Search Results (3,383)

With the large-scale integration of Electric Vehicles (EVs) into distribution systems, the spatiotemporal uncertainty of charging loads and the interplay between user charging behavior and network operational constraints present new challenges to the safe and economical operation of the power system. To address the insufficient coordination among flexibility characterization, distributed optimization, and user-side responses, this paper proposes a closed-loop collaborative dispatch strategy. The strategy integrates flexibility aggregation, endogenous dynamic pricing, and user charging-station selection behavior. Firstly, a three-tier collaborative architecture comprising the Distribution System Operator (DSO), Electric Vehicle Aggregators (EVAs), and EV users is established, with rolling updates implemented using Model Predictive Control (MPC). A flexible aggregation model is developed based on set operations of vehicle-level constraints, dynamically calculating power boundaries and energy feasibility domains. Furthermore, a distributed coordinated optimization model between the DSO and multiple EVAs is established and solved via the Alternating Direction Method of Multipliers (ADMM) under privacy-preserving conditions. By analyzing the correlation between ADMM dual variables and the marginal value of network constraints, a Distribution Locational Marginal Pricing (DLMP) -inspired dynamic price signal—endogenous to the optimization—is constructed to guide spatial reallocation of charging loads. Joint simulations based on an IEEE 33-node distribution network and the Sioux Falls transport network demonstrate that the proposed strategy reduces 24 h network losses from 9.05 MWh (uncoordinated) to 8.41 MWh, lowers user total costs from 22,200 yuan to 7100 yuan, and eliminates voltage limit violations (duration reduced from 1.50 h to 0), while exhibiting good distributed solution performance and closed-loop control capability. Full article

Vehicle-to-Grid (V2G) enables energy and information exchange between electric vehicles (EVs) and the power grid. Large amounts of distributed data are generated in V2G systems. The charging data of EVs connected to charging piles (CPs) are aggregated by charging stations (CSs), charging service operators (COs), and load aggregation platforms. The aggregated results support grid regulation and electricity market trading. However, some CPs within a station are idle or offline due to hardware failures or communication disruptions, preventing their data from being aggregated in time. Consequently, traditional schemes that rely on full-pile participation result in incomplete ciphertext aggregation results due to the absence of data contributions from some CPs, making correct decryption unreliable. In addition, most existing data aggregation schemes only provide a single result. They cannot satisfy the differentiated data access demands of V2G business entities. To address these challenges, a user-driven differentiated fault-tolerant data aggregation scheme for V2G interaction is proposed. First, the EC-ElGamal encryption scheme is employed to preserve data confidentiality, while the ECDSA batch signature verification mechanism is adopted to ensure data integrity. Second, a mask compensation mechanism is designed to restore the completeness of the aggregated ciphertext under the failures of some CPs. Finally, on-demand differential de-aggregation and controlled authorized decryption are designed based on Shamir’s secret sharing scheme. Data users (DUs) are allowed to access target ciphertexts on demand, only upon obtaining sufficient authorization from CPs. Security and performance analyses demonstrate that the proposed scheme effectively resists chosen-plaintext and key-collusion attacks with practical efficiency. The proposed scheme provides a secure and reliable solution for V2G data aggregation. Full article

Selecting three-way catalysts (TWCs) solely from precious-metal loading or a single light-off metric may not reflect the broad operating domain encountered by plug-in hybrid electric vehicles (PHEVs). This study linked bench-scale conversion characteristics of three aged, Pt-free Pd-Rh formulations to cycle-level emissions. The formulations represented a low-loading baseline (TWC-1), a proportional increase in Pd and Rh (TWC-2), and a higher-loading Pd-rich strategy (TWC-3). Light-off, temperature–space-velocity, and λ-sweep data were incorporated into coupled vehicle, engine-out emission, catalyst thermal, and aftertreatment models for Worldwide Harmonized Light Vehicle Test Cycle (WLTC) and Real-driving Emission (RDE) evaluation. The formulation ranking varied with the test boundary. At 30,000 h⁻¹, TWC-3 exhibited the lowest CO light-off temperatures, whereas TWC-2 achieved the lowest T50 values for C₃H₆ and NO. At 50,000 h⁻¹ with its corresponding inlet composition, TWC-2 produced the lowest T50 and T90 values for all three species. Across the broader operating domain, TWC-1 deteriorated most when lower temperature coincided with higher space velocity, while the higher-loading Pd-rich strategy provided no consistent advantage for C₃H₆ or NO conversion. The WLTC emphasized light-off and intermediate-temperature activity, whereas the predominantly hot RDE profile included space velocities above 200,000 h⁻¹. Relative to TWC-3, TWC-2 reduced predicted TWC-out CO and NO_x emissions by 10.0% and 4.1% over the WLTC and by 36.4% and 18.8% over the RDE profile, respectively, while producing the lowest cycle-integrated THC emissions. These results demonstrate that the highest precious-metal loading does not necessarily provide the best cycle-level emission control. Linking formulation-specific conversion characteristics with cycle-dependent operating-domain distributions provides a more representative basis for TWC selection than a single light-off metric. Full article

