This study used thermal impedance spectroscopy to measure a 46 Ah high-power lithium-ion pouch cell, introducing a testing setup for automotive-sized cells to extract the relevant thermal parameters, reducing the time for thermal characterisation in the complete operational range. The results are validated by measuring the heat capacity using an easy-to-implement calorimetric measurement method. For the investigated cell at 50% state of charge and an ambient temperature of 25 °C, values for the specific heat capacity of 1.25 J/(gK) and the cross-plane thermal conductivity of 0.47 W/(mK) are obtained. For further understanding, the values were measured at different states of charge and at different ambient temperatures, showing a notable dependency only on the thermal conductivity from the temperature of −0.37%/K. Also, a comparison of the cell with a similar-sized 60 Ah high-energy cell is investigated, comparing the influence of the cell structure to the thermal behaviour of commercial cells. This observation shows about 15% higher values in heat capacity and cross-plane thermal conductivity for the high-energy cell. Consequently, the presented setup is a straightforward implementation to accurately obtain the required model parameters, which could be used prospectively for module characterisation and investigating thermal propagation through the cells.

Sodium-ion batteries (SIBs) are gaining attention in research and industry as a sustainable alternative to lithium-ion batteries (LIBs). However, the advantages of sodium over lithium in terms of accessibility, price, and environmental impact are currently not fully exploited because of inexperience in production, leading to inhomogeneities in their behavior. Using electrical (e.g., open-circuit voltage curve (OCV), electrochemical impedance spectroscopy) and non-electrical measurement methods (e.g., laser scanning microscopy, computed tomography), three widely used LIB technologies and one SIB technology, all with the same rated capacity (1500 mAh) and format (18650), are compared in this article. The study reveals significant differences, such as a 12% lower cell weight at the same rated capacity of the SIB using less windings in the jelly roll, as well as a high energy density cell configuration and a much more severe dependency of the discharge capacity on temperature, exceeding the LIBs by at least a factor of 5. Additionally, the impedance of the SIB differs due to slower ion kinetics on the electrodes, showing relevant differences in both the frequency behavior and the pulse relaxation to the LIBs. An OCV reconstruction indicates the sparsity in the available literature data and the necessity to further investigate the characteristics of the SIB to validate it as a drop-in technology on the market.

Lithium-ion batteries (LIBs) are widely used in diverse applications, ranging from portable ones to stationary ones. The appropriate handling of the immense amount of spent batteries has, therefore, become significant. Whether recycled or repurposed for second-life applications, knowing their chemistry type can lead to higher efficiency. In this paper, we propose a novel machine learning-based approach for accurate chemistry identification of the electrode materials in LIBs based on their temperature dynamics under constant current cycling using gated recurrent unit (GRU) networks. Three different chemistry types, namely lithium nickel cobalt aluminium oxide cathode with silicon-doped graphite anode (NCA-GS), nickel cobalt aluminium oxide cathode with graphite anode (NCA-G), and lithium nickel manganese cobalt oxide cathode with graphite anode (NMC-G), were examined under four conditions, 0.2 C charge, 0.2 C discharge, 1 C charge, and 1 C discharge. Experimental results showed that the unique characteristics in the surface temperature measurement during the full charge or discharge of the different chemistry types can accurately carry out the classification task in both experimental setups, where the model is trained on data under different cycling conditions separately and jointly. Furthermore, experimental results show that the proposed approach for chemistry type identification based on temperature dynamics appears to be more universal than voltage characteristics. As the proposed approach has proven to be efficient in the chemistry identification of the electrode materials LIBs in most cases, we believe it can greatly benefit the recycling and second-life application of spent LIBs in real-life applications.

The increasing use of renewable energy makes balancing generation and demand in power systems more challenging. This has led to a growing focus on battery technologies and energy storage solutions. These systems are essential for the effective integration of renewable energy into sustainable power networks. However, most experimental studies of lithium-ion batteries are limited to deep charge and discharge cycles, which do not fully represent how batteries operate in stationary applications. In these applications, batteries experience not only the main charge and discharge processes but also additional stress from current fluctuations due to the variable nature of renewable energy. These smaller cycles, known as micro-cycles, have a depth of discharge of less than 2% and occur alongside the primary charge and discharge processes. This paper presents the results of an experimental study focused on battery aging, considering micro-cycles that emulate real-world conditions for batteries in microgrid storage systems. It demonstrates that the degradation in storage systems occurs differently when compared to deep charge and discharge cycles. It provides a detailed analysis of the degradation processes occurring in microgrid batteries using EIS diagnostics and post-mortem analysis and demonstrates the applicability of the findings for renewable energy systems.