Large-scale lithium-ion battery (LiB) applications significantly promote marine transport decarbonization. However, inevitable marine transport swaying conditions markedly deteriorate the health of batteries. There are no quantitative methods to elucidate unique degradation behaviors and their unknown interdependencies. In this context, this article proposes a physics-informed battery aging model (PIBM). First, the dominant aging patterns of batteries affected by the external mechanical stress are revealed through a laboratory-scale experimental system with standard marine transport swaying conditions. Then, a universal pseudo-2-D (P2D) electrochemical model combining coupled degradation mechanisms is introduced to quantify the particle-level state degradation. Subsequently, a general data-driven strategy that respects physical causality is embedded into the modified P2D model. This strategy can approximate the finite-order partial differential equations (PDEs) and efficiently resolve the full-life Li+ diffusion dynamics. Compared with the conventional hybrid data-driven model, the proposed method quantifies the health degradation of batteries from the particle to the cell level and demonstrates more accurate and faster performance.

DC shipboard microgrids (DC-SMGs) are vulnerable to voltage instability due to uncertain load disturbances, compounded by degradation-induced variations in the power support capability of the energy storage system (ESS). To address these challenges, a novel stability metric-based model predictive control (MPC) with dynamic state-of-power (SOP) constraints for the ESS is proposed. First, a SOC–SOH-dependent offline 1-RC parameter look-up table is established from shipboard battery test data, so that $R_{0}$, $R_{p}$, and $\tau$ can be queried online to capture aging- and state-dependent battery dynamics. Second, a two-phase dual-model SOP is developed by converting terminal-voltage limits into horizon-dependent current bounds and enforcing them in the MPC as time-varying constraints, enabling precise voltage regulation and strict battery safety. Furthermore, a quantitative stability metric derived from domain-of-attraction (DOA) analysis is integrated as an adaptive voltage-tracking weight within the MPC to promote state convergence under load disturbances. Simulation results show that the proposed scheme reduces DC-bus voltage fluctuation by 78% and significantly decreases terminal-voltage and SOP-constraint violations compared with static-SOP methods.

Persistently high electricity prices in markets like the European Union and Japan significantly impede electric vehicle (EV) adoption by eroding their operational cost advantage, thus jeopardizing transportation decarbonization targets. This study proposes a novel market-based strategy: Bundling EVs with off-grid wind energy generators (WEGs), grounded in product bundling theory. We develop a multi-agent sequential game model to analyze the interactions between a profit-maximizing manufacturer and cost-minimizing consumers, incorporating the dual uncertainties of wind generation and driving demand. The model is calibrated using real-world data from France and the United States. Results demonstrate that the bundling model promotes EV adoption when the utility premium outweighs the WEG cost, particularly under high electricity prices and favorable wind conditions. Specifically, by reducing effective charging costs through self-consumption in markets such as France and the United States, deploying a spatially optimized 2 kW WEG boosts EV adoption by 7-10% and increases corporate profits by 18-30% relative to standalone sales. Moreover, this strategy aligns economic and environmental objectives, achieving substantial life-cycle emission reductions of 25-28%. Consequently, this bundling strategy mitigates adoption barriers while concurrently leveraging EVs as storage and WEGs for off-grid generation to advance zero-carbon transportation

The operation of electric transfer vehicles (ETVs) in seaports faces significant challenges from harsh environmental conditions, including salt spray, temperature variations, and wind, which accelerate battery aging and increase energy consumption. Traditional scheduling approaches often neglect these environmental stresses, leading to suboptimal battery lifespan and energy efficiency. This paper proposes a novel adaptive scheduling framework that jointly optimizes task assignment, vehicle routing, and charging strategies under realistic seaport environmental conditions. First, we quantify the impact of salt spray and temperature on battery calendar/cyclic aging through electrochemical models, and formulate wind/road slope-induced energy consumption variations. A two-stage optimization model is then developed, comprising (1) a vehicle-task matching model for cost and battery aging minimization, and (2) an environmental aware routing-charging model. To solve this complex problem, we design an integer programming-critic reinforcement learning algorithm that integrates integer programming for matching decisions and a critic network for policy evaluation. Case studies using real-world data from Yingkou Port demonstrate that the proposed framework effectively adapts to environmental conditions and reduces both battery aging and total operational costs.