To address the limited endurance of unmanned underwater vehicles (UUVs) during long-duration missions, this study proposes a foldable and retractable wave energy converter (WEC) conformally integrated with the UUV hull. A two-degrees-of-freedom heave-coupled dynamic model of the float–UUV system is established, and parameter-matching relationships are derived using complex dynamic stiffness and impedance-matching theory. A bidirectionally coupled STAR-CCM+-AMESim co-simulation framework resolves the nonlinear viscous flow field, relative motion, and PTO dynamic response in closed loop. Under regular wave conditions defined based on a representative Bohai Sea state, the effects of the transmission ratio and spring stiffness on the coupled motion and equivalent resistive load power output are systematically investigated. Under the specified wave condition, average electrical power varies unimodally with both parameters, reaching 70.8 W at a transmission ratio of 15 and a spring stiffness of 4642 N/m; the corresponding peak power is 161.2 W. The system is more sensitive to increases than decreases in transmission ratio, suggesting a value slightly below the theoretical optimum for engineering design. The instantaneous power shows an asymmetric double-peak pattern, indicating a shift in dominance between direct float-driven generation and spring-mediated energy release. Agreement between theory and co-simulation provides numerical cross-validation and offers a theoretical basis and numerical methodology for designing and optimizing WECs on mobile UUV platforms. Full article

The high penetration of renewable energy and the large-scale development of electric vehicles (EVs) have made voltage violations in distribution networks increasingly prominent. The flexible on-load tap changer (F-OLTC), combining mechanical coarse regulation and power-electronic stepless fine adjustment, offers advantages in both regulation range and speed. However, existing protection strategies fail to account for the impact of EV charging/discharging surges and the multi-physics coupling constraints of the equipment, making it difficult to coordinate voltage quality and equipment lifetime. This paper proposes an adaptive zonal cooperative protection strategy for the F-OLTC that accounts for EV charging/discharging characteristics. An integrated protection architecture is constructed that fuses EV state awareness and equipment health monitoring. A multi-feature fusion fault identification method is proposed to distinguish EV disturbances, external faults, and internal converter faults. A comprehensive margin index is established, and dynamic zoning is achieved based on feature-space clustering. A coordinated stepped and stepless adaptive voltage regulation protection is designed, with dynamic adjustment of thresholds and delays, and fast EV power support is dispatched during mechanical switching transients. Simulations on a modified IEEE 33-node system show that the proposed strategy reduces average voltage deviation by 46.2%, tap operations by 28.6%, voltage sag depth by 81%, recovery time by 70.8%, and network loss by 21.9%, effectively improving voltage quality and reducing mechanical stress on the switching devices, which is expected to contribute to extended maintenance intervals and improved equipment reliability. Full article

Sustainable urban freight logistics requires routing decisions that jointly account for operating cost, vehicle technology, low-emission-zone (LEZ) access, terrain-sensitive energy use, and battery feasibility. This study develops a policy–terrain-coupled mixed-fleet routing framework integrating LEZ exposure, system-level carbon settlement, terrain-sensitive energy consumption, electric-vehicle (EV) battery feasibility, and route-level EV/internal-combustion-engine vehicle reassignment within a unified daily total operational cost (DTOC) evaluator. An adaptive large-neighborhood search (ALNS) procedure reconstructs feasible routes, while vehicle type is re-evaluated through counterfactual comparison of the complete system objective. The main experiments use 60 enhanced Gehring–Homberger benchmark-derived scenarios and 20 independent seeds, supplemented by ablation, carbon-price, EV-fixed-cost, heuristic-weight, convergence, and customer-scale scalability analyses. The ALNS-based framework achieves the lowest mean DTOC among the tested procedures, albeit with higher runtime. Policy and terrain information alter modeled fleet composition, with topology-dependent cost effects. Lower EV fixed costs consistently increase EV share, whereas carbon-price effects vary across network structures. All runs in the additional 200–1000-customer tests were feasible, although runtime increased with problem size. London- and Madrid-informed cases are treated as archetypes rather than as real-world validation cases. These results provide a basis for scenario screening and comparative planning of policy–terrain interactions before city-specific calibration and deployment. Full article

