Low power dissipation and maximum battery runtime are crucial in portable electronics. With accurate and efficient circuit and battery models in hand, circuit designers can predict and optimize battery runtime and circuit performance. In this paper, an accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment. This model accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response. A simplified model neglecting the effects of self-discharge, cycle number, and temperature, which are nonconsequential in low-power Li-ion-supplied applications, is validated with experimental data on NiMH and polymer Li-ion batteries. Less than 0.4% runtime error and 30-mV maximum error voltage show that the proposed model predicts both the battery runtime and I-V performance accurately. The model can also be easily extended to other battery and power sourcing technologies.

Accurate electrical battery model capable of predicting runtime and I-V performance

Wireless sensor networks have emerged recently as an effective way of monitoring remote or inhospitable physical environments. One of the major challenges in devising such networks lies in the constrained energy and computational resources available to sensor nodes. These constraints must be taken into account at all levels of the system hierarchy. The deployment of sensor nodes is the first step in establishing a sensor network. Since sensor networks contain a large number of sensor nodes, the nodes must be deployed in clusters, where the location of each particular node cannot be fully guaranteed a priori. Therefore, the number of nodes that must be deployed in order to completely cover the whole monitored area is often higher than if a deterministic procedure were used. In networks with stochastically placed nodes, activating only the necessary number of sensor nodes at any particular moment can save energy. We introduce a heuristic that selects mutually exclusive sets of sensor nodes, where the members of each of those sets together completely cover the monitored area. The intervals of activity are the same for all sets, and only one of the sets is active at any time. The experimental results demonstrate that by using only a subset of sensor nodes at each moment, we achieve a significant energy savings while fully preserving coverage.

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Power efficient organization of wireless sensor networks

A critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend operational time. To monitor a set of targets with known locations when ground access in the monitored area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The loss of precise sensor placement would then be compensated by a large sensor population density in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node for processing. In this paper we propose an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while nodes from all other sets are in a low-energy sleep mode. In this paper we address the maximum disjoint set covers problem and we design a heuristic that computes the sets. Theoretical analysis and performance evaluation results are presented to verify our approach.

/pdf/improving-wireless-sensor-network-lifetime-through-power-14ep8g7ghd.pdf

Improving wireless sensor network lifetime through power aware organization

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

Advances in battery technology have not kept pace with rapidly growing energy demands. Most laptops, handheld PCs, and cell phones use batteries that take anywhere from 1.5 to 4 hours to fully charge but can run on this charge for only a few hours. The battery has thus become a key control parameter in the energy management of portables. To meet the stringent power budget of these devices, researchers have explored various architectural, hardware, software, and system-level optimizations to minimize the energy consumed per useful computation. Research in battery-aware optimization is now moving from stand-alone devices to networks of wireless devices, specifically, ad hoc and distributed sensor networks. Computationally feasible mathematical models are now available that capture battery discharge characteristics in sufficient detail to let designers develop an optimization strategy that extracts maximum charge.

Battery modeling for energy aware system design

In this paper, we introduce a discrete-time model for the complete power supply sub-system that closely approximates the behavior of its circuit-level (i.e., HSpice), continuous-time counterpart. The model is abstract and efficient enough to enable event-driven simulation of digital systems described at a very high level of abstraction and that include, among their components, also the power supply. Therefore, it can be successfully used for the purpose of battery life-time estimation during design optimization, as shown by the results we have collected on a meaningful case study. Experiments prove also that the accuracy of our model is very close to that provided by the corresponding Spice-level model.

/pdf/a-discrete-time-battery-model-for-high-level-power-4m4abbrsbe.pdf

A discrete-time battery model for high-level power estimation

For portable applications, long battery lifetime is the ultimate design goal. Therefore, the availability of battery and voltage converter models providing accurate estimates of battery lifetime is key for system-level low-power design frameworks. In this paper, we introduce a discrete-time model for the complete power supply subsystem that closely approximates the behavior of its circuit-level continuous-time counterpart. The model is abstract and efficient enough to enable event-driven simulation of digital systems described at a very high level of abstraction and that includes, among their components, also the power supply. The model gives the designer the possibility of estimating battery lifetime during system-level design exploration, as shown by the results we have collected on meaningful case studies. In addition, it is flexible and it can thus be employed for different battery chemistries.

/pdf/discrete-time-battery-models-for-system-level-low-power-37405k27co.pdf

Discrete-time battery models for system-level low-power design

In this paper we present algorithms for the synthesis of encoding and decoding interface logic that minimizes the average number of transitions on heavily-loaded global bus lines at no cost in communication throughput (i.e., one word is transmitted at each cycle). The distinguishing feature of our approach is that it does not rely on designer's intuition, but it automatically constructs low-transition activity codes and hardware implementation of encoders and decoders, given information on word-level statistics. We propose an accurate method that is applicable to low-width buses, as well as approximate methods that scale well with bus width. Furthermore, we introduce an adaptive architecture that automatically adjusts encoding to reduce transition activity on buses whose word-level statistics are not known a priori. Experimental results demonstrate that our approaches out-perform specialized low-power encoding schemes presented in the past.

/pdf/architectures-and-synthesis-algorithms-for-power-efficient-1vq55mjo7u.pdf

Architectures and synthesis algorithms for power-efficient bus interfaces

Battery lifetime extension is a primary design objective for portable systems. We introduce the concept of battery-driven dynamic power management, which strives to enhance lifetime by automatically adapting discharge rate and current profiles to battery charge state.

Battery-driven dynamic power management

Multi-battery power supplies are becoming popular in electronic appliances of the latest generations, due to economical and manufacturing constraints. Unfortunately, a partitioned battery subsystem is not able to deliver the same amount of charge as a monolithic battery with the same total capacity. In this paper, we define the concept of battery scheduling, we investigate policies for solving the problem of optimal charge delivery, and we study the relationship of such policies with different configurations of the battery subsystem. Results, obtained for different workloads, demonstrate that the choice of the proper scheduling can make, in the best cease, system lifetime as close as 1% of that guaranteed by a monolithic battery of equal capacity.

R. Scarsi

Papers

A discrete-time battery model for high-level power estimation

Discrete-time battery models for system-level low-power design

Architectures and synthesis algorithms for power-efficient bus interfaces

Battery-driven dynamic power management

Extending lifetime of portable systems by battery scheduling