Reliable onboard estimation of the State of Health of lithium-ion batteries remains challenging because battery impedance is simultaneously affected by ageing, temperature, and operating conditions. Although Passive Electrochemical Impedance Spectroscopy enables impedance estimation under dynamic operating conditions without dedicated excitation signals, the resulting impedance measurements must account for temperature effects before being exploited for diagnostic purposes. This work proposes a diagnostic framework combining passive impedance measurements, internal temperature estimation, and supervised learning for battery SoH estimation. The approach relies on a thermal model identified using Thermal Impedance Spectroscopy to estimate the internal cell temperature and separate temperature-induced impedance variations from ageing-related effects. The charge-transfer resistance extracted from passive impedance measurements is then combined with the estimated temperature and State of Charge to estimate battery SoH using a supervised neural network. Unlike previous studies, which generally address passive impedance estimation, thermal modelling, or data-driven diagnosis separately, the proposed methodology integrates these components into a unified framework. The methodology is experimentally validated on lithium-ion cells under representative dynamic driving conditions and different ageing levels. The results demonstrate that coupling PEIS with internal temperature estimation improves the robustness of impedance-based diagnostic indicators, enabling accurate SoH estimation under realistic onboard operating conditions.

Sodium-ion batteries are attracting increasing interest for stationary energy storage, yet their aging behavior and modeling remain less mature than for lithium-ion systems. This paper presents an initial experimental and modeling study of commercial 18650 sodium-ion cells (0.9 Ah) based on capacity measurements, incremental capacity analysis (ICA), differential voltage analysis (DVA), electrochemical impedance spectroscopy (EIS), and distribution of relaxation times (DRT). Cycling tests were performed under selected operating conditions, and the early aging response was monitored through repeated electrochemical characterization. The impedance spectra were interpreted using a DRT-guided equivalent circuit model (ECM), which was then used to reconstruct the cell voltage in the time domain under a representative current profile. The results show good alignment between measured and simulated voltage under moderate dynamic conditions, while also highlighting the limitations of the model under sharp current transients. In addition, the study reveals inter-cell variability and temperature-dependent degradation trends that deserve further statistical and mechanistic investigation. Overall, this work establishes a physically constrained modeling framework that links frequency-domain diagnostics to time-domain behavioral modeling and provides a basis for future state-of health prediction studies on commercial sodium-ion cells.

Fast charging is essential for enabling the widespread adoption of battery-powered systems, particularly in electric vehicles; however, it poses significant challenges due to accelerated battery degradation at high currents. Multistage constant current (MSCC) strategies are widely used in practice to mitigate this issue due to their simplicity and effectiveness. However, their conventional optimization is commonly performed on a single charging cycle, neglecting the time-varying and nonlinear nature of battery degradation. This work proposes a lifecycle-integrated optimization methodology that explicitly accounts for electrothermal-aging dynamics over the entire battery lifespan. A coupled electrothermal-aging equivalent circuit model is employed to capture capacity fade, resistance growth, and thermal behavior. Two optimization strategies are developed: (i) an adaptive period-based framework minimizing State-Of-Health (SOH) loss over successive lifecycle segments, and (ii) a full-lifecycle framework that directly maximizes Effective Full Cycles (EFC) to end-of-life. The resulting constrained multi-objective optimization problem, including charging time, energy loss, and degradation, is solved using particle swarm optimization. Results obtained on an INR18650HG2 lithium-ion cell show that the full-lifecycle approach outperforms the conventional Constant Current Constant Voltage (CCCV) charging technique and the single-cycle MSCC optimization that achieves 347.1 EFC at 25°C (18% improvement over CCCV), with relative gains reaching 66% under higher temperature conditions. The findings demonstrate that lifecycle-aware optimization significantly enhances charging strategy robustness and highlights the superiority of charge-throughput-driven formulations for fast-charging applications. From a practical perspective, extending battery lifetime while maintaining fast-charging capability can reduce operational costs and enhance the viability of electric vehicle deployment.

Metallized film capacitors are often responsible for failures in electronic systems. Predicting their lifetime to anticipate such failures is vital for assessing the reliability of these systems. This study presents accelerated aging tests involving voltage and temperature on 27 capacitors. The aim is to enhance the existing database and analyze the behavior of the resulting curves. A post-mortem analysis is carried out to evaluate the failure mechanisms. Based on this analysis, a new model is introduced. This model is evaluated against the data and compared with current laws in the literature and functions describing the experimental curves. The results confirm the model’s effectiveness, and its predictive capability is discussed.

