Computing lifetimes for battery-powered devices

The battery lifetime of mobile devices depends on the usage pattern of the battery, next to the discharge rate and the battery capacity. Therefore, it is important to include the usage pattern in battery lifetime computations. We do this by combining a stochastic workload, modeled as a continuous-time Markov model, with a well-known battery model. For this combined model, we provide new algorithms to efficiently compute the expected lifetime and the distribution and expected value of the delivered charge

Model-based energy analysis of battery powered systems

The use of mobile devices is often limited by the lifetime of the included batteries.\ud This lifetime naturally depends on the battery’s capacity and on the rate at which\ud the battery is discharged. However, it also depends on the usage pattern, i.e.,\ud the workload, of the battery. When a battery is continuously discharged, a high\ud current will cause it to provide less energy until the end of its lifetime than a lower\ud current. This effect is termed the rate-capacity effect. On the other hand, during\ud periods of low or no discharge current, the battery can recover to a certain extent.\ud This effect is termed the recovery effect. In order to investigate the influence of the\ud device workload on the battery lifetime a battery model is needed that includes\ud the above described effects.\ud Many different battery models have been developed for different application\ud areas. We make a comparison of the main approaches that have been taken.\ud Analytical models appear to be the best suited to use in combination with a device\ud workload model, in particular, the so-called kinetic battery model. This model is\ud combined with a continuous-time Markov chain, which models the workload of a\ud battery powered device in a stochastic manner. For this model, we have developed\ud algorithms to compute both the distribution and expected value of the battery\ud lifetime and the charge delivered by the battery. These algorithms are used to\ud make comparisons between different workloads, and can be used to analyse their\ud impact on the system lifetime.\ud In a system where multiple batteries can be connected, battery scheduling can\ud be used to “spread” the workload over the individual batteries. Two approaches\ud have been taken to find the optimal schedule for a given load. In the first approach\ud scheduling decisions are only taken when a change in the workload occurs. The\ud kinetic battery model is incorporated into a priced-timed automata model, and we\ud use the model checking tool Uppaal Cora to find schedules that lead to the longest\ud system lifetime.\ud The second approach is an analytical one, in which scheduling decisions can\ud be made at any point in time, that is, independently of workload changes. The\ud analysis of the equations of the kinetic battery model provides an upper bound\ud for the battery lifetime. This upper bound can be approached with any type of scheduler, as long as one can switch fast enough. Both the approaches show\ud that battery scheduling can potentially provide a considerable improvement of the\ud system lifetime. The actual improvement mainly depends on the ratio between\ud the battery capacity and the average discharge current

Computing Battery Lifetime Distributions

The usage of mobile devices like cell phones, navigation systems, or laptop computers, is limited by the lifetime of the included batteries. This lifetime depends naturally on the rate at which energy is consumed, however, it also depends on the usage pattern of the battery. Continuous drawing of a high current results in an excessive drop of residual capacity. However, during \ud intervals with no or very small currents, batteries do recover to a certain extend. We model this complex behaviour with an inhomogeneous Markov reward model, following the approach of the so-called Kinetic battery Model (KiBaM). \ud The state-dependent reward rates thereby correspond to the power consumption of the attached device and to the available charge, respectively. We develop a tailored numerical algorithm for the computation of the distribution of the consumed energy and show how different workload patterns influence the overall lifetime of a battery

A first Experimental Investigation of the Practical Efficiency of Battery Scheduling

Nowadays, mobile devices are used more and more, and their battery lifetime is a key concern. In this paper, we concentrate on a method called battery scheduling with the aim to optimize the battery lifetime of mobile devices. This technique has already been largely theoretically studied in other papers. It consists, for systems containing multiple batteries, in switching the load from one battery to the other. Then, while following a given scheduling sequence, advantage can be taken from the recovery and rate capacity effects. However, little studies with experimental data of battery scheduling have been found. In this paper we describe a simple setup for measuring the possible gain of battery scheduling, and give some exploratory results for two types of real batteries: a smart Li-Ion battery used in the Thales personal communication system and a more commonly used NiCd battery. The results, so far, show that system lifetime extension is not systematic, and generally can only reach less then 10%