Nowadays port areas have witnessed both the large-scale deployment of photovoltaic (PV) systems and the progressive electrification of logistics equipment for a sustainable future. However, when PV generation exceeds port-side energy demand, the surplus electricity injects into the main grid from the port microgrid, which brings bi-directional power flow. Meanwhile, the integration of high-power equipment such as quay cranes further intensifies power fluctuations and harmonic distortion, thereby accelerating the rise in transformer hotspot temperature and the rate of insulation aging, which ultimately jeopardizes the safe and stable operation of the power system. To address the aforementioned challenges, this study proposes an integrated crane scheduling strategy. The proposed methodology first constructs a relative aging rate model based on multi-physics field simulations, which quantitatively characterizes the coupled impacts of transformer loading rate and total harmonic distortion (THD) under bidirectional power flow conditions. Subsequently, a hyperplane projection technique is employed to establish a tractable representation of the transformer's safe operating domain, thereby facilitating the integration of aging constraints into the scheduling optimization process. Ultimately, through the coordinated adjustment of crane connection parameters such as the number of connected units, operational timing, and access nodes, simultaneous control of transformer aging and optimization of port operational costs can be achieved. Simulation results on the 14-node system of Rizhao Port prove the effectiveness of the proposed method in balancing operational economy and equipment reliability.

Information flow in monitoring and control is deeply intertwined with energy flow, and modern distribution systems have evolved into cyber-physical distribution systems (CPDSs). Under extreme contingencies, co-occurring infrastructure failures within the cyber network and distribution system can easily cause significant degradation of restorative control functionalities, necessitating cyber-physical collaborative recovery (CPCR) for the CPDS. To address this problem, we first propose a self-constrained hybrid flow (SCHF) framework that systematically integrates flexible topology, information flow, and energy flow in a consolidated mathematical formula. Then, an SCHF-based CPCR model is developed, followed by a two-stage solution strategy incorporating information criticality metrics (ICMs) decomposition to increase computational efficiency for a large-scale CPDS. Validation experiments on IEEE 33-bus and 123-bus systems demonstrate our method's effectiveness and its improvements in the load recovery ratio and computation time compared with those of other existing recovery methods.

There is a temperature gradient distribution inside the capacitor core, and classical theoretical formulas cannot quantitatively describe the influence of the nonlinear characteristics of the insulation material of the capacitor core on the electric field strength distribution. It is urgent to introduce nonlinear characteristics of the insulation medium into the optimization design of the valve side bushing, fully considering the electric thermal coupling mechanism to improve its operational reliability. To address the above issues, based on the in-depth research results of key physical mechanisms, this paper proposes a model-driven complete digital twin modeling approach for valve side casing and constructing an industrial software for the digital twin. Firstly, the characteristics of the digital twin modeling of valve side casing based on the mechanism model are systematically analyzed, and a complete structural specification for the digital twin model of valve side casing is investigated. Then, the design of the complete digital twin model of the valve side casing is designed and the full process interaction and connectivity of the digital twin modeling of the valve side casing is realized. Finally, the proposed method provides a reference for the digital twin modeling of high-value power equipment.

Due to the inherently low-inertial and weakly damped nature of shipboard microgrids, they are highly susceptible to time-varying and instantaneous shipboard loads, and the bus voltage can easily drop outside the safe operating range, increasing the risk of propulsion degradation or even loss of ship maneuverability. To address voltage issues, large-scale energy storage systems (ESSs) can be utilized to coordinate with main engines to mitigate power variations and provide emergency energy support. However, different from terrestrial applications, the operating characteristics of the lithium-ion battery are easily changed in harsh marine applications, which affects the rapid power support capability of the battery. In this paper, a novel grid-forming converter control framework based on a dynamic battery model is proposed to prevent voltage sags and deal with emergency scenarios. Firstly, based on the battery test data under swaying conditions, an accurate state coupling characteristics model of the battery is established to capture its dynamic feasible power operating range. Then, a primary grid-forming control is developed to coordinate with main engines to quickly deal with unpredictable operating risks, including instantaneous loads and generator faults. Especially, based on the observable battery dynamics, the grid-forming support capability of the battery, such as virtual inertia, can be quantified. Considering that the power supply capability of the battery significantly degrades over prolonged use, a secondary power-sharing control is further proposed to dynamically allocate power regulation commands and thereby mitigate the power deficiency. Finally, the proposed method is validated on a laboratory-scale experimental system. Compared with two representative methods, the proposed method can reduce the DC voltage sag by 54.56% at most while preventing the overload operation of the battery and increasing its useful life by 30%.