Impedance characterization of a Lithium-ion cell is a commonly used method to assess its state of health or internal temperature. However, Electrochemical Impedance Spectroscopy (EIS) requires a dedicated excitation system and can only be performed under steady-state conditions, making it challenging to implement onboard. The objective of this work is to develop a passive EIS method, which does not require an external excitation device and allows impedance estimation even under dynamic operating conditions. This paper proposes an impedance measurement approach based on the analysis of the naturally occurring harmonic content in electric vehicle driving cycles. To achieve this, a signal processing algorithm was developed to extract and analyze these harmonics while compensating for non-stationary effects related to current, State of Charge (SoC), and temperature. The methodology is validated through experimental tests reproducing realistic operating conditions.

New-generation batteries are attracting increasing interest in response to today’s energy storage challenges, as evidenced by the steady rise in scientific publications on the topic. However, their industrial deployment remains limited due to the complexity of aging mechanisms, which are still poorly understood and difficult to control. While several promising developments have emerged in laboratory settings, they remain too immature to be scaled up. These aging processes, which directly affect the performance, safety, and lifespan of battery systems, also determine their technical and economic viability. This review offers a comparative analysis of aging phenomena—both specific to individual technologies and common across systems—drawing on findings from accelerated testing, post-mortem analyses, and modeling. It highlights critical failures such as interface instability, loss of active material, and mechanical stress, while also identifying shared patterns and the unique features of each technology. By combining experimental data with theoretical approaches, the article proposes an integrated framework for understanding and prioritizing aging mechanisms by technology type. It underscores the limitations of current characterization techniques, the urgent need for harmonized testing protocols, and the importance of standardized data sharing. Finally, it outlines possible avenues for improving the understanding and mitigation of aging phenomena.

The dataset gives a comprehensive data collection focusing on the ageing of twenty 18650 Graphite/LFP 1.1 Ah commercial cells in first and second life. The ageing from a pristine state up to 40% of capacity loss is uncommon due to the extensive testing time required. Therefore, this new data collection provides insights on long-term ageing of lithium-ion batteries. The initial aim of the study was to comprehend the ageing in first and second-life applications, with a special focus on the sudden acceleration of capacity loss that can occur after a long-term use. The ageing experiment was designed to assess the impact of various test conditions: electric vehicle use, high current charge and discharge, full or partial discharge and a reduced operating voltage window. The cells were characterised every 100 cycles to measure the remaining capacity and the pseudo-open-circuit-voltage. This dataset can help the research community in studying second-life applications or any other study on long-term ageing of Graphite/LFP batteries: new diagnosis and prognosis techniques, AI, battery modelling, and more.

The massive electrification of vehicles is a key point in the energy transition. This shift leads to various challenges, especially regarding lithium-ion batteries, as they can cause several environmental and socioeconomic impacts throughout their lifecycle. Therefore, it is essential to assess these effects in order to reduce them. This paper provides a comprehensive study of the literature regarding lithium-ion batteries sustainability (ecological, social and economic aspects) in the context of electric mobility. This paper highlights the growing number of published Life Cycle Assessment (LCA) addressing environmental impacts, while emphasizing the need for methodological harmonization at multiple levels, particularly in LCA tools and methods, as well as in the battery life cycle models considered. The lack of transparent and accessible data has been also highlighted. In the case of Social Life Cycle Assessment (SLCA), evaluating social impacts, a significant lack of studies and data have been noted. Moreover, this methodology requires further clarification and development. Life Cycle Costing (LCC), assessing economic impacts, rarely covers the entire life cycle of batteries, and its framework needs to be defined more clearly. Finally, sustainability studies, combining the three previous concepts, are rare, and the methodological frameworks and links between the three sustainability dimensions need to be clarified. This work explicitly identifies the obstacles and levers for accurately assessing the sustainability of lithium-ion batteries. In particular, recommendations are made on three major points. Firstly, the harmonization of assessment methodologies. Secondly, the need for interdisciplinary contributions to develop robust LCA models, illustrated by the example of the electrical engineering community, which can contribute to the integration of behavioural and aging models, usage scenarios, as well as account for the diversity of battery technologies. Thirdly, recommendations are made to support the development of more robust data and models, through open-science or the development of a secure data-sharing framework. Finally, all the data extracted from our study are open access.

This paper presents a comprehensive review of lithiumion battery fast-charging strategies for Battery Electric Vehicles (BEVs), focusing on key battery chemistries, and their associated aging mechanisms. The study classifies and compares key charging methods, including Constant Current Constant Voltage (CCCV), Boost Charging (BC), Constant Power Constant Voltage (CPCV), Varying Current Decay (VCD), and Multi-Stage Constant Current (MCC). It emphasizes optimized MCC, which uses adaptive algorithms to balance fast charging with battery aging. Findings indicate that optimized MCC offers the optimal trade-off in terms of speed, efficiency, and longevity, positioning it as a leading strategy for future BEV charging systems.

