Peak Power Draw Research Articles

Machine Learning Based Foreign Object Detection in Wireless Power Transfer Systems

As power electronics continue to drive advancement in electronic devices by managing the system's energy flow, it is advantageous to examine all areas of energy transfer. Wireless power transfer is one section seeing increased adoption in consumer, industrial, and healthcare applications. This consequently increases the concern for safety, reliability, and efficiency. Power transmission to any unintended load is a primary safety issue in high-power wireless power transfer applications, such as unmanned aircraft systems. Foreign object detection is utilized to selectively transmit power to only the intended target to mitigate this issue. This paper seeks to demonstrate a novel alternative method of using machine learning and artificial intelligence techniques for performing foreign object detection solely on the transmitter portion of the system by only monitoring the TX coil current. This method is first evaluated in SPICE simulation software. Then by transitioning to a closed-loop hardware-based testbed designed to be deployed in unmanned aircraft systems. During the test process for the novel FOD method, the aggregate detection accuracy achieved was 83%, including worst-case misalignment conditions with the RX coil. When the outlying worst-case conditions are excluded, the detection accuracy improves to 97%. To measure the FOD effectiveness in preventing temperature rise in a metallic object, the metallic object was placed at the peak power draw location on the transmitter coil. When the metallic object is left at this location for extended periods, the novel FOD method lowers the maximum temperature reached by 86.8°C when compared to no FOD being active. The primary advantage to this novel method is the simplistic hardware additions, which only require minimal alterations to a WPTS without any FOD implemented. Along with this, implementing this foreign object detection method in the transmitter would mitigate the need for a direct communication link between the receiver and transmitter. As a result, the entire system complexity would decrease, thus increasing the UAS power density while maintaining a safe, effective, and reliable wireless power transmission method.

Grid-Sim: Simulating Electric Fleet Charging with Renewable Generation and Battery Storage

The inevitable electrification of the sub-Saharan African paratransit system poses substantial threats to an already crippled electricity supply network. The integration of any electric vehicle fleet in this region will require in-depth analyses and understanding of the grid impact due to charging. This allows informative decisions for sufficient planning to be made for the required network infrastructure or the implementation of applicable ‘load-shifting’ techniques. This paper presents Grid-Sim, a software tool that enables comprehensive analysis of the grid impact implications of electrifying vehicle fleets. Grid-Sim is applied to assess the load profiles, energy demand, load-shifting techniques, and associated emissions for two charging stations serving an electrified minibus taxi fleet of 202 vehicles in Johannesburg, South Africa. It is found that the current operation patterns result in a peak grid power draw of 12 kW/taxi, grid-drawn energy of 87.4 kWh/taxi/day, and, subsequently, 93 kg CO2/taxi/day of emissions. However, when using the built-in option of including external batteries and a solar charging station, the average peak power draw reduces by 66%, and both grid-drawn energy and emissions reduce by 58%.

Transient Finite Element Simulation of a Lithium-Ion Battery Pack Thermal Management System Based on Latent Heat System Materials