With the development of marine resources exploitation and electrified transportation, large-scale lithiumion battery (LIB) energy storage systems are increasingly used in marine electrification platforms. Unlike terrestrial environments, the complex marine environment significantly impacts the degradation pathways of LIBs, potentially accelerating their performance decline. However, the underlying impact mechanisms remain unclear. This paper introduces a marine environment adaptability testing (MEAT) laboratory for LIBs. The MEAT laboratory conducts performance tests on LIBs in simulated marine environments, including high/low temperature, swaying/vibration, and salt spray conditions. The results indicate that high/low temperature and swaying conditions accelerate the cycle aging of LIBs by 30% over 200 cycles, while specific vibration conditions may extend the battery service life by up to 15%. Additionally, salt spray conditions accelerate the self-discharge of LIBs, which is closely related to exposure time and initial state of charge (SoC). Using multi-characteristic characterization methods of the MEAT laboratory, the mechanisms by which complex marine environments affect LIB performance are explored. These findings contribute to improving the adaptability of LIB models to marine environments and provide guidance for the design of marine environment adaptability testing standards.

Converter valves in ultra-high-voltage direct current (UHVDC) converter stations continuously release megawatt-level low-grade waste heat, which is typically rejected through cooling towers, resulting in energy loss and substantial auxiliary electricity consumption for valve-hall air conditioning. This study develops a cascade waste-heat utilization system in which converter-valve waste heat is upgraded by a vapor-compression heat pump (VCHP) and subsequently used to drive a single-effect LiBr–H2O absorption refrigeration (AR) subsystem for chilled-water production. A coupled thermodynamic model is established for the cooling-water loop, VCHP cycle, AR cycle, and solution heat exchanger (SHX), with explicit temperature–pressure–concentration closure and crystallization-avoidance constraints. Under representative 24 h operating disturbances, the VCHP evaporator absorbs 3.88 MW of waste heat, the condenser delivers 5.13 MW of upgraded heat, and the compressor consumes 1.25 MW, resulting in a mean heating coefficient of performance, (COPHP), of 4.10. The AR subsystem provides 2.0 MW of chilled-water capacity with a mean COPAR of 0.74, while the corresponding cooling-only COPsys is 0.56. The strong-solution SHX outlet temperature remains within 51.3–54.9 °C, indicating crystallization-safe operation. Compared with a baseline configuration that rejects waste heat through cooling towers and satisfies cooling demand using electrically driven chillers, the proposed system improves normalized integrated performance by approximately 40% across winter, summer, and transition scenarios. The results demonstrate the thermodynamic feasibility of the proposed VCHP–AR cascade under the modeled operating conditions and indicate its potential to improve energy utilization in UHVDC converter stations.

Lithium plating degrades battery performance and raises safety concerns, and monitoring it is key to ensuring battery safety and lifespan. This paper takes the LiFePO4 pouch cell as the research object. This study investigates lithium-iron-phosphate pouch cells, controlling lithium plating through charging operations at-20 degrees C. It explores the correlation between lithium plating levels and electrochemical impedance spectroscopy (EIS) parameters, establishing a SOC-independent metric for quantifying lithium plating severity. Experiments employed four cumulative-20 degrees C low-temperature charge cycles, verifying the extent of lithium plating through capacity curves, differential capacity curve, and anode dissection analysis. EIS measurements at various SOCs extracted the 100Hz impedance modulus, cutoff impedance Zre_R0, and the P1 peak of the relaxation time distribution (DRT) as SOC-independent quantitative indicators of lithium plating severity. Based on decoupling ratio evaluation, the 100Hz impedance modulus proved the optimal metric, with its single-frequency measurement characteristics highly suitable for engineering applications. The proposed lithium plating quantification method offers advantages, including non-destructive detection, rapid response, and SOC independence, demonstrating engineering potential for online lithium deposition monitoring in battery management systems.

