This paper presents a reduced-order electrochemical battery model designed for the online implementation of battery control systems. The model is based on porous-electrode and concentrated-solution theory frameworks and is able to predict voltage as well as the internal electrochemical variables of a battery. The reduction of the model leads to a physics-based one-dimensional discrete-time state-space reduced-order model (ROM), which is especially beneficial for online systems. Models optimized around different operational setpoints are combined to predict cell variables over a wide range of temperatures and state of charges (SOCs) using the output-blending method. A sigma-point Kalman filter is further used to manage inaccuracies generated by the reduction process and experimental-related issues such as measurement error (noise) in the current and voltage sensors. The state-estimation accuracies are measured against a full-order model (FOM) developed in COMSOL. The whole system is able to track the internal variables of the cell, as well as the cell voltage and SOC with very high accuracy, demonstrating its suitability for an online battery control system.

The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. Existing methodologies are diverse, experimental based, model based, and the new trends of artificial intelligence. This review aims to analyze the existing methodologies and compare them, opening the spectrum to those based on artificial intelligence (AI). AI-based studies are increasing in number and have a wide variety of applications, but no classification, in-depth analysis, or comparison with existing methodologies is yet available.

Solid-state batteries represent a promising technology in the field of high-energy-density and safe storage systems. Improving the understanding of how defects form within these cells would greatly facilitate future development, which would be best served by applying nondestructive analytical tools capable of characterization of the key components and their changes during cycling and/or aging. Spatially offset Raman spectroscopy (SORS) represents a potentially useful technique, but currently there is a lack of knowledge regarding its use in this field. To fill this gap, we present an investigation into the use of simple defocused micro-SORS on systems constructed using typical components found within solid-state cells. By analyzing the constituents and the assembled system, it was possible to obtain depth profiling spectra and show that spectra may be obtained from layers which are normally obscured, demonstrating the technique’s potential for nondestructive chemical analysis of the subsurface. In this way, the results presented validate the potential of micro-SORS as a technique to develop to support future solid-state battery development, as well as the nondestructive battery analytical field.

Progress towards a decarbonized economy is dependent on the development of energy storage devices enabling the deployment of new strategies in the transport and energy sectors. Lithium-Ion Batteries (LIBs) play a key role in facilitating the shift from fossil fuels to renewables based on ubiquitous sources of decentralized energy. However, the rapidly increasing demand for batteries has generated environmental concerns regarding recycling, and raw materials depletion and exhaustion. Moreover, the level of extraction and processing of battery raw materials from local resources is almost negligible, inducing geopolitical concerns regarding potential threats to production and the stability of local manufactures. Finally, the storage and processing of hazardous wastes remains a difficult challenge in the circular economy concept. Recycling, therefore, is beneficial to the environment and has a high economic value via strengthening local supply chains. This work presents an alternative, comprehensive, and holistic review of LIB recycling technologies; from traditional metallurgy strategies to innovative, direct recycling methods and pioneering in-production and design-for-recycling approaches. It identifies new directions for making recycling efficient and techno-economically profitable and sustainable - including the vital role of robotics, artificial intelligence, and machine learning. Keywords: Lithium Ion Batteries, Recycling, Design for Recycling, In-production Recycling, Artificial Intelligence

The increasing push for electrified transportation and stationary energy storage demands that lithium-ion batteries (LIBs) perform under more aggressive charging conditions. Meeting such requirements without compromising safety remains a key challenge for the industry. One of the persistent obstacles to achieving fast safe charging is lithium plating, a degradation process that leads to capacity fading, increased internal resistance, and elevated safety risks (e.g., internal short-circuits). In this study, we combine in situ electrochemical diagnostics, specifically incremental capacity analysis (ICA), differential voltage analysis (DVA), and electrochemical impedance spectroscopy (EIS), with post-mortem characterization methods, including computed tomography (CT), scanning electron microscopy (SEM), X-ray diffraction (XRD), solid-state ⁷Li nuclear magnetic resonance (NMR), to assess lithium plating behavior under varied conditions and gas chromatography–mass spectrometry (GC-MS) to analyze the electrolyte. Commercial 18650 Cells (NCA cathode and SiOx-graphite anode) were cycled at −10 and 25 °C to induce different degradation modes, enabling a comparative analysis of lithium plating. Key electrochemical signatures (e.g., increased charge transfer resistance and accelerated solid electrolyte interface (SEI) growth) were correlated with physical evidence of lithium deposition. Notably, solid-state ⁷Li NMR detected metallic lithium only in cells aged at −10 °C confirming that low temperature operation promotes plating. Importantly, while ICA and DVA offer a practical, field-deployable solution for early detection of lithium plating in BMS applications, advanced postmortem techniques, like NMR and CT, are used for laboratory-based validation of the degradation mechanism. Our results provide a realistic pathway toward smarter, safer battery management strategies.