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In infrastructure-as-a-service environments, Cloud data centers employ virtualization technology to host various applications in virtual machines (VMs) and enable application isolation on shared physical resources. Additionally, live VM migration has been adopted to perform load balancing by moving VMs across distinct hosts. However, co-located VMs that show significant positive correlations on their CPU utilization patterns are at a higher risk of triggering overloading events and incurring performance degradation, even when their host is operating below its critical limits. To address this problem, a VM consolidation mechanism inspired by host-switching behaviors in symbiotic associates is proposed in this paper. In the proposed mechanism, hosts and VMs in Cloud data centers represent symbionts in an ecosystem. Two heuristic functions, inspired by host susceptibility and symbiotic coefficient among symbionts, are proposed to yield better resource utilization via VM consolidations. Experiment results demonstrate that the proposed mechanism can achieve reductions in both energy consumption and service level agreement violations of Cloud data centers.

Battery aging leads to reduction in a battery’s cycle life, which restricts the development of energy storage technology. At present, the state of health (SOH) assessment technology, which is used to indicate the battery cycle life, has been widely studied. This paper tries to find a way to adjust the battery management system adaptively in order to prolong the battery cycle life with the change of SOH. In this paper, an improved Galvanostatic Intermittent Titration Technique (GITT) method is proposed to decouple the terminal voltage into overpotential (induced by total internal resistance) and stoichiometric drift (caused by battery aging, indicated by OCV). Based on improved GITT, the open circuit voltage-temperature change (OCV-dT/dV) characteristics of SOH are described more accurately. With such an accurate description of SOH change, the adaptive method to change the discharge and charge cut-off voltage is obtained, whose application can prolong battery cycle life. Experiments verify that, in the middle of a battery’s life-cycle, the adaptive method to change the discharge and charge cut-off voltage can effectively improve the cycle life of the battery. This method can be applied during the period of preventive maintenance in battery storage systems.

Parameterized evaluation of thermal characteristics for a lithium ion battery is a challenge due to the facts: 1) heat generated by a battery is extremely hard to be measured; 2) the measured surface temperature is unable to reflect the entire thermal status. In this study, thermal energy conversion efficiency is proposed to evaluate the thermal behaviors. Results show thermal energy conversion efficiencies at 2 C are about 4.6% and 8.1% when charging and discharging, respectively, showing the former is significantly less than the later. The elevated temperature also shows the similar trend. The increased max temperatures at 2 C are about 16 K and 30 K when charging and discharging, respectively. It is found that the irreversible thermal energy is higher when discharging than when charging. The linear relations between thermal energy conversion efficiencies, and the max and min temperature are discovered, proving that temperature distribution is determined by the thermal energy conversion efficiency. The increased max and min temperatures as function of a current rate and state of charge (SOC) or depth of discharge (DOD) are estimated and plotted as three-dimensional figures. Thus, the temperature range can be known according to the ranges of SOC/DOD and current rates.

Efficient energy and temperature management techniques are essential elements for operators of cloud data centers. Dynamic virtual machine (VM) consolidation using live migration techniques presents a great opportunity for cloud service providers to adaptively reduce energy consumption and optimize their resource utilization. In recent studies, power consumption readings of individual physical hosts were chosen as the main monitoring parameters in their allocation policies, whereas very few have considered host temperature, which has shown to have a negative impact on server reliability, as a migration criterion. In this work, a thermal-aware VM consolidation mechanism is proposed for resource allocation optimization and server reliability assurance. We consider the variability in host temperature as a migration criterion to avoid outage incidents via having better VM consolidations. Extensive simulation results obtained from CloudSim show the promising performance of the proposed mechanism in energy saving while reducing the number of server outage incidents due to fluctuations in host temperature.