Accurate diagnosis of micro short-circuits (MSCs) is essential for ensuring the safety of lithium-ion batteries used in electric vertical take-off and landing (eVTOL) aircraft. Unlike conventional electric vehicles, eVTOL batteries normally operate under high-rate discharge conditions, where strong polarization and rapid voltage variations are apt to mask the weak signatures of MSCs. To address this challenge, this study proposes an MSC diagnosis method based on multiscale voltage residual analysis. A battery model is first established to characterize the normal response under high-rate discharge, and the discrepancy between the measured and estimated terminal voltages is used to construct the model residual. Features describing the overall voltage evolution, residual statistical distribution, and multiscale residual fluctuations are then extracted. Specifically, the shadow region integral area and voltage–capacity slope are used to characterize the global voltage trajectory, while the residual mean and kurtosis quantify the systematic deviation and non-Gaussian fluctuation of the residual. Wavelet decomposition is further applied to capture the low- and high-frequency residual characteristics. After feature reduction, eight representative features are retained to establish the diagnostic model. Experimental validation under high-rate discharge conditions demonstrates that the proposed method can effectively identify MSCs despite interference from abnormal aging, thereby reducing the false alarms caused by feature similarity. This study provides a reliable approach for micro short-circuit diagnosis of eVTOL lithium-ion batteries under strong polarization and highly dynamic operating conditions. Full article

The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stage stochastic mixed-integer linear programming (MILP) framework for the optimal day-ahead energy management of a smart university campus. The proposed model jointly coordinates photovoltaic generation, battery energy storage systems, electric vehicle charging, HVAC operation, and demand response under uncertainties associated with solar generation, electricity demand, energy prices, and ambient temperature. Unlike previous campus energy management approaches, the proposed framework explicitly distinguishes first-stage scheduling decisions from second-stage recourse actions, enabling adaptive operation while preserving decision consistency across uncertainty scenarios. A realistic case study based on the operational characteristics of the Universidad Politécnica Salesiana campus in Ecuador is used to evaluate the proposed methodology. The results demonstrate that the coordinated stochastic scheduling strategy reduces daily operating costs by 36.37%, decreases CO₂ emissions by 42.81%, and lowers peak grid demand by 37.99% compared with conventional operation. In addition, photovoltaic self-consumption reaches 91.7%, while renewable energy utilization increases to 93.4% without compromising occupant thermal comfort. The proposed framework provides a scalable pathway toward low-carbon, resilient, and energy-efficient smart campus operation. Full article

Auxiliary heating, ventilation, and air conditioning (HVAC) systems can reduce electric vehicle (EV) driving range by over 20%, yet prevailing machine learning estimators often suffer from temporal data leakage, uninterpretable black-box structures, and lack real-time driver feedback. To address these challenges, this study presents a physics-informed decoupled machine learning framework integrated with a multi-objective Pareto Human–Machine Interface (HMI) advisory system. Powertrain traction power is estimated using a HistGradientBoosting regressor incorporating a mechanistic Vehicle Specific Power (VSP) feature, while cabin thermal dynamics are modeled via a regularized Random Forest regressor enriched with a Newtonian thermal decay function. Evaluated across an empirical 55-trip dataset using a 5-Fold GroupKFold cross-validation protocol, the traction and thermal models achieved out-of-sample accuracy of R² = 0.9869 (MAE = 0.71 kW) and R² = 0.8656 (MAE = 0.25 kW), respectively. Feature attributions were verified using SHAP analysis. An onboard Pareto optimization loop dynamically balances range extension against passenger thermal discomfort to deliver actionable driver recommendations. Multi-trip evaluation indicates that a representative 30% auxiliary load suppression yields average net energy savings of 5.21% entirely through software-driven guidance. Full article

