The development of lithium-ion batteries has experienced massive progress in recent years. Battery aging models are employed in advanced battery management systems (BMSs) to optimize the use of the battery and prolong its lifetime. However, Li-ion battery cells often experience fluctuations in battery capacity and performance during cycling, which makes capacity prediction more difficult. Moreover, the reason for the capacity regeneration phenomenon occurring after resting periods is not clear yet, as well as the influence of cycling conditions on capacity regeneration. A relationship between this phenomenon to cycling state of charge (SoC) ranges and current rates was investigated in this article on a battery cell with lithium nickel manganese cobalt (NMC) oxide positive electrode. Experimental results show that the capacity increase is a consequence of decreased internal impedance after the resting period. The experiments also showed that a significant power drop and subsequent power regeneration after a resting period occurs only for specific SoC ranges, and applying a resting period after battery cycling can mitigate this power fading process. The semi-empirical model of battery degradation including capacity regeneration is proposed in this article based on physical processes inside of the cell retaining low computational requirements. The acquired results can be utilized in BMSs for more accurate state of health (SoH) estimation and to prolong battery lifetime.

A fast and efficient charging infrastructure has become indispensable in the evolving energy landscape and thriving electric vehicle (EV) market. Irrespective of the charging stations’ internal alternating current (AC) or direct current (DC) bus configurations, the main concern is the exponential growth in charging demands, resulting in network congestion issues. In the context of exponential EV growth and the provision of charging facilities from low-voltage distribution networks, the distribution network may require frequent upgrades to meet the rising charging demands. To avoid network congestion problems and minimize operational expenses (OE) by integrating energy storage systems (ESS) into ultra-fast charging stations (UFCS). This paper presents a techno-economic analysis of a UFCS equipped with a battery ESS (BESS). To reduce reliance on the electric grid and minimize OE, a dual-objective optimization problem is formulated and solved via grid search and dual-simplex algorithms. Analytical energy and physical BESS models are employed to evaluate the optimization matrices. The intricacies of BESS aging are examined to ensure an optimal BESS size with a more extensive lifespan than the corresponding payback period. The integrated BESS significantly reduced reliance on the grid to tackle network congestion while fulfilling charging demands. The dynamic pricing (DP) structure has proven more favorable, as the average per unit cost remains lower than the static tariff (ST). Results illustrate that integrating BESS reduces the OE and peak-to-average ratio (PAR) by 5-to-49% and 16-to-73%, respectively. Moreover, the combination of 70% BESS and 30% grid capacities outperforms the other configurations with a 73% reduction in PAR and a 49% reduction in OE before BESS reaches the end-of-life.

Grid-following control (GFL) has been widely implemented as the dominant control method for inverter-based resources (IBR). However, because GFL cannot provide sufficient inertia for frequency regulation, grid-forming control (GFM) is proposed as an alternative solution. However, the impact of grid dynamics characteristics on GFL and GFM control is rarely discussed. Therefore, this paper systematically derives and compares the frequency response of GFL and GFM control, considering the grid dynamics that are emulated by a synchronous generator. The impact of the inertia of GFM is investigated by pole-zero maps.

Consensus algorithm-based secondary control, such as virtual impedance, is typically used to achieve power-sharing and ensure stable operation in microgrids. The introduction of communication, however, increases the system's vulnerability. Communication failure, e.g., under cyber attack or disruption, can lead to a huge loss. To solve this, this article proposes a signal reconstruction approach for the miscommunicated signals. The power-sharing convergence, both in normal conditions and under communication failure, is analyzed via the Lyapunov method. The feasibility and effectiveness of the proposed approach are validated on a lab-scale microgrid.

Distributed secondary control achieves voltage restoration and power sharing through communication among adjacent units but exposes the microgrid to potential cyber-attacks. Traditional mitigation strategies modify the secondary controller after the attack, addressing the issue only postoccurrence. Furthermore, in microgrid planning, the structure of the communication network significantly influences the resilience to attacks, but it remains to be explored. This article presents a proactive defense mechanism by designing a resilient communication network. The proposed method quantifies the impact of attacks and develops a multiobjective optimization algorithm to design the network, considering quantified attacks, convergence, time-delay robustness, and communication costs. The method is validated through OPAL-RT simulations of an islanded microgrid with ten converters.