A score function for state of charge profiles for rechargeable batteries

We propose a new score function to compare and evaluate the relative impact of state-of-charge profiles on overall battery lifetime. Our score function, based on on a discrete Fourier transform of the state-of-charge profile, formalizes and generalizes earlier ideas found in the literature, and can form an important help in optimizing overall life time for battery powered systems. In this paper we introduce and illustrate the method, and discuss its merits as well as open issues and related literature

Comparative effects of oleoyl-estrone and a specific β3-adrenergic agonist (CL316, 243) on the expression of genes involved in energy metabolism of rat white adipose tissue

Background: The combination of oleoyl-estrone (OE) and a selective b3-adrenergic agonist (B3A; CL316,243) treatment in rats results in a profound and rapid wasting of body reserves (lipid). Methods: In the present study we investigated the effect of OE (oral gavage) and/or B3A (subcutaneous constant infusion) administration for 10 days to overweight male rats, compared with controls, on three distinct white adipose tissue (WAT) sites: subcutaneous inguinal, retroperitoneal and epididymal. Tissue weight, DNA (and, from these values cellularity), cAMP content and the expression of several key energy handling metabolism and control genes were analyzed and computed in relation to the whole site mass. Results: Both OE and B3A significantly decreased WAT mass, with no loss of DNA (cell numbers). OE decreased and B3A increased cAMP. Gene expression patterns were markedly different for OE and B3A. OE tended to decrease expression of most genes studied, with no changes (versus controls) of lipolytic but decrease of lipogenic enzyme genes. The effects of B3A were widely different, with a generalized increase in the expression of most genes, including the adrenergic receptors, and, especially the uncoupling protein UCP1. Discussion: OE and B3A, elicit widely different responses in WAT gene expression, end producing similar effects, such as shrinking of WAT, loss of fat, maintenance of cell numbers. OE acted essentially on the balance of lipolysislipogenesis and the blocking of the uptake of substrates; its decrease of synthesis favouring lipolysis. B3A induced a shotgun increase in the expression of most regulatory systems in the adipocyte, an effect that in the end favoured again the loss of lipid; this barely selective increase probably produces inefficiency, which coupled with the increase in UCP1 expression may help WAT to waste energy through thermogenesis. Conclusions: There were considerable differences in the responses of the three WAT sites. OE in general lowered gene expression and stealthily induced a substrate imbalance. B3A increasing the expression of most genes enhanced energy waste through inefficiency rather than through specific pathway activation. There was not a synergistic effect between OE and B3A in WAT, but their combined action increased WAT energy waste

Bond and shear mechanics within reinforced concrete beam-column joints incorporating the slotted beam detail

The recent earthquakes in Christchurch have made it clear that issues exist with current RC frame design in New Zealand. In particular, beam elongation in RC frame buildings was widespread and resulted in numerous buildings being rendered irreparable. Design solutions to overcome this problem are clearly needed, and the slotted beam is one such solution. This system has a distinct advantage over other damage avoidance design systems in that it can be constructed using current industry techniques and conventional reinforcing steel. As the name suggests, the slotted beam incorporates a vertical slot along part of the beam depth at the beam-column interface. Geometric beam elongation is accommodated via opening and closing of these slots during seismically induced rotations, while the top concrete hinge is heavily reinforced to prevent material inelastic elongation. Past research on slotted beams has shown that the bond demand on the bottom longitudinal reinforcement is increased compared with equivalent monolithic systems. Satisfying this increased bond demand through conventional means may yield impractical and economically less viable column dimensions. The same research also indicated that the joint shear mechanism was different to that observed within monolithic joints and that additional horizontal reinforcement was required as a result. Through a combination of theoretical investigation, forensic analysis, and database study, this research addresses the above issues and develops design guidelines. The use of supplementary vertical joint stirrups was investigated as a means of improving bond performance without the need for non-standard reinforcing steel or other hardware. These design guidelines were then validated experimentally with the testing of two 80% scale beam-column sub-assemblies. The revised provisions for bond within the bottom longitudinal reinforcement were found to be adequate while the top longitudinal reinforcement remained nominally elastic throughout both tests. An alternate mechanism was found to govern joint shear behaviour, removing the need for additional horizontal joint reinforcement. Current NZS3101:2006 joint shear reinforcement provisions were found to be more than adequate given the typically larger column depths required rendering the strut mechanism more effective. The test results were then used to further refine design recommendations for practicing engineers. Finally, conclusions and future research requirements were outlined