Grid-connected electric vehicles (EVs) function as distributed loads or energy storage units. The integration of electric vehicles (EVs) into microgrids can provide various services, including ancillary services, active power control, reactive power compensation, and most importantly, frequency regulation. Electric vehicles equipped with vehicle-to-grid (V2G) technology provide frequency regulation services to compensate for the intermittent production of renewable energy and achieve load balancing. Electric vehicles operating with microgrids play a vital role in integrating renewable energy sources (RES), such as wind and solar farms. The intermittent power generation from these renewable sources can lead to significant frequency fluctuations in microgrids. The microgrid can benefit from ancillary services such as frequency regulation due to the increasing number of electric vehicles in future networks and the improved management of their charging and discharging. Simulation results indicate that the proposed frequency support strategy based on electric vehicles significantly improves the dynamic performance of the microgrid. These results confirm the effectiveness of integrating electric vehicles through the V2G concept to enhance frequency stability in isolated microgrids that rely on renewable energy. Full article

The accelerated deployment of electric vehicles requires planning tools able to quantify how much charging infrastructure can be integrated into distribution systems without violating operational constraints. This paper proposes a multi-scenario optimization framework for the siting and sizing of electric vehicle charging stations in radial distribution networks. The problem is formulated as a mixed-integer nonlinear programming model in which candidate-station slots, binary siting decisions, integer EV assignments, hourly power-flow constraints, voltage limits, thermal limits, charger power factor, and charging strategy are coordinated. The objective function combines hosting capacity maximization with active energy losses and voltage deviation terms through a scalarized formulation. Unmanaged and managed charging strategies are evaluated under weekday and weekend operating scenarios. Four adaptive population-based optimizers are analyzed under identical computational conditions: particle swarm optimization, a population-based genetic algorithm, JAYA, and the multi-verse optimizer. Monte Carlo random sampling is included separately as a non-adaptive baseline without memory or learning. The methodology is tested on a modified 33-bus distribution system using Colombian demand profiles and line-current limits. The campaign includes 720 cases and 7200 independent runs. In the 720-case stochastic campaign, the largest feasible solution serves 765 EVs, equivalent to 5.508 MW, with a minimum voltage of 0.9084 p.u. and a maximum loading of 99.83%. Statistical validation shows no significant Holm-adjusted pairwise differences among the adaptive algorithms in hosting capacity, while PSO provides the most robust feasibility behavior. Supplementary robustness analyses quantify the influence of candidate-site definition, objective scaling, voltage limits, base charging-power scale, and native-load growth. A complementary deterministic 69-bus assessment under a normalized branch-current envelope preserves the qualitative managed-versus-unmanaged trend, with feasible sequential allocations of 779 and 225 equivalent EV charging units, respectively. The proposed framework provides a reproducible basis for identifying robust EVCS locations, estimating hosting capacity, and quantifying tradeoffs among charging capacity, network losses, voltage performance, and computational effort. Full article

Precise data regarding the overall mass of the machinery and the gradient of the track are crucial for optimizing the control of monorail cranes and enhancing energy efficiency. Within the context of electric monorail cranes (EMCs), accurately estimating the total mass of the machinery and the track gradient poses a formidable challenge. This challenge arises from the strong coupling between the overall mass of the machine and the track gradient, the robustness of parameter estimation methods under varying operational conditions, and the generalizability of the algorithm to real-world operations and rail scenarios of EMCs. To address these challenges, this paper proposes a novel parameter estimation scheme that comprehensively considers the impact of parameter coupling relationships and multiple influencing factors in the transportation scenarios of EMCs under actual working conditions. First, to overcome measurement difficulties induced by strong coupling between the EMC mass and track gradient, a decoupling estimation method based on cascaded dual observers is proposed to jointly estimate the two states. Secondly, to mitigate track slope estimation errors under complex track types and diverse operating conditions, an enhanced immune optimization algorithm, integrating a Weibull function and Levy flight mechanism, in conjunction with an unscented Kalman filter (UKF), is developed. Furthermore, to achieve high-precision and stable parameter identification results, a Weibull dynamic forgetting factor is incorporated into the RLS algorithm, leading to the design of a WDFF-RLS estimator. Finally, real vehicle experiments were conducted on complex tracks at the test site to validate the accuracy and robustness of the proposed estimation method. Full article

