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This paper uses a one-dimensional, physics-based model of a valve-regulated lead-acid (VRLA) battery to examine the impact of demand response on uninterruptible power supply (UPS) availability in a datacenter. Datacenters are facilities that provide services such as cloud computing, web search, etc. They are also large electricity consumers. An energy-efficient 15 MW datacenter, for instance, may pay $1 m per month for electricity. Datacenters often utilize VRLA batteries to ensure high reliability in serving their computational demand. This motivates the paper's central question: to what extent does the use of datacenter UPS batteries for demand response affect their availability for their primary purpose (namely, emergency power)? We address this question using a physics-based model of the coupled diffusion-reaction dynamics of VRLA batteries. We discretize this model using finite differences, and simulate it for different datacenter battery pack sizes. The results show that for a typical datacenter power demand profile, a VRLA battery pack sized for UPS functionality can provide demand response with only a minimal loss of UPS availability. [ABSTRACT FROM AUTHOR]

Abstract: Fuel cell hybrid vehicles offer a high-efficiency and low-emission substitute for their internal combustion engine counterparts. The hybridization significantly improves the fuel economy of the vehicle; however, exploiting the hybridization requires a well-designed power management strategy that optimally shares the power demand between the power sources. This paper deals with the optimization of power management strategy of a fuel cell/battery hybrid vehicle, both off-line and in real-time. A new formulation of the optimization problem for the real-time strategy is presented. The new approach allows the optimization of the controller over a set of driving cycles at once, which improves the robustness of the designed strategy. The real-time optimization is applied to two forms of real-time controllers: a PI controller based on Pontryagin's Minimum Principle with three parameters and a fuzzy controller with ten parameters. The results show that the PI controller can outperform the fuzzy controller, even though it has fewer parameters. The real-time controllers are designed by simulation and then validated by experiment. [Copyright &y& Elsevier]

Abstract: This paper introduces an online and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV). Based on analytic analysis between fuel-rate and battery current at different driveline power and vehicle speed, quadratic equations are applied to simulate the relationship between battery current and vehicle fuel-rate. The power threshold at which engine is turned on is optimized by genetic algorithm (GA) based on vehicle fuel-rate, battery state of charge (SOC) and driveline power demand. The optimal battery current when the engine is on is calculated using quadratic programming (QP) method. The proposed algorithm can control the battery current effectively, which makes the engine work more efficiently and thus reduce the fuel-consumption. Moreover, the controller is still applicable when the battery is unhealthy. Numerical simulations validated the feasibility of the proposed controller. [Copyright &y& Elsevier]