In order to improve volumetric power density or gravimetric power density and reduce passive component losses of the power-receiver in inductive power transfer (IPT) system, a LCCN (inductor-capacitor-capacitor-none) compensated IPT system is proposed in this paper. The secondary compensation topology is eliminated to construct a compact and light-weight power-receiver. And a design method for the open-loop IPT system to achieve not only load-independent constant current (CC) output or constant voltage (CV) output transfer characteristics but also load-independent zero phase angle (ZPA) operating conditions is investigated. With the proposed method, the VA ratings of inverter is reduced and DC-DC converter for CV output is discarded to reduce system cost and power losses. At last, a compact LCCN compensated IPT system for automated guided vehicle (AGV) was designed and system parameters according to configuration requirements was calculated. A 1.0 kW MATLAB simulation platform and experimental prototype have been implemented to demonstrate the feasibility and validity of the theoretical analysis. The efficiency of the system can reach 92.046% at CC mode and 93.675% at constant voltage CV mode.

Lithium-ion batteries (LIBs) are widely adopted in electric vehicles and electronic equipments due to their high energy density, low self-discharge rate, and long cycle life. However, LIBs suffer from capacity fade with cyclic usage. Prediction of remaining useful life (RUL) is therefore essential for ensuring the reliability and safety of LIBs. This paper proposes a bat-based particle filter (PF) algorithm using a semi-empirical model to predict RUL. The semi-empirical model is adopted as the capacity degradation model. Model parameters are then updated by the bat-based PF algorithm. The prediction results of the proposed prediction approach are compared with PF methods with different resampling algorithms. Simulation results show that the proposed prediction approach can achieve better performance in RUL prediction.

After a blackout, it is essential to restore the blackout area rapidly to minimize possible losses. In parallel restoration, the blackout area is first partitioned into several subsystems, which will then be restored in parallel to accelerate the restoration process. In order to ensure restoration reliability, each subsystem should have enough generation power and satisfy a set of constraints before triggering the parallel restoration process. This paper models this as a constrained optimization problem and proposes a partitioning strategy to solve it in three steps. In the first step, some existing methods and expert knowledge are used for initialization of the partitioning process. The second step ensures the satisfaction of modeled constraints. The third step operates greedily to find suitable partitions for parallel restoration. The proposed strategy is implemented and evaluated on IEEE 39- and 118-bus power systems. Evaluation results show that it provides adequate subsystems for parallel restoration. Unlike some existing partitioning strategies, the proposed strategy can be used to partition a power system into multiple subsystems in a single execution.

Compared with the equivalent circuit models or other empirical models, a physics-based model has advantages of accurate and elaborate, and thus becomes a potential candidate used to estimate states of Li-ion batteries in a battery management systems (BMS). The traditional pseudo-two-dimensional (P2D) model couples a large number of nonlinear partial differential equations, leading to the model too complicated to be employed in actual application. The simplified single-particle (SP) model has a trend for the real usage, however, its accuracy needs to be improved, since it meets the demand only under the condition of a low charge/discharge rate. To overcome the drawbacks of the SP model, the extended single-particle model (ESP) with higher accuracy is proposed in this study. We also propose a new state of charge (SOC) closed-loop estimation algorithm based on the combination of ESP model and the ampere-hour integration. Results show that the ESP model can effectively simulate the performance of the battery, and the closed-loop SOC estimation algorithm can correct the initial SOC error without increasing the computational complexity. The mean error of the closed-loop SOC estimation based on ESP is reduced by about 95% and 92.5% than ampere-hour integration under 1 C discharge and FUDS discharge, respectively.

The efficiency of inductive power transfer (IPT) system is highly related to the conditions of load and the compensation network. Combined with lithium battery charging characteristic, the transfer efficiency exhibits sharply decreasing in constant voltage (CV) mode in wireless electric vehicle (EV) charging system. This paper compares the light-load efficiency between LCC-Series (LCC-S) and LCC-Parallel (LCC-P) compensation networks under constant voltage (CV) charging mode. An efficiency optimization method is proposed for the LCC-P compensated wireless EV charging system, which is more suitable for the load variation during the constant voltage (CV) charging phase of the lithium battery. After the theoretical analysis is conduced, the MATLAB simulation and experiment were completed to prove the accuracy of the analysis result.