Transportation electrification has led to the widespread applications of lithium-ion batteries (LIBs). The long-term use of electric vehicles (EVs) leads to the possibility of exceeding the safety boundary for battery operation, such as overcharge and over-discharge. Moreover, the influence of how the operating boundary thresholds of batteries change dynamically with the environment is still insufficiently studied. This paper investigates the influence law of vibration conditions on the discharge cut-off voltages of LIBs through non-destructive analysis and disassembly analysis methods. Previous studies have shown that the lower the discharge cut-off voltage is, the more significant the battery capacity degradation becomes. However, our analysis results indicate that by changing the discharge cut-off voltage (2.5V) recommended by the manufacturer appropriately, vibration can delay the aging of the battery by optimizing the proportion of inorganic components in the solid electrolyte interface (SEI) film. This study provides a deeper understanding of the effects of vibration on the degradation mechanism of batteries under different discharge cut-off voltages and offers new insights into the assessment of battery performance and the improvement of environmental adaptability in vibration conditions.

During operation, converter valves continuously generate a large amount of waste heat, which is currently usually removed by a valve cooling system for temperature reduction; however, because the return-water temperature of converter valves is generally low, the waste heat is of insufficient grade, making it difficult to achieve efficient recovery and cascade utilization of low-grade thermal energy and thereby causing energy waste. To address this issue, this paper proposes a novel comprehensive utilization system for converter-valve waste heat based on heat pump technology. The proposed system recovers the waste heat of converter valves through a heat pump cycle, thereby achieving both heat recovery and heat-grade upgrading, and further drives an absorption system for cooling and heating, so as to realize converter-valve cooling and waste-heat utilization. Multi-scenario simulation analysis is carried out on the MATLAB simulation platform, and the results show that, compared with conventional waste-heat utilization systems, the proposed combined cooling and heating system exhibits superior overall performance, with the annual comprehensive energy efficiency ratio improved by more than 40%. In winter, the system can recover 15–20 MW of waste heat, with a heating COP higher than 4.5; in summer, it can provide 2000 kW of 7 °C chilled water while reducing the water consumption of the cooling tower by 30%. The proposed system can effectively meet the cooling and heating demands of areas such as valve halls and significantly enhance the utilization level of converter-valve waste heat.

The DC voltage of shipboard microgrids is vulnerable to drastic load variations under uncertain navigation conditions. Due to the limited regulation capability of the main engine, DC voltage sags are more critical under severe operating scenarios. Grid-forming energy storage systems (ESSs) are expected to enhance voltage stability by actively providing sufficient virtual inertia and emergency power support. However, the grid-forming support performance of ESSs may vary with their service life. To address this issue, an adaptive grid-forming ESS controller considering battery dynamics is proposed. First, based on battery cyclic aging experiments under irregular propulsion current conditions, a fractional-order battery state-coupled model is established to quantify the feasible power range of ESSs. Then, an adaptive VDC-based grid-forming controller is developed to coordinate DC voltage regulation with the time-varying grid-forming support performance, enabling adaptive power support capability while ensuring battery operation within the feasible power range. Five indices are defined to evaluate the voltage-support performance of ESSs. Finally, the proposed controller is then verified under typical main-engine-side fault and DC-distribution-side fault scenarios using a hardware-in-the-loop (HIL) platform. Compared to traditional voltage controllers, the proposed controller reduces the DC voltage sag by up to 74.53% under fault scenarios while avoiding battery overload.

DC shipboard microgrids (DC-SMGs) are vulnerable to voltage instability due to fast and uncertain load disturbances, compounded by degradation-induced variations in the power support capability of the energy storage system (ESS). To address these challenges, a novel state-aware metric-driven robust model predictive control (MPC) strategy for the ESS is proposed. Firstly, a SOC-SOP-SOH coupled model is established based on experimental data to quantify the battery’s power support capability. This capability is then introduced as a time-varying constraint in the MPC framework, enabling precise regulation and strictly enforcing battery operational safety. Secondly, an equivalent input disturbance (EID)-based MPC framework is developed to effectively estimate and compensate for external load disturbances. Furthermore, a novel quantitative stability metric, derived from Lyapunov-based domain of attraction (DOA) analysis, is proposed and integrated as an optimization objective within the MPC to drive superior asymptotic stability convergence. Finally, simulation verification demonstrates that the proposed scheme maintains DC-bus voltage deviations within 10%, reduces voltage fluctuation by 15.56% compared with benchmark controllers, and ensures healthy battery operation.