Highly self-sufficient energy hubs offer a promising solution to mitigate grid congestion in favor of grid operators and to reduce grid fees for the benefit of energy hub operators. Meanwhile, the energy hub's capacity may far exceed the grid connection capacity, creating a weak grid situation. As a result, power quality issues such as voltage fluctuations, frequency deviations, and even instability may occur. In this work, a grid-forming energy storage system (GFM-ESS) is integrated to address these potential problems. A model of the GFM-ESS and energy hub is established based on a 50 kW PV-Hydrogen energy hub demonstrator, where PV-generated power is utilized for green hydrogen production. A trade-off design is proposed to identify the optimal balance between the capacity of the GFM-ESS and the grid connection. The voltage and frequency response at the hub's bus are analyzed to evaluate this trade-off. While experiments with the 50 kW demonstrator are ongoing, simulation results are provided to validate the effectiveness of the proposed design.

Accurate battery capacity estimation is essential for the effective and reliable operation of lithium-ion battery management systems. Battery impedance is a key parameter that encapsulates electrochemical information, closely correlating with the internal states of batteries. This study proposes a novel capacity estimation framework that effectively balances accuracy, efficiency, and practicality. Firstly, a novel feature extraction method is introduced to extract health features from the imaginary impedance at a single frequency. The extracted feature demonstrates a strong and stable correlation with battery degradation under various operation conditions, while significantly reducing data requirements. To address the impact of diverse degradation patterns on estimation accuracy, an initial adjustment method is applied to precisely retrace the relative degradation paths of batteries. The results show that the mean absolute percentage error of battery capacity estimation decreases from 15.65% to 2.87%. Additionally, a transformer-based capacity estimation model is developed, which integrates a feature fusion unit to explicitly eliminate the influence of temperature on model performance. As a result, the model's accuracy improves by over 28% under various thermal conditions.

The global race toward decarbonization has reached a transformative inflection point as electrification surges across the transportation sector in most of the world. No longer confined to passenger vehicles, electric mobility now spans trucks, ships, and even aircraft, driven by a confluence of environmental mandates, policy momentum, and technological innovation. Nowhere is this shift more pronounced than in Europe, where the Trans-European Transport Network (TEN-T) envisions a seamlessly connected, zero-emissions infrastructure backbone by the midpoint of this century (Figure 1). At the heart of this revolution lies a new breed of ultrafast-charging technologies, electrified highways, and maritime ports—each pushing the limits of energy delivery, grid integration, and power electronics. Yet, as the charging power scales from kilowatts to multimegawatts, and as electric mobility moves from concept to logistics-critical reality, the challenges to the power grid—especially at the distribution level—are becoming clearly visible. This article explores the emerging architectures and innovations required to enable this new era of electric transport, from the Megawatt Charging System (MCS) to medium-voltage (MV) grid integration with solid-state transformers (SSTs) and grid-forming (GFM) battery energy storage systems (BESSs) as key components.

Partially rated DC interlinking converters are recognized for their high-gain power regulation capabilities, which effectively synergize active power across DC microgrids (DCMGs). Integrating energy storage units (ESUs) to address the intermittent nature of renewables in DCMGs has become an enhanced requirement for these converters. This article proposes a partially rated multiport interlinking converter (PMIC) that incorporates a distributed ESU. The PMIC controls a floating voltage and a bidirectional shunt current on the DC line, ensuring full galvanic isolation for the ESU while operating at a low DC-link voltage. It regulates multidirectional power flow and balances power during peak and off-peak periods. A decentralized droop-based power flow control strategy is proposed for the PMIC, which distributes renewable energy generation, load consumption, and ESU utilization proportionally across the system. The control strategy includes two tailored continuously differentiable activation functions, Sigmoid and Hyperbolic Tangent, to facilitate autonomous global power-sharing and seamless ESU engagement. Simulation and experimental case studies confirm the PMIC's capability to smooth renewable energy fluctuations and enhance the power and voltage profiles of DCMGs.