Battery packs in high-intensity use run the risk of overheating, potentially inducing catastrophic thermal runaway. Thermal runaway occurs when an individual cell in a connected system fails and subsequently applies increased stress on the remaining cells, leading to cascading failures and destruction of the battery pack as a whole. One method for controlling battery temperature during usage is utilizing the battery pack casing as a latent heat system (LHS), specifically incorporating phase-change materials (PCMs). Using this method, the transient thermal response of a 84-cell, 270 V lithium-ion battery pack for an aircraft application was simulated using ANSYS FLUENT. Heat generation rates and specific heat capacity of a single cell were experimentally measured and used as input to the thermal and mechanical model. A heat generation load was applied to the entire battery pack, and natural convection film boundary conditions were applied to the exterior of the enclosure. The maximum temperature of the battery pack without any casing reached 69.8 ᵒC after 800 s of usage at a simulated peak power draw of 1013 W or 16 A. This exceeds the manufacturer’s maximum recommended operating temperature of 60 ᵒC. For other materials, we found the maximum temperature of the batteries reached 57.0 ᵒC with plastic casing and 45.0 ᵒC with aluminum casing. We present a design of the pack that incorporates a passive thermal management system using a composite material consisting of phase-changing material, Puretemp60®, mixed with expanded graphite (EG). Simulations of the battery pack showed a decrease in the maximum temperature with the Puretemp60® as the sole material reaching a maximum of 54.2 ᵒC and the Puretemp EG composite matrix having a maximum temperature of 51.1 ᵒC, both after 800 s at the same power draw.The optimal material combination was then determined through evaluating several PCM and EG composites in several ratios using ANSYS FLUENT, followed by experimental testing. The mixture was cost effective, easy to mix, and demonstrated good mechanical properties. Results illustrated that a mixture of 25% expanded graphite with 75% Puretemp60® (PCM) was capable of maintaining battery temperatures within the acceptable operating range under moderate load conditions and therefore preventing thermal runaway. A resulting weight reduction of about 23.5% was also achieved using the Puretemp EG composite matrix in comparison to aluminum casing, and about a 7.2% reduction compared to plastic casing.Differential scanning calorimetry (DSC) was utilized to determine the phase transformation of the composites. The addition of expanded graphite minimally affected the phase transformation temperature, while decreasing the latent heat of transformation by about 40%. Scanning electron microscope (SEM) pictures visualized the morphology of the graphite during each stage of the composite fabrication process. The Puretemp60® and expanded graphite were easily mixed and the backscattering electron images indicated the composition was homogenous.The heat generation rate of the battery was calculated using the q̇gen= mCp(dT/dt) + UA∆T. The change in temperature with respect to time was experimentally determined from a series of discharges. Heat generation in the batteries was modeled as uniformly distributed. The average of the experimentally measured heat generation rates for a 1P constant power discharge were divided by the volume of the battery to yield a curve for the average body heat flux load in W/m3 over the discharge of the battery. A polynomial curve fit to the average heat generation rates was applied as a heat load to each battery in the simulation. The heat generation due to discharge was tested with a setup where the battery was insulated in polyurethane foam in order to reduce convection heat transfer during the experiments. A polyurethane foam block was cut such that the battery could be placed in the center, along with three T-type thermocouples to measure the surface temperature and external air temperatures. Two metal cylinders, one consisting of copper and the other of 6061 aluminum, were machined to the same cylindrical dimensions as the battery. These cylinders were then used to determine the heat loss, or UA value, of the foam insulation block setup. A National instruments NI 9147, Chroma Programmable DC Power Supply, and LabView software were used for the experiments. Figure 1

Assessment of control tools for utilizing excess distributed photovoltaic generation in domestic electric water heating systems

Appliance level control and automation is an increasingly promising demand-side management tool with growing installation of advanced metering, monitoring and control infrastructure in both residential and commercial contexts. Successful implementation of appliance control and automation can alleviate network peak demand and improve distributed photovoltaic (D-PV) self-consumption to reduce its network voltage and reverse power flow impacts. Domestic electric water heating (DEWH) systems are widely deployed globally and have one of the highest peak power draw and overall energy consumption of household appliances. DEWH storage tanks offer large thermal energy storage capacity which can be used for shifting demand to lower demand periods. With growing D-PV deployment, they also offer the opportunity to store excess generation that would be otherwise exported to the grid. In this work, an intelligent water heating control tool (IWHC) is developed to store excess D-PV generation in DEWH storage tanks as thermal energy, according to the D-PV generation characteristics, household electricity consumption, hot water draw (HWD) patterns, and real time energy monitoring. The IWHC tool was installed and tested in nine Australian households with D-PV and DEWH systems. For performance comparison, two other commercially available control tools, timer, and diverter, were installed and tested in eleven other households with D-PV and DEWH systems. For each control tool, energy simulation models were developed, and the collected field performance data was used to validate the models. The validated simulation models were extended to a broader set of 380 Australian households with a year of D-PV, household and DEWH electricity consumption data. The results indicate that, on average, households can utilize 2.4 kWh, 1.8 kWh and 3.4 kWh of daily excess D-PV generation for water heating, using the IWHC, timer and diverter, respectively. Financial savings from the control of DEWH are highly dependent on households’ tariffs and daily HWD profiles. Under the most optimal morning dominant HWD profile scenario and with an average tariff, households can, on average, save $100, $80, and $170 per year with the IWHC, timer and diverter, respectively. However, the diverter’s superior field performance comes with higher capital cost, making IWHC the most attractive option.