With the rapid development of the electric vehicle (EV) industry, large-scale integration of EVs into the power grid has led to increasingly prominent problems such as low charging efficiency, intensified load fluctuations, and reduced economic benefits for users. To address these issues, an optimization model is constructed with charging time, load fluctuation, and user charging cost as the objectives, comprehensively considering uncertainties including renewable energy output, user charging behavior, and electricity price fluctuations. An uncertainty-aware multi-objective scheduling strategy based on an improved chaotic Lévy flight multi-objective particle swarm optimization (CLM-MOPSO) algorithm is proposed. Specifically, Weibull and Beta distributions are adopted to generate scenarios for wind and photovoltaic power output, while Poisson and normal distributions are used to characterize the uncertainty of user charging behavior. In addition, a stochastic electricity price process and load uncertainty sets are introduced to establish a robust optimization framework based on multi-scenario stochastic programming. On this basis, an improved CLM-MOPSO algorithm is designed, in which Tent chaotic mapping is utilized for high-quality population initialization, Lévy flight mutation is introduced to enhance the global search capability, and adaptive parameter adjustment together with an external archive mechanism is incorporated to improve the search efficiency while maintaining good convergence and diversity of the Pareto solution set. Finally, simulation studies based on real road network and power grid operation data are conducted, and the results verify the effectiveness of the proposed method. The results demonstrate that the proposed method significantly reduces charging time, mitigates load fluctuations, and lowers user charging costs, while also exhibiting strong robustness and potential for practical engineering applications. Full article

Bidirectional DC–DC converters are key interfaces in battery energy storage systems, electric vehicles, fuel cell vehicles, and DC microgrids, where transparent mathematical models are required for simulation, controller evaluation, and energy-flow analysis. This paper presents a hybrid index matrix framework for the Python-based modeling of a bidirectional buck–boost converter coupled to a first-order Thevenin battery model. In contrast to a classical state-space formulation, the index matrix is used as a label-aware model-assembly layer: component equations are aligned by explicit row and column identifiers and subsequently projected into ordered numerical matrices for solution. Charging, idle, and discharging equations are solved using a fixed-step backward-Euler procedure, and a PI current controller with duty-cycle saturation and anti-windup regulates the power-flow direction. A conventional switched ODE implementation is retained only as a software-level numerical-consistency check between two implementations of the same assumptions; it is not presented as experimental validation or as an independent physical benchmark. For the reported 60 s current profile, the model gives a current RMSE of 0.0863 A and a peak current of 4.3684 A, corresponding to 9.2094% overshoot at the idle-to-discharge transition. The power-integration balance is 1.6091 Wh input, 1.5868 Wh output, and 0.0223 Wh estimated loss under the adopted conduction-oriented loss model. The conditional one-step sensitivity matrices have a spectral radius of 1.00000 in all three modes; the unit eigenvalue is consistent with the slowly varying SOC state, while the remaining electrical eigenvalues lie inside the unit circle. These eigenvalue results are interpreted as local non-divergence diagnostics rather than proof of asymptotic closed-loop or switched-system stability. The framework provides a transparent and reproducible numerical workflow, while experimental validation, detailed switching-level loss modeling, step-size convergence, and formal closed-loop/switched-system stability analysis remain necessary future work. Full article

End-of-life (EoL) electric vehicle (EV) batteries comprise numerous interconnected components with complex topological and precedence relationships. These constraints significantly increase the difficulty of disassembly sequence planning (DSP), as feasible sequences must satisfy multiple dependency requirements. Moreover, the large number of possible disassembly alternatives creates a vast search space, making sequence optimisation susceptible to combinatorial explosion and convergence to local optima. Therefore, effective DSP requires both robust constraint-handling mechanisms to ensure sequence feasibility and efficient optimisation strategies to identify high-quality solutions. To address these challenges, this paper proposes a disassembly sequence optimisation method that integrates a hard-constraint rule base, the linear upper confidence bound (LinUCB) algorithm, and the Bees Algorithm (BA). First, a disassembly-oriented hard-constraint rule base is developed to standardise the identification of component topological relationships and precedence constraints, thereby ensuring the generation of feasible disassembly sequences. A LinUCB-based contextual adaptive operator-selection mechanism is subsequently introduced to dynamically select neighbourhood operators according to the current search state. A weighted multi-criteria evaluation function incorporating disassembly time, payment cost, and human–robot utility is integrated into the BA. Two representative EoL-EV battery case studies with different levels of structural complexity are used for validation. Across 50 independent runs, LinUCB-BA reduced the mean normalised weighted objective value by 39.01% and 28.12% relative to simplified swarm optimisation (SSO) and teaching–learning-based optimisation (TLBO), respectively, in the 27-component case, and by 7.19% and 2.33% in the 16-component case. Compared with the enhanced discrete Bees Algorithm (EDBA) ablation baseline, further reductions of 1.98% and 0.43% were achieved, together with lower run-to-run variability. These results indicate that the proposed framework is effective for the two investigated battery disassembly scenarios, while broader validation across additional battery architectures and operating conditions remains necessary. Full article