Whether an algorithm of state of charge (SOC) estimation being able to use successfully in a real usage or not is highly determined by its robustness, because various errors including sensors’ errors, initial data errors, model parameters errors and so on disturb the calculation, and thus make the accuracy of estimation decrease significantly and even a wrong SOC be given out. However, systemic analysis of robustness of the algorithms that have been comprehensive studied or even been used in real applications still remains unsolved until now. In this study, various scenarios in which the random error such as current sensor bias, voltage sensor bias, and initial parameter identification errors exists are tested by using an ampere hour integration (Ah), the extended Kalman Filter (EKF) and a dual extended Kalman filter (DEKF) algorithm. Results show the robustness of three algorithms in the scenarios give different deviations. In case of the disturbance of the random error, the SOC can be effectively estimated by using the DEKF algorithm, since the maximum error of the SOC estimation from the algorithm is less than 3%, which is better than the results by using the EKF and the Ah integration.

Live migration, which allows virtual machines (VMs) to move across distinct physical hosts, is widely adopted for realizing energy saving and load balancing in modern Cloud data centers. However, putting VMs with high correlations on their CPU utilization patterns onto the same host is very likely to trigger overloading incidents and commit Service Level Agreements (SLA) violations, even when the host has not yet reached its critical limits. To avoid such SLA violations and to maintain a low energy consumption, this work proposes a heuristics-based VM allocation mechanism. Under the proposed mechanism, VMs showing high CPU utilization correlations to other co-located peers and hosts with extreme utilization values are both assigned with high heuristic values. VMs with high heuristic values are therefore less preferred to be co-located onto a host that has a high heuristic value. Performance of the proposed mechanism was evaluated using CloudSim with real-world workload data. Simulation results show that when comparing with other existing mechanisms, the proposed idea can further reduce both energy consumption and SLA violations.

Virtualization technology has been widely adoptedin Cloud data centers for adaptive resource provisioning. Withvirtualization, multiple virtual machines (VMs) can be colocatedon a single physical host to yield maximum efficiency. However, VMs which show high CPU utilization correlationsto other co-located peers are more likely to trigger overloadingincidents. This work provides an analysis on effects ofcorrelation-based VM allocation criteria to Cloud data centers. The correlations among VMs' CPU utilizations are consideredas parameters for decision making in VM allocation processes. Three different expressions of correlation-based criteria areintroduced and evaluated in this work. According to oursimulation results obtained from CloudSim with real-worldworkload traces, Cloud data centers with correlation-based allocationcriteria can perform better in terms of reducing energyconsumption and avoid committing Service Level Agreementsviolations than those with power-based criteria.

With the rapid growing number of Cloud applications, demands for large-scale data centers have raised to historical high. Cloud data centers allow dynamic and flexible resource provisioning to accommodate time varying computational demands. Recent studies have proposed several allocation policies based mainly on power consumption of servers. Host temperature, however, is rarely considered as a monitoring parameter. This work proposes a power and thermal-aware virtual machine (VM) allocation mechanism for Cloud data centers. The objective of the proposed mechanism is to reduce the overall energy consumption and VM migration numbers, while avoiding violations of Service Level Agreements (SLA) in Cloud data centers. The proposed mechanism was implemented and evaluated on CloudSim. Simulation results show that the proposed allocation mechanism brings significant benefits in terms of energy saving and other performance indices.

The power semiconductor switches are the most unreliable devices in the Photovoltaic (PV) energy conversion system. Thermal stress is the prime cause behind the power semiconductor switch failure mechanism, the selection of switching strategy and converter topology can have a notable effect on the converter reliability. This paper presents a lifetime comparison of Insulated-Gate Bipolar Transistor (IGBT) modules in three classical multilevel inverter topologies on the basis of power cycling, thermal cycling, and lifetime performance. The electro-thermal analysis has been implemented to identify the amplitude of the temperature swings and mean junction temperature. The Bayer lifetime model is used to evaluate the consumed lifetime of IGBT modules in the multilevel inverters based on the calculation method of numerical junction temperature. The accuracy in lifetime estimation has improved by considering the mission profile. The study shows that the IGBT modules in cascaded H-bridge multilevel inverter have a higher lifetime when compared to the other two classical multilevel inverters