This paper presents a deep learning-based framework for the automated extraction and classification of equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data, aiming to enhance lithium-ion battery (LIB) diagnostics. EIS provides detailed insights into internal battery mechanisms, such as charge transfer, diffusion, and double-layer capacitance, by analyzing frequency-dependent impedance responses. Traditional interpretation methods are labour-intensive and limited in scalability. To address this, a one-dimensional convolutional neural network (1D-CNN) is employed to classify EIS spectra into four distinct ECM classes using features derived from real and synthetic datasets. The model architecture incorporates hierarchical convolutional layers, dropout, batch normalization, and global average pooling, achieving a classification accuracy of 95.65% on the test set. The predictions align closely with Nyquist plot characteristics of each ECM, validating the interpretability and robustness of the model.

Solid-state batteries (SSBs) are emerging as a promising alternative to conventional lithium-ion batteries due to their superior safety, energy density, and lifespan. However, understanding their thermal behavior under dynamic operating conditions is crucial for ensuring safety and performance, especially in e-mobility applications. This study presents a comparative thermal analysis of SSB and lithium nickel cobalt aluminum oxide (NCA) 21700 cells during charging under various ambient temperatures (0 °C, 25 °C, and 40 °C). Key metrics such as temperature gradients (ΔT/Δt) and differential temperature rise (ΔT) are evaluated to identify critical thermal behaviors. The results reveal that SSBs exhibit significantly higher ΔT and ΔT/Δt. While battery management systems (BMS) typically regulate absolute temperature rise (ΔT), this study highlights the importance of monitoring ΔT/Δt as a critical parameter for mitigating accelerated degradation and preventing thermal runaway. The findings contribute valuable insights toward developing robust thermal management strategies for next-generation battery systems.

Solid-state batteries (SSBs) represent one of the most promising technologies for next-generation energy storage systems, offering potential advantages in safety, energy density, and longevity compared to traditional lithium-ion batteries. However, comprehending and managing the thermal behavior of these batteries across a wide range of operating conditions is essential for ensuring their safe and reliable operation. Furthermore, gray box modeling-based state estimation and control are extremely crucial due to the higher degree of nonlinearity exhibited under dynamic operating conditions of SSBs. Gray box modeling is a fusion of equivalent circuit models and data-driven techniques, requiring battery test data and information on charging/discharging and thermal characteristics of SSBs. Therefore, this paper presents a comprehensive analysis of the thermal behavior of SSBs through laboratory experiments conducted in a controlled environment. Additionally, it investigates the thermal characteristics of SSBs under various charging and discharging conditions to assess their suitability for e-mobility applications. Moreover, it examines the thermal behavior and stability of SSBs and introduces a concept of a gray box modeling-based temperature detection scheme for an effective thermal management system. The insights gained from this study can inform the development of advanced thermal management strategies and contribute to the design of safer and more efficient solid-state battery technologies for e-mobility applications.

Controller area network (CAN) is widely used in automotive applications and has become the standard communication protocol to enable efficient communication primarily between electronic control units (ECUs) to reduce the complexity and cost of electrical wiring in automobiles through multiplexing. Towards developing the cloud-based electric vehicle battery data monitoring and digital-twinning of a battery management system (BMS), this paper introduced an online CAN data acquisition and visualization technique from an automotive grade BMS of NXP®®. Python-based CAN data processing tool is developed to process the raw data from the NXP® BMS and an open-source platform Grafana®is utilized together with the InfluxDB for visualization of the time-series data in real-time from a battery module containing 14 SAMSUNG 21700 lithium-ion battery cells. Each of those elements is implemented through the Docker container platform to become a standardized unit called a container. Besides presenting the detailed architecture of the data acquisition and visualization platform and the python-based data processing tool, this paper demonstrated the capability of the proposed architecture through examples of visualizing individual cell voltage, current, and temperature in real-time and their applications and utility in implementing cloud-based BMS.

Fast charging of lithium-ion battery (LIB) packs at low temperatures can have several effects on the performance and overall health of the battery. Repetitive fast charging at low temperatures accelerates internal resistance growth, leading to inefficient charging. Slow and inefficient chemical reactions at low temperatures result in slower charging rates and increased heat generation. Furthermore, repeated fast charging at subzero temperatures accelerates degradation processes due to increased wear on the battery, significantly reducing the cycle life of the battery. This research paper presents a series of experimental studies conducted on a 21700 Lithium-Nickel-Manganese-Cobalt-Oxide (NMC) LIB cell to investigate the temperature gradient and its impact on battery performance at a wide range of ambient temperatures (-5°C to 25°C) and charging rate (1C to 2 C). The findings highlight the highest rate of change of surface temperature and differential temperature (15°C) with a charging rate of 2 C at ambient temperature of -5°C. Moreover, a reduction in battery discharge performance is observed during low-temperature charging compared to charging at 25°C with the same charging rate. These findings are crucial for the development of health-conscious fast charging algorithms, improved thermal management techniques, and the establishment of a thermal safety framework.