energy storage systems, this could reduce costs, increase safety and improve knowledge about the aging progress in an individual cell to sort out for second life applications.

In this paper, we investigate different current collector materials for in situ deposition of lithium using a slurry-based β-Li3PS4 electrolyte layer with a focus on transferability to industrial production. Therefore, half-cells with different current collector materials (carbon-coated aluminum, stainless steel, aluminum, nickel) are prepared and plating/stripping tests are performed. The results are compared in terms of Coulombic efficiency (CE) and overvoltages. The stainless steel current collector shows the best performance, with a mean efficiency of ηmean,SST=98%; the carbon-coated aluminum reaches ηmean,Al+C=97%. The results for pure aluminum and nickel indicate strong side reactions. In addition, an approach is tested in which a solvate ionic liquid (SIL) is added to the solid electrolyte layer. Compared to the cell setup without SIL, this cannot further increase the CE; however, a significant reduction in overvoltages is achieved.