Pricing EV charging service with demand charge

Pricing electric vehicle (EV) charging services is difficult when the electricity tariff includes both time-of-use energy cost and demand charge based on peak power draw. In this paper, we propose a pricing scheme that assigns a session-specific energy price to each charging session at the end of the billing period. The session price precisely captures the costs of energy, demand charge, and infrastructure congestion for which that session is responsible in that month while optimizing the trade-off between inexpensive time-of-use pricing and peak power draw. While our pricing scheme is calculated offline at the end of the billing period, we propose an online scheduling algorithm based on model predictive control to determine charging rates for each EV in real-time. We provide theoretical justification for our proposal and support it with simulations using real data collected from charging facilities at Caltech and JPL. Our simulation results suggest that the online algorithm can approximate the offline optimal reasonably well, e.g., the cost paid by the operator in the online setting is higher than the offline optimal cost by 9.2% and 6.5% at Caltech and JPL respectively. In the case of JPL, congestion rents are enough to cover this increase in costs, while at Caltech, this results in a negligible average loss of $18 per month.

SmoothOperator

With the ever growing popularity of cloud computing and web services, Internet companies are in need of increased computing capacity to serve the demand. However, power has become a major limiting factor prohibiting the growth in industry: it is often the case that no more servers can be added to datacenters without surpassing the capacity of the existing power infrastructure. In this work, we first investigate the power utilization in Facebook datacenters. We observe that the combination of provisioning for peak power usage, highly fluctuating traffic, and multi-level power delivery infrastructure leads to significant power budget fragmentation problem and inefficiently low power utilization. To address this issue, our insight is that heterogeneity of power consumption patterns among different services provides opportunities to re-shape the power profile of each power node by re-distributing services. By grouping services with asynchronous peak times under the same power node, we can reduce the peak power of each node and thus creating more power head-rooms to allow more servers hosted, achieving higher throughput. Based on this insight, we develop a workload-aware service placement framework to systematically spread the service instances with synchronous power patterns evenly under the power supply tree, greatly reducing the peak power draw at power nodes. We then leverage dynamic power profile reshaping to maximally utilize the headroom unlocked by our placement framework. Our experiments based on real production workload and power traces show that we are able to host up to 13% more machines in production, without changing the underlying power infrastructure. Utilizing the unleashed power headroom with dynamic reshaping, we achieve up to an estimated total of 15% and 11% throughput improvement for latency-critical service and batch service respectively at the same time, with up to 44% of energy slack reduction.

Design and Simulation of Passive Thermal Management System for Lithium-Ion Battery Packs on an Unmanned Ground Vehicle

The transient thermal response of a 15-cell, 48 V, lithium-ion battery pack for an unmanned ground vehicle (UGV) was simulated using ANSYS fluent. Heat generation rates and specific heat capacity of a single cell were experimentally measured and used as input to the thermal model. A heat generation load was applied to each battery, and natural convection film boundary conditions were applied to the exterior of the enclosure. The buoyancy-driven natural convection inside the enclosure was modeled along with the radiation heat transfer between internal components. The maximum temperature of the batteries reached 65.6 °C after 630 s of usage at a simulated peak power draw of 3600 W or roughly 85 A. This exceeds the manufacturer's maximum recommended operating temperature of 60 °C. We present a redesign of the pack that incorporates a passive thermal management system consisting of a composite expanded graphite (EG) matrix infiltrated with a phase-changing paraffin wax. The redesigned battery pack was similarly modeled, showing a decrease in the maximum temperature to 50.3 °C after 630 s at the same power draw. The proposed passive thermal management system kept the batteries within their recommended operating temperature range.

Cluster Energy Optimization: An Algorithmic Approach

In fact, Gartner projected global revenue for cloud computing to reach almost $150 billion by 2014. However, The 2011 market is already approx $68 billion globally. With increase in web technologies and Internet, a proportional increase in Cloud computing technologies has been cited. Cloud computing has been emerging as a flexible and powerful computational architecture to offer ubiquitous services to users. A variety of hardware and software resources are integrated together as a resource pool, the software is no longer resided in a single hardware environment, it is performed upon the schedule of the resource pool for optimized resource utilization. The optimization of energy consumption in the cloud computing environment is the question how to use various energy conservation strategies to efficiently allocate resources. The need of different resources in cloud environment is unpredictable. It is observed that load management in cloud is utmost needed in order to provide QOS. The jobs at over-loaded physical machine are shifted to under-loaded physical machine and turning the idle machine off in order to provide green cloud. For energy optimization, DVFS and Power-Nap are good strategies. As much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. In this paper, we have proposed an algorithm for energy optimization having the constraint QOS and SLA.