In order to reuse batteries, aging models must describe cell aging in both first and second life to predict the remaining useful life. However, most of the aging studies on lithium-ion batteries are focused first life experiments from 100 % to 70 % of State of Health. The lack of data is a barrier to understand the aging and its impact on long-term use. This study aims to highlight the discrepancy of aging between cells in the same test condition and the mismatch between experimental results and aging models in a long-term aging study. Experimental tests are conducted on cylindrical 18650 cells in different cases of use: electric vehicle use, fast charge, reduce voltage window, partial or complete depth of discharge. Experiments were conducted over a two-year period to study cells aging from a pristine state to the end-of-life. The aging of the negative electrode is estimated and have a strong impact on the cell aging in long term use, regardless of the test case. In high C-rate cycling or with a complete depth of discharge, the impact of the negative electrode on the cell aging is significantly higher. An empirical method is presented to take into account the electrode aging in order to exhibit the impact of the electrode on the cell. The impact of this finding in tested on existing model and then discussed.

Metallized film capacitors are a common cause of electronic system failure. Since some of their failures can lead to serious accidents and severe damage to their surroundings, such as explosions or fires, their fail-safe should be ensured independently of their intern structure. The aim of this article is to investigate the runaway process leading to these catastrophic failures. Non-destructive accelerated aging tests to prevent unusual failures are carried out on 42 capacitors. Their key characteristics are measured periodically. Runaway indicators are found and analyzed. Based on the results, knowledge about the runaway process is deepened. Furthermore, a solution appears to avoid the catastrophic failures.

Second-life applications for lithium-ion batteries offer industry opportunities to defer recycling costs, enhance economic value, and reduce environmental impacts. However, cells are affected by numerous aging phenomena which can lead to an acceleration in capacity loss. This paper uses postmortem techniques to compare aging phenomenon in 1.1 Ah 18650 graphite/LFP cells, examining the differences between a pristine cell and three cells aged to 40~30% of state of health (SoH). Macroscopic and microscopic techniques are used to identify aging phenomenon occurring in the cell on both positive and negative electrodes. Energy-dispersive X-ray spectroscopy (EDXS) and scanning electron microscopes (SEMs) with back-scattered electron (BSE) detector are used to analyze each electrode. These methods are used to analyze the morphology and the material on each electrode. The results show a stable positive LFP electrode whereas numerous deposits and cracking occurred on the negative electrode. A discussion of the appearance of those aging phenomenon is presented. Impacts for industrial cells in second-life applications are finally discussed.

Second-life applications for lithium-ion batteries offer the industry opportunities to defer recycling costs, enhance economic value, and reduce environmental impacts. An accurate prognosis of the remaining useful life (RUL) is essential for ensuring effective second-life operation. Diagnosis is a necessary step for the establishment of a reliable prognosis, based on the aging modes involved in a cell. This paper introduces a method for characterizing specific aging phenomenon in Graphite/Lithium Iron Phosphate (G/LFP) cells. This method aims to identify aging related to the loss of active material at the negative electrode (LAMNE). The identification and tracking of the state of health (SoH) are based on Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) peak-tracking techniques. The remaining capacity of the electrode is thus evaluated based on these diagnostic results, using a model derived from half-cell electrode characterization. The method is used on a G/LFP cell in the format 18650, with a nominal capacity of 1.1 Ah, aged from its pristine state to 40% of state of health.

Metallized polypropylene film capacitors are known to be one of the most common causes of failure in electronic systems. Predicting their lifetime to anticipate failures is a key issue in the assessment of these systems' reliability. In this paper, accelerated ageing tests applying voltage, temperature and humidity were conducted on 42 capacitors. The aim is to evaluate and sharpen the existing laws in literature thanks to the support of these data. Therefore, based on an analysis of failures and the understanding of this phenomenon, a new law is introduced modelling the capacitance degradation. The model has been assessed against the data, as compared to present laws in literature and other functions, describing the evolution of experimental curves.

Due to their high specific volumetric capacitance, electrolytic capacitors are used in many fields of power electronics, mainly for filtering and energy storage functions. Their characteristics change strongly with frequency, temperature and aging time. Electrolytic capacitors are among the components whose lifetime has the greatest influence on the reliability of electrical systems. Over the past three decades, many efforts in academic research have been devoted to improving reliability capacitor. Industrial applications require more reliable power electronic products. It is in this context that the different electrolytic capacitors and their characteristics are discussed. The aging process of aluminum electrolytic capacitors is explained. Finally, this paper reviews existing methods of failure prognosis of electrolytic capacitors.

With the aim of relocating consumption and production, energy management at the scale of a micro-grid seems to be a lever to improve system efficiency, lifespan and profitability. The purpose of our work is to implement an energy management policy using reinforcement learning techniques in order to maximize the profits and considering different time-varying phenomena: variable power production from a PV system, variable power consumption from a DC load and variable energy price. This work will also take into account the battery degradation as a cost to be minimized. In the near future, this application would enable a better integration of electric vehicles in the grid.