Mastering operational limitations of LEO satellites – The GOMX-3 approach

When working with space systems the keyword is resources. For a satellite in orbit all resources are sparse and the most critical resource of all is power. It is therefore crucial to have detailed knowledge on how much power is available for an energy harvesting satellite in orbit at every time – especially when in eclipse, where it draws its power from onboard batteries. This paper addresses this problem by a two-step procedure to perform task scheduling for low-earth-orbit (LEO) satellites exploiting formal methods. It combines cost-optimal reachability analyses of priced timed automata networks with a realistic kinetic battery model capable of capturing capacity limits as well as stochastic fluctuations. The procedure is in use for the automatic and resource-optimal day-ahead scheduling of GOMX-3, a power-hungry nanosatellite currently orbiting the earth. We explain how this approach has overcome existing problems, has led to improved designs, and has provided new insights

Does Your Domestic Photovoltaic Energy System Survive Grid Outages?

Domestic renewable energy systems, including photovoltaic energy generation, as well as local storage, are becoming increasingly popular and economically feasible, but do come with a wide range of options. Hence, it can be difficult to match their specification to specific customer’s needs. Next to the usage-specific demand profiles and location-specific production profiles, local energy storage through the use of batteries is becoming increasingly important, since it allows one to balance variations in production and demand, either locally or via the grid. Moreover, local storage can also help to ensure a continuous energy supply in the presence of grid outages, at least for a while. Hybrid Petri net (HPN) models allow one to analyze the effect of different battery management strategies on the continuity of such energy systems in the case of grid outages. The current paper focuses on one of these strategies, the so-called smart strategy, that reserves a certain percentage of the battery capacity to be only used in case of grid outages. Additionally, we introduce a new strategy that makes better use of the reserved backup capacity, by reducing the demand in the presence of a grid outage through a prioritization mechanism. This new strategy, called power-save, only allows the essential (high-priority) demand to draw from the battery during power outages. We show that this new strategy outperforms previously-proposed strategies through a careful analysis of a number of scenarios and for a selection of survivability measures, such as minimum survivability per day, number of survivable hours per day, minimum survivability per year and various survivability quantiles

Does Your Domestic Photovoltaic Energy System Survive Grid Outages?

Domestic renewable energy systems, including photovoltaic energy generation, as well as local storage, are becoming increasingly popular and economically feasible, but do come with a wide range of options. Hence, it can be difficult to match their specification to specific customer’s needs. Next to the usage-specific demand profiles and location-specific production profiles, local energy storage through the use of batteries is becoming increasingly important, since it allows one to balance variations in production and demand, either locally or via the grid. Moreover, local storage can also help to ensure a continuous energy supply in the presence of grid outages, at least for a while. Hybrid Petri net (HPN) models allow one to analyze the effect of different battery management strategies on the continuity of such energy systems in the case of grid outages. The current paper focuses on one of these strategies, the so-called smart strategy, that reserves a certain percentage of the battery capacity to be only used in case of grid outages. Additionally, we introduce a new strategy that makes better use of the reserved backup capacity, by reducing the demand in the presence of a grid outage through a prioritization mechanism. This new strategy, called power-save, only allows the essential (high-priority) demand to draw from the battery during power outages. We show that this new strategy outperforms previously-proposed strategies through a careful analysis of a number of scenarios and for a selection of survivability measures, such as minimum survivability per day, number of survivable hours per day, minimum survivability per year and various survivability quantiles