This article provides the design procedure of a ±350 V series resonant balancing converter for bipolar DC grids. The process creates a η-ρ Pareto front to design a 3 kW converter with natural convection cooling.

Offshore wind is expected to be a major player in the global efforts toward decarbonization, leading to exceptional changes in modern power systems. Understanding the impacts and capabilities of the relatively new and uniquely positioned assets in grids with high integration levels of inverter-based resources, however, is lacking, raising concerns about grid reliability, stability, power quality, and resilience, with the absence of updated grid codes to guide the massive deployment of offshore wind. To help fill the gap, this paper presents an overview of the state-of-the-art technologies of offshore wind power grid integration. First, the paper investigates the most current grid requirements for wind power plant integration, based on a harmonized European Network of Transmission System Operators (ENTSO-E) framework and notable international standards, and it illuminates future directions. The paper discusses the wind turbine and wind power plant control strategies, and new control approaches, such as grid-forming control, are presented in detail. The paper reviews recent research on the ancillary services that offshore wind power plants can potentially provide, which, when harmonized, will not only comply with regulations but also improve the value of the asset. The paper explores topics of wind power plant harmonics, reviewing the latest standards in detail and outlining mitigation methods. The paper also presents stability analysis methods for wind power plants, with discussions centered on validity and computational efficiency. Finally, the paper discusses wind power plant transmission solutions, with a focus on high-voltage direct-current topologies and controls.

This article summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the state-of-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.

Communication-based distributed secondary control is deemed necessary to restore the state of islanding AC microgrids to set points. As its limited global information, the microgrids become vulnerable to cyber-attacks, which by falsifying the communicating singles, like the angular frequency, can disturb the power dispatch in the microgrids or even induce blackout by pushing the microgrids beyond the safe operation area and triggering the protection. To make the microgrids more cyber secure, adaptive resilient control for the secondary frequency regulation is proposed. It assumes that each converter is communicating with its adjacent converters. With the proposed control, the weight of the communication channel being attacked is automatically reduced, and the more the communicating signals are falsified, the further the weight of that communication channel is weakened. The proposed approach does not rely on attack detection and thereby is easy to implement; Besides, it still works when challenged by a combination of multi-attack signals; Moreover, it applies to multiple communication lines getting attacked cases. Finally, the effectiveness and feasibility of the proposed resilient control scheme are validated by both simulations and experimental results.

This article introduces an auxiliary power supply (APS) designed explicitly for bipolar DC grids, utilizing a flyback topology for robust and efficient operation. Central to our design is a high-voltage 3300 V silicon carbide (SiC) MOSFET and a planar transformer comprising one primary, one auxiliary, and two secondary windings. This APS uniquely addresses the resilience requirements of bipolar grids by enabling seamless switching between poles in the event of a pole failure, thus maintaining continuous power supply. Control is achieved through the UC3844A integrated circuit from Texas Instruments. The converter's effectiveness is demonstrated through experimental validations, confirming its suitability for advanced bipolar DC grid applications.

Accurately evaluating battery degradation is not only crucial for ensuring the safe and reliable operation of electric vehicles (EVs) but also fundamental for their intelligent management and maximum utilization. However, the non-linearity, non-measurability, and multi-stress coupled operating conditions have posed significant challenges for battery health prediction. This paper proposes a battery capacity estimation framework based on real-world operating data. Firstly, a comprehensive feature pool is constructed from the direct external features extracted during multi-step fast charging processes and the quantitative representation of operating conditions. Subsequently, a two-step feature engineering is introduced to select the most relevant features and eliminate the interference components. The battery capacity estimation framework is then implemented using machine learning methods. Validation results demonstrate that the proposed framework achieves superior estimation accuracy with lower computational expense compared to the modelling process without feature engineering. The MAPE and RMSE reach 1.18% and 1.98 Ah, respectively, representing reductions in errors of up to 8.53% and 11.21%. Collectively, the proposed framework paves the foundation for online health prognostics of batteries under practical operating conditions.

