Energies

With the rapid expansion of data centers and the growing demand for cloud computing, their share in total electricity consumption has surged, making them a major high-power load in power systems. Consequently, accurately modeling their energy consumption and quantifying the feasible region have become critical research challenge. Existing studies have focused on energy consumption models for single data centers and single time periods, while limited attention has been given to multi-data centers energy optimization that considers spatiotemporal workload migration. This paper presents an energy consumption model for multi-data centers that accounts for the spatiotemporal transfer flexibility of delay-tolerant workloads. By enabling task migration across data centers (spatial dimension) and workload deferral within each center (temporal dimension), the model dynamically adjusts the operational states of IT equipment to minimize overall system operating costs while satisfying computational demands. To address the computational challenges caused by the large number of integer variables, the sliding window method and equipment aggregation method are employed to ensure the model can be efficiently solved. To further capture the flexibility of data center energy consumption, a method for computing the energy consumption elasticity space is proposed based on multi-parametric programming. This elasticity space characterizes the feasible range of energy consumption under operational constraints and provides boundary conditions for power system dispatch optimization. Simulation studies using real operational data from a large-scale Internet enterprise show that the proposed model reduces the total operational cost by approximately 3.4% compared to the baseline model without flexibility, decreases the frequency of IT equipment state transitions, and enhances the flexibility of data centers in supporting power system supply-demand balance and renewable energy integration.

Transportation is the highest emitting sector in the US, and electrifying transportation is an effective way to reduce emissions. However, electrification efforts have typically focused on battery electric vehicles (BEVs); but extended-range EVs (EREVs), EVs with a backup gasoline generator, could play a major role. Nonetheless, reducing transportation-related costs and carbon emissions hinges on understanding how an EREV’s range and charging profile affect electric miles driven and, by extension, emission savings. This study evaluates the distribution of vehicle miles traveled (VMT) between electric and gasoline modes for EREVs across electric range (25–150 miles) and charging frequency scenarios. Using 2023 U.S. trip data by distance and monthly VMT benchmarks, we apply a dynamic mean-distance estimation method to match observed totals and allocate VMT to EV or gasoline power based on trip length. We explore different charging, efficiency, and cost scenarios. Our results show, at current average efficiencies, that EREVs with a 50-mile range (13.7 kWh battery) could electrify 73.3% of national VMT, while 150-mile range EVs could electrify 86.8% illustrating that there are diminishing returns at higher ranges. We also compute corresponding carbon emissions savings using national fuel economy and emissions factors. Results highlight the nonlinear trade-offs between range and emissions reduction. Findings suggest that expanding the EREV range significantly boosts electrification potential up to 100 miles but offers marginal gains beyond. However, if users charge infrequently, larger range EVs are needed to maintain the benefits of vehicle electrification. Our results imply that policymakers and manufacturers should prioritize moderate range EREVs for households who frequently charge (e.g., homeowners) and long range BEVs for infrequent users (e.g., apartment dwellers).

This study presents an experimental framework for mapping the air-gap magnetic flux in electric machines operating under controlled eccentricity and tilt conditions. A six-degree-of-freedom industrial robotic arm positions the rotor, while the stator accommodates a dense single-axis Hall-sensor array. Synchronous data acquisition at 10 kHz captures magnetic-field dynamics during torque-producing excitation. A coordinate-transformation method synthesises virtual rotor poses from a limited set of physical measurements, eliminating the need for exhaustive mechanical scanning. The proposed approach generates pose-resolved RMS and THD maps, together with harmonic amplitude and phase signatures, thereby revealing localised asymmetries and phase-decoherence effects that are not predicted by idealised finite-element models. In a custom PMSM-like prototype, the local RMS value doubles (from 31 mT to 64 mT), while the THD increases by more than 25% across displacement and tilt grids. These findings provide quantitative experimental evidence of misalignment-induced magnetic-field symmetry breaking, supporting model validation and digital-twin calibration for traction, aerospace, and robotic applications.

This study undertakes a detailed computational examination of a direct refrigerant cooling approach for a 50 Ah prismatic lithium iron phosphate (LiFePO₄) battery. We conducted a systematic assessment to determine how the cooling plate’s topological layout and flow orientation influenced key performance indicators, namely thermal homogeneity, heat removal efficiency, and hydraulic pressure loss. Utilizing a validated two-phase flow model with 1,1,1,2-Tetrafluoroethane (R134a), simulations were performed on six distinct serpentine channel designs under a wide range of operating scenarios, covering variations in mass flow rate, saturation temperature, and inlet vapor quality. The simulation data revealed a strong correlation between the cooling plate’s geometric parameters and the system’s thermal behavior. In terms of uniformity, the optimized Case 6 configuration significantly outperformed Case 2, achieving a 76% improvement by narrowing the maximum mid-plane temperature difference from 2.02 °C down to 0.48 °C. A trade-off was observed regarding the mass flow rate: while higher rates lowered the peak temperature by approximately 18%, they simultaneously led to increased hydraulic pressure loss and slight non-uniformity. Similarly, decreasing the saturation temperature improved heat extraction but exacerbated flow resistance. Notably, this study identified an inlet vapor quality of 0.1 as the optimal point for maximizing temperature uniformity. These insights provide a robust theoretical foundation for optimizing the design and operation of compact direct refrigerant-based BTMSs.