Batteries

This study uses electrochemical impedance spectroscopy (EIS) to investigate coupled effects of mechanical deformation depth and size on impedance responses of large-format prismatic lithium-ion batteries (LIBs). Stepwise out-of-plane deformations were applied using hemispherical impactors of two different diameters (30 mm and 180 mm), representing localized and global mechanical loading while maintaining consistent contact conditions. Cells were deformed to 25%, 50%, 75%, and 95% of the internal short-circuit deformation depth, with EIS measurements conducted at each level. Relative changes of measured impedance parameters and fitted equivalent circuit model (ECM) parameters were analyzed. Results show that localized deformation decreases charge transfer resistance ΔR₁ up to 8.0% and total impedance ΔZ up to 1.6%, indicating enhanced charge mobility due to internal structural damage. In contrast, global compression increases ohmic resistance ΔR₀ up to 2.1% and ΔZ up to 2.0%, likely due to reduced separator porosity. Phase angle ΔPhase showed opposite trends under localized and global loading, reflecting different capacitive responses. These results reveal that deformation depth and size significantly influence EIS measurements, with non-linear interactions and transition points indicative of irreversible damage. These results support the use of EIS as a non-destructive diagnostic tool for identifying mechanical damage in LIBs.

The widespread adoption of electric mobility has accelerated decarbonization in transportation applications, increasing the reliance on lithium-ion batteries (Li-IBs) in electric vehicles (EVs) and energy storage systems. To analyze battery risk under different combinations of ambient temperature, discharge C-rate, and state-of-charge (SoC) windows, this study experimentally investigates power fade (PF) and capacity fade (CF) as degradation-based risk indicators. In addition to experimental observations, degradation conditions reported in previous studies are considered to identify reliable and unreliable operating zones. Several variables, including operating temperature, current rate, and SoC, influence the short- and long-term performance of Li-IBs in EV applications and should be evaluated from a safety perspective. Under combined thermal and electrical operating conditions, battery degradation progresses, associated with reductions in usable energy and power, increased internal heat generation, and increased safety risks. Due to the nonlinear behavior of Li-IBs, conventional risk models may not always fully represent battery performance; therefore, qualitative analysis and risk assessment are employed. Aging is monitored using discharge capacity, discharge energy, power rating, internal resistance, and open-circuit voltage within the proposed framework. The experimental results show that operational risk increases under high discharge C-rates combined with low ambient temperature. Discharging at 0.2 C at 25 °C with an SoC of 80% is identified as a critical operating scenario within the investigated conditions, as it results in both CF and PF. In contrast, Li-IB safety is not significantly affected under CF conditions at 4 C and 3 C at 10 °C at the same SoC level, nor under PF conditions at 0.2 C at 10 °C with SoC levels of 80% and 50%. The multi-indicator risk assessment combines individual indicators to compare operating conditions in terms of associated safety risk. Finally, the results confirm that relying on a single performance indicator tends to underestimate degradation, while a combined multi-indicator approach provides a better representation of Li-IB performance over battery lifetime.

It is now crucial to accurately monitor the state of health (SoH) of batteries in a setting where the use of electric vehicles (EVs) and renewable energy technologies is still growing. To solve this issue and evaluate the SoH, this paper makes use of deep learning technology. The suggested method incorporates voltage, current, and temperature data, which are important indications of the SoH and can potentially be obtained directly from the battery management system (BMS). Although deep neural networks (DNNs) have previously been employed for SoH estimation, our study distinguishes itself by implementing a robust, completely configurable DNN application in MATLAB/Simulink R2019a. This design enables the adjustment of activation functions, layer depth, and neuron count to adapt to different battery aging conditions. To achieve optimal performance, numerous configurations were examined, highlighting the relevance of hyperparameter setting. Our technique avoids traditional feature engineering while providing a practical, adaptive, and accurate SoH estimate framework appropriate for real-world integration. The precision of the improved model was then verified against a Li-ion battery dataset with various discharge profiles given by the national aeronautics and space administration (NASA). The collected findings revealed that the proposed method is more accurate and robust than other regularly used models. The DNN model achieved a Mean absolute error (MAE) of 1.433% and a Coefficient of determination of 0.99998, outperforming previous methods such as CNN-BiGRU, which reported an MAE of 2.448% in a recent publication. This study demonstrates the reliable performance of the DNN in predicting the SoH of Li-ion cells.