The communication network used in distributed sec-ondary control (DSC) for microgrid power and voltage regulation is vulnerable to cyber-attacks. Unlike the predominantly resilient research on secondary control, which tends to employ passive defense strategies, this paper presents a proactive defense mecha-nism to design a resilient network for microgrid secure operation. This proposed method involves preparing the resilient scheme before attacks occur and facilitates timely resilience during an attack. First, novel metrics are introduced to effectively quantify the impact of various cyber attacks. Then, a multiobjective optimization method is applied to design the communication graph considering the quantified attacks, convergence, time-delay robustness, and communication cost. Simulations are performed on a microgrid consisting of 10 inverter units under different scenarios to validate the effectiveness of the proposed methodology.

Balancing converters play a pivotal role in bipolar dc grids, and while numerous topologies have been explored, many suffer from drawbacks, such as reliance on bulky passive components, limited soft-switching capabilities, or complex control mechanisms. This article introduces an LC series-resonant balancing converter topology that addresses these issues, featuring smaller passive components, the ability to achieve soft switching across its entire operational range, and simplified control with proper design. By operating in the capacitive region, the converter ensures soft switching for the complete load range, enabling all switches to achieve zero voltage switching (ZVS) turn on. The study reveals that the converter's power flow depends on both the switching frequency (fsw) and the phase shift angle (φ), but notably, when the resonant inductor value is kept sufficiently low, fsw and φ become decoupled, simplifying power flow control. In addition, this article provides detailed design guidelines for the converter's resonant tank and validates the operation and control of the converter through an experimental setup.

This paper concerns the control problem of the active caused by the mismatched impedance in resistive feeders-dominated microgrids. A distributed model predictive control (DMPC) scheme is suggested to regulate the virtual impedance of each involved unit. Moreover, the proposed method benefits resilience to communication failure by designing the communication matrix. Furthermore, it involves propagating information among units in a short period, significantly reducing the communication and computation burden. Finally, the performance of the proposed control scheme is evaluated in terms of its convergence, robustness to communication delay and load variations, resilience to communication failure, and plug-and-play functionality without communication in an inverter-connected system.

Isolated bidirectional DC–DC (IBDC) converters are needed in a wide range of applications including DC microgrids, electric vehicles, and energy storage devices. Among various IBDC topologies, the dual active bridge (DAB) converter is one of the most promising solutions owing to its simple and symmetric structure, the capability of zero-voltage switching (ZVS) for all switches, and the wide voltage conversion range. This chapter presents the working principle and performance characterization of the DAB converter as well as its modeling and control. Four typical modulation schemes are introduced. Based on the single-phase shift modulation, active and reactive power flows are derived. Trade-offs among ZVS operation range, component current stress, and output power rating are analyzed, providing guidance for optimizing the inductance. In terms of control, large- and small-signal circuits of the reduced-order model are developed. The small-signal model is further improved by capturing the impacts of power losses. Two typical closed loop control strategies, output voltage feedback and output voltage feedback plus output current feedforward, are introduced and designed for an example DAB converter. Both the modeling methods and control strategies are verified by piecewise linear electrical circuit simulation (PLECS). The presented performance characterizations, large- and small-signal models, and control strategies offer practical design insights for DAB converters.

Multiport dc-dc converters have been intensively concerned and studied in power electronics-based systems. However, the instability issue and its suppression method are rarely discussed. This article investigates the stability of the triple active bridge (TAB) converter with two constant power loads by impedance-based method, where the output impedance of the TAB converter is derived by the extra element theorem. Then, a virtual impedance-based active damping (AD) control is proposed, where the virtual impedance has four different positions at the output capacitor of the dc port. The effect of the proposed AD control on output impedance and system dynamic response is analyzed. Meanwhile, the range of the virtual impedance is specified. Simulation is performed in PLECS and experimental tests are conducted on a scale-down TAB prototype to verify the effectiveness of the proposed AD control.