Benefits and limitations of tapping into stored energy for datacenters

Datacenter power consumption has a significant impact on both its recurring electricity bill (Op-ex) and one-time construction costs (Cap-ex). Existing work optimizing these costs has relied primarily on throttling devices or workload shaping, both with performance degrading implications. In this paper, we present a novel knob of energy buffer (eBuff) available in the form of UPS batteries in datacenters for this cost optimization. Intuitively, eBuff stores energy in UPS batteries during "valleys" - periods of lower demand, which can be drained during "peaks" - periods of higher demand. UPS batteries are normally used as a fail-over mechanism to transition to captive power sources upon utility failure. Furthermore, frequent discharges can cause UPS batteries to fail prematurely. We conduct detailed analysis of battery operation to figure out feasible operating regions given such battery lifetime and datacenter availability concerns. Using insights learned from this analysis, we develop peak reduction algorithms that combine the UPS battery knob with existing throttling based techniques for minimizing datacenter power costs. Using an experimental platform, we offer insights about Op-ex savings offered by eBuff for a wide range of workload peaks/valleys, UPS provisioning, and application SLA constraints. We find that eBuff can be used to realize 15-45% peak power reduction, corresponding to 6-18% savings in Op-ex across this spectrum. eBuff can also play a role in reducing Cap-ex costs by allowing tighter overbooking of power infrastructure components and we quantify the extent of such Cap-ex savings. To our knowledge, this is the first paper to exploit stored energy - typically lying untapped in the datacenter - to address the peak power draw problem.

The PowerNap Server Architecture

Data center power consumption is growing to unprecedented levels: the EPA estimates U.S. data centers will consume 100 billion kilowatt hours annually by 2011. Much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. Typical idle periods---though frequent---last seconds or less, confounding simple energy-conservation approaches. In this article, we propose PowerNap, an energy-conservation approach where the entire system transitions rapidly between a high-performance active state and a near-zero-power idle state in response to instantaneous load. Rather than requiring fine-grained power-performance states and complex load-proportional operation from individual system components, PowerNap instead calls for minimizing idle power and transition time, which are simpler optimization goals. Based on the PowerNap concept, we develop requirements and outline mechanisms to eliminate idle power waste in enterprise blade servers. Because PowerNap operates in low-efficiency regions of current blade center power supplies, we introduce the Redundant Array for Inexpensive Load Sharing (RAILS), a power provisioning approach that provides high conversion efficiency across the entire range of PowerNap’s power demands. Using utilization traces collected from enterprise-scale commercial deployments, we demonstrate that, together, PowerNap and RAILS reduce average server power consumption by 74%.

PowerNap

Data center power consumption is growing to unprecedented levels: the EPA estimates U.S. data centers will consume 100 billion kilowatt hours annually by 2011. Much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. Typical idle periods though frequent--last seconds or less, confounding simple energy-conservation approaches. In this paper, we propose PowerNap, an energy-conservation approach where the entire system transitions rapidly between a high-performance active state and a near-zero-power idle state in response to instantaneous load. Rather than requiring fine-grained power-performance states and complex load-proportional operation from each system component, PowerNap instead calls for minimizing idle power and transition time, which are simpler optimization goals. Based on the PowerNap concept, we develop requirements and outline mechanisms to eliminate idle power waste in enterprise blade servers. Because PowerNap operates in low-efficiency regions of current blade center power supplies, we introduce the Redundant Array for Inexpensive Load Sharing (RAILS), a power provisioning approach that provides high conversion efficiency across the entire range of PowerNap's power demands. Using utilization traces collected from enterprise-scale commercial deployments, we demonstrate that, together, PowerNap and RAILS reduce average server power consumption by 74%.

PowerNap

Data center power consumption is growing to unprecedented levels: the EPA estimates U.S. data centers will consume 100 billion kilowatt hours annually by 2011. Much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. Typical idle periods though frequent--last seconds or less, confounding simple energy-conservation approaches. In this paper, we propose PowerNap, an energy-conservation approach where the entire system transitions rapidly between a high-performance active state and a near-zero-power idle state in response to instantaneous load. Rather than requiring fine-grained power-performance states and complex load-proportional operation from each system component, PowerNap instead calls for minimizing idle power and transition time, which are simpler optimization goals. Based on the PowerNap concept, we develop requirements and outline mechanisms to eliminate idle power waste in enterprise blade servers. Because PowerNap operates in low-efficiency regions of current blade center power supplies, we introduce the Redundant Array for Inexpensive Load Sharing (RAILS), a power provisioning approach that provides high conversion efficiency across the entire range of PowerNap's power demands. Using utilization traces collected from enterprise-scale commercial deployments, we demonstrate that, together, PowerNap and RAILS reduce average server power consumption by 74%.