Recharging Probably Keeps Batteries Alive

Hermanns, Holger; Krčál, Jan; Nies, Gilles

doi:10.1007/978-3-319-25141-7_7

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…The battery is modeled according to the Kinetic Battery Model (KiBaM) (cf. [27]), which divides the battery in two wells: the available charge 𝑎 and the bound charge 𝑏. The KiBaM incorporates two important nonlinear effects: A battery has less effective capacity if it is drained with a high discharge rate, which is the rate-capacity effect.…”

Section: The Satellite Case Studymentioning

confidence: 99%

Learning optimal decisions for stochastic hybrid systems

Niehage

Hartmanns

Remke

2021

Proceedings of the 19th ACM-IEEE International Conference on Formal Methods and Models for System Design

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We apply reinforcement learning to approximate the optimal probability that a stochastic hybrid system satisfies a temporal logic formula. We consider systems with (non)linear continuous dynamics, random events following general continuous probability distributions, and discrete nondeterministic choices. We present a discretized view of states to the learner, but simulate the continuous system. Once we have learned a near-optimal scheduler resolving the choices, we use statistical model checking to estimate its probability of satisfying the formula. We implemented the approach using Q-learning in the tools HYPEG and modes, which support Petri net-and hybrid automata-based models, respectively. Via two case studies, we show the feasibility of the approach, and compare its performance and effectiveness to existing analytical techniques for a linear model. We find that our new approach quickly finds near-optimal prophetic as well as non-prophetic schedulers, which maximize or minimize the probability that a specific signal temporal logic property is satisfied.

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Section: The Satellite Case Studymentioning

confidence: 99%

Learning optimal decisions for stochastic hybrid systems

Niehage

Hartmanns

Remke

2021

Proceedings of the 19th ACM-IEEE International Conference on Formal Methods and Models for System Design

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“…High-level analytical models, such as the Kinetic Battery Model (KiBaM) [7], require much less knowledge of the battery, and can be easily combined with other models. For example, in [8], the KiBaM is extended to a random KiBaM and combined with a Markovian task process that models the battery load. With the combined model, one can compute the probability the battery is depleted due to the defined load pattern.…”

Section: Battery (Degradation) Modelsmentioning

confidence: 99%

Battery Aging, Battery Charging and the Kinetic Battery Model: A First Exploration

Jongerden

Haverkort

2017

Quantitative Evaluation of Systems

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Rechargeable batteries are omnipresent and will be used more and more, for instance for wearables devices, electric vehicles or domestic energy storage. However, batteries can deliver power only for a limited time span. They slowly degrade with every charge-discharge cycle. This degradation needs to be taken into account when considering the battery in long lasting applications. Some detailed models that describe battery degradation processes do exist, however, these are complex models and require detailed knowledge of many (physical) parameters. Furthermore, these models are in general computationally intensive, thus rendering them less suitable for use in larger system-wide models. A model better suited for this purpose is the so-called Kinetic Battery Model. In this paper, we explore how this model could be enhanced to also cope with battery degradation, and with charging. Up till now, battery degradation nor battery charging has been addressed in this context. Using an experimental set-up, we explore how the KiBaM can be used and extended for these purposes as well, thus allowing for better integrated modeling studies.

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“…Unlike our method, this approach discusses a single battery case only. Another novel work in [12] extends KiBaMs with random initial SoC and load, without discretising time. In this way, probabilistic guarantees about the system lifetime can be provided.…”

Section: Related Workmentioning

confidence: 99%

Model Checking and Evaluating QoS of Batteries in MPSoC Dataflow Applications via Hybrid Automata

Ahmad

Jongerden

Stoelinga

et al. 2016

2016 16th International Conference on Application of Concurrency to System Design (ACSD)

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Abstract-System lifetime is a major design constraint for battery-powered mobile embedded systems. The increasing gap between the energy demand of portable devices and their battery capacities is further limiting durability of mobile devices. Thus, the guarantees over Quality of Service (QoS) of batteryconstrained devices under strict battery capacities are of primary interest for mobile embedded systems' manufacturers and stakeholders.This paper presents a novel approach for deriving QoS of applications modelled as synchronous dataflow (SDF) graphs. We map these applications on heterogeneous multiprocessor platforms that are partitioned into Voltage and Frequency Islands, together with multiple kinetic battery models (KiBaMs). By modelling the whole system as hybrid automata, and applying model-checking, we evaluate, (1) system lifetime; and (2) minimum required initial battery capacities to achieve the desired application performance. We demonstrate that our approach shows a significant improvement in terms of scalability, as compared to a priced timed automata based KiBaM model. This approach also allows early detection of design errors via model checking.

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Recharging Probably Keