Model-based energy analysis of battery powered systems

Jongerden, M.R.

doi:10.3990/1.9789036531146

Cited by 13 publications

(27 citation statements)

References 52 publications

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“…A part of the BTS corresponding to Example 17 is given in Figure 7. ... (16,16) The battery diffusion constant k = 1 8 and its width constant c = 1 2 . Figure 7.…”

Section: Example 17mentioning

confidence: 99%

“…Batteries are involved devices, exhibiting various different phenomena. Accordingly, there are many different works considering these aspects, for example scheduling the load among several batteries [4,9,16,18], optimizing the lifetime of a battery with respect to the "cycle aging effect" [1], analyzing the thermal effects, and more.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work has either considered ideal energy sources (for example, [8]) or provides approximations for the battery life-time with respect to various discharge scenarios (for example, [16,17]). …”

Section: Introductionmentioning

confidence: 99%

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Battery transition systems

Boker

Henzinger

Radhakrishna

2014

Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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The analysis of the energy consumption of software is an important goal for quantitative formal methods. Current methods, using weighted transition systems or energy games, model the energy source as an ideal resource whose status is characterized by one number, namely the amount of remaining energy. Real batteries, however, exhibit behaviors that can deviate substantially from an ideal energy resource. Based on a discretization of a standard continuous battery model, we introduce battery transition systems. In this model, a battery is viewed as consisting of two parts -the available-charge tank and the bound-charge tank. Any charge or discharge is applied to the available-charge tank. Over time, the energy from each tank diffuses to the other tank.Battery transition systems are infinite state systems that, being not well-structured, fall into no decidable class that is known to us. Nonetheless, we are able to prove that the ω-regular modelchecking problem is decidable for battery transition systems. We also present a case study on the verification of control programs for energy-constrained semi-autonomous robots.

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“…A part of the BTS corresponding to Example 17 is given in Figure 7. ... (16,16) The battery diffusion constant k = 1 8 and its width constant c = 1 2 . Figure 7.…”

Section: Example 17mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Battery transition systems

Boker

Henzinger

Radhakrishna

2014

Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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“…Therefore, (4.41) can be rewritten as: 44) which is equivalent to (4.18) from the analytical model. Therefore, we have:…”

Section: Proof Of Equivalent Overhead Costmentioning

confidence: 99%

“…Traffic load is not only an indicator to bandwidth usage of the network, but also is one of the major sources of battery usage [44]. We aim to develop an expression for the total cost of transmissions in bits per second per node.…”

Section: Cost Functionsmentioning

confidence: 99%

Context discovery in ad-hoc networks

Liu¹

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With the rapid development of wireless technology and portable devices, mobile adhoc networks (MANETs) are more and more present in our daily life. Ad-hoc networks are often composed of mobile and battery-supplied devices, like laptops, mobile phones, and PDAs. With no requirement for infrastructure support, MANETs can be used as temporary networks, such as for conference and office environments, and for disaster areas. The disadvantage is that they usually have limited bandwidth and that devices in ad-hoc networks have energy-constrained power supplies, which requires simple and efficient underlying communication protocols. One of the most fundamental actions that such devices in networks need to do is to find information about the environment they are operating in. To share and use the available context information in the network, devices first need to discover and locate the required information. This action is called context discovery. However, none of the existing discovery protocols can well support resource-limited fully-distributed MANETs. Therefore, in this thesis, we design and develop a new context discovery protocol for MANETs, which is called Ahoy.By using attenuated Bloom filters, Ahoy reduces traffic load to discover available context information and provides directional probabilistic querying. We build an analytical model to evaluate the performance of Ahoy compared with two conventional approaches: pro-active and reactive discovery protocols, and to allow for optimization of Ahoy's parameters. The results of the analytical model are validated by simulations. We estimate the network traffic generated by Ahoy in both static and dynamic environments. We find that Ahoy requires significantly less network traffic than the other two protocols in static networks, and that it is stable in a dynamic environment in which the network topology may change.We also study the vulnerability of Ahoy when it encounters different malicious attacks. Our analyses shows that compared with pro-active and reactive protocols, Ahoy is not more vulnerable than the other two protocols. In some cases, the use of attenuated Bloom filters can even help to protect the contents of packets up to a certain level. In case of serious risks, we propose specialized security countermeasures to enhance the network security of Ahoy.Finally, we build a prototype of Ahoy and test it on UNIX-like platforms.v vi Through these analysis and studies, we conclude that the novel discovery protocol Ahoy proposed in this thesis can discover information efficiently, while generating only little network traffic, in both static and dynamic fully-distributed MANETs.

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Characterization of Load Uncertainty in Unstructured Terrains and Applications to Battery Remaining Run‐time Prediction

LeSage

Longoria

2013

Journal of Field Robotics

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Deployment of field robots in unstructured environments continues to rise, and the electrochemical battery remains the de facto standard for energy storage in robotic applications. However, robot mission planning, which relies on battery depletion time information, enforces conservative operation due to the lack of statistical rigor on the run‐time predictions. A two‐tier self‐supervised load characterization methodology for mobile robots operating in unstructured environments is proposed. Coupled with load characterization, a model‐based statistical battery remaining run‐time prediction algorithm utilizing particle filtering is presented. Given measured power loads during operation, the characterization algorithm employs Gaussian mixture modeling to cluster measured loads into a priori unknown regions. With clustered power demand regions, the computation of transition probabilities between the mixture models provides a jump Markov characterization of the historical power loads. An experimental study utilized Packbot data gathered during operation on general desert terrain. A particle filter prediction framework was shown to more accurately predict the remaining run‐time of the Packbot given the unstructured terrain compared to existing load‐averaging techniques. © 2013 Wiley Periodicals, Inc.

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Model-based energy analysis of battery powered systems

Cited by 13 publications

References 52 publications

Battery transition systems

Battery transition systems

Context discovery in ad-hoc networks

Characterization of Load Uncertainty in Unstructured Terrains and Applications to Battery Remaining Run‐time Prediction

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