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.

This paper proposes a next-generation predictive framework for thermal behavior prediction of lithium-ion batteries up to 60 seconds ahead of time, leveraging advanced deep-learning framework for time series prediction. The core of this work lies in a two-stage architecture that combines the iTransformer for accurate short-term forecasting of battery current, voltage, and temperature parameters with the Kolmogorov–Arnold Network (KAN) for core temperature estimation based on the predicted battery behavior. Experimental results using real-world drive cycles and thermal data demonstrate a high-accuracy forecasting performance, with the iTransformer achieving RMSEs as low as 0.17 A for current, 0.04 V for voltage, and 0.13°C for the surface temperature at 60 seconds ahead. The KAN achieves a core temperature estimation mean absolute error (MAE) of 0.20°C at 60 seconds ahead. An R2of 0.943 shows the system’s robustness in core temperature prediction across a full battery state-of-charge profile. The proposed framework can be directly integrated into the thermal management and charging control logic for real-time, adaptive charging and thermal management systems. This paper demonstrates the potential of advanced machine learning algorithms for predictive state estimation in lithium-ion battery management to ensure safer, more efficient, and longer-lasting energy storage systems for future e-mobility applications.

The area of wireless power transfer (WPT) technology is developing rapidly and has a wide range of possible uses. In-depth reviews, comparisons, and analysis of power converters and compensation topologies designed especially for Single Input Multiple Output (SIMO) WPT systems are provided in this study. A particular focus is on evaluating compensation topologies in terms of their efficiency and output load independence. To achieve both independence and higher efficiency in SIMO WPT systems, a hybrid compensation strategy with different converters topology is suggested based on a thorough examination and analysis of the literature. This review article opens opportunities for future developments and applications in this emerging sector by offering insightful information about the state of SIMO WPT systems today.

State-of-the-art lithium-ion battery state estimation techniques, including state-of-charge, state-of-health, and remaining useful life in battery management systems (BMS), are extensively dependent on equivalent electric circuit model (ECM) parameters. ECM parameters are subject to change due to battery aging, changes in operating temperature, and other operating conditions. Therefore, updating the model parameters in real-time by on-board BMS is essential to maintain the accuracy of state estimation where ECM is used as the primary building block. ECM parameters obtained in laboratory conditions cannot guarantee accuracy in onboard BMS throughout the entire life cycle of an electric vehicle battery. Currently, onboard state estimation techniques use static ECM parameters due to complexity and computational time in parameterization and online computation, especially with higher-order ECMs. Therefore, this paper presents a comparative analysis of 2-RC, 3-RC, and 4-RC ECMs concerning parameterization time, computational cost, and accuracy, providing an understanding of the possible integration of dynamic ECM models through regular parameterization in onboard BMS. The sensitivity analysis depicts that the 3-RC model would be most appropriate for considerably high accuracy for a BMS with moderate computational power, ensuring safe and reliable state estimation throughout the lifetime of the battery. Moreover, with the changes in ambient conditions, as the sensitivity of R2and C2are much lower compared to other parameters thus, these do not need update with the changes in operating temperature.

Hydrogen and Battery Energy Storage Systems (BESS) are being widely integrated in microgrids with renewable energy storage systems. However, factors such as control strategies, capital costs, system efficiency and operation constraints have an impact on the planning of microgirds and system sizing. The effect of controller strategies, battery technologies on the system performance was studied and a sensitivity analysis has also been carried out for the system for two types of loads: single house and residential. It was found that cycle charging proved to be more effective for single house while for a residential community the load following strategy was more effetive. Also, in terms of battery chemistry Lithium Ion Nickel Managanese Cobalt Oxide (Li-Ion NMC) battery had the longest lifetime. The sensitivity analysis showed that capital cost for hydrogen electrolyzer and the solar reserve capacity had the most impact on the system sizing.

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.

Rapid printed circuit board (PCB) development for modular battery management systems (BMS) can provide a flexible, scalable, and customizable solution for all lithium-ion battery-powered devices as well as in research and development. Therefore, to satisfy the increasing demand and stringent requirements of high performance, low cost, and smaller footprints, a single-stage PCB development technique using a CO2laser, galvo scanner, and two-axis computer numerical control (CNC) router is proposed in this paper. The system can selectively cut/erode between the copper or substrate layers with only a single active laser by controlling the intensity of the laser beam. The proposed method circumvents the fabrication of a negative and the chemical processing steps of PCB manufacturing, resulting in higher production speed at a reduced cost. The technique is also environmentally friendly and suitable for small-batch production to support small industries and research. The superiority of the proposed method is also demonstrated by a comparison between the proposed and a high-density fiber laser-based method.

The increasing events of fire and catastrophic failure of lithium-ion batteries (LIBs) due to inaccurate thermal information or improper thermal management based on surface temperature data only, once again indicates the necessity of accurate core temperature information. In view of this, a rapid, more convenient but accurate thermal modeling technique for LIB is introduced in this paper using SIMBA. SIMBA is a powerful new generation power electronics simulation software powered by Python. A second-order electro-thermal model-based core temperature estimation scheme is developed in SIMBA. A wide range of battery test data is used for experimental validation of the model. Further, four standard drive cycle profiles are used to assess the impact of discharge current on the core temperature of LIB. The electro-thermal model allows estimating the core temperature from external measurements including voltage, current, and surface temperature without installing a physical core temperature sensor which is practically challenging. The proposed modeling technique is extremely convenient and the model is computationally efficient and simple enough to be implemented in practical purpose lithium-ion battery management systems.

A hybrid Fuzzy proportional-integral-derivative (Fuzzy-PID)-based control algorithm is introduced for a non-dissipative (active) charge equalization scheme consisting of a supercapacitor and two-stage bidirectional DC-DC converter. The proposed control algorithm intelligently modulates the balancing parameters while realizing the cell-to-pack-to-cell (C2P2C) balancing of a lithium-ion battery pack. The charge imbalance and charging/discharging profile are considered by the Fuzzy-logic algorithm to determine the balancing current that not only enhances the balancing speed but also ensures the operation under a safe operating region and a longer battery life. Accurate determination of cell state of charge (SOC) is highly challenging, here the proposed Fuzzy-based control algorithm eliminates the necessity of accurate SOC determination that eventually reduces the computational cost and necessity of highly precise sensors. The proposed topology requires fewer active components due to the utilization of C2P2C balancing, resulting in a simple control algorithm and lower implementation cost. The supercapacitor is used as an energy buffer to enhance the robustness and life of the balancing circuitry. Simulation studies are conducted in MATLAB-Simscape to verify the effectiveness of the proposed scheme. Further, a comparative study with the state-of-the-art is performed to demonstrate the superiority of the scheme.

This paper presents a detailed review on different architecture topologies for Electric Vehicle (EV) charging station. The proposed paper enables to have a solid reference on electric transportation charging station topologies on different levels starting from the infrastructure of common bus to types of chargers and power flow uni/bi-directionality. Besides, two detailed comparative analyses on recent proposed EV architecture topologies based multilevel converters as Cascaded H-Bridge (CHB) and Neutral Point Clamped (NPC) converters are elaborated. The presented CHB analysis tackles enhancements’ attempts on charging time, balancing batteries’ State-Of-Charge (SOC), reducing power losses and balancing capacitors’ voltages. However, the presented NPC analysis includes attempts in improving NPC’s limited voltage balance operation, balancing the bipolar DC bus, regulating load disturbances, enhancing power quality and power utilization. The two analyses are summarized in two comparison tables to sum up the aim and the enhancement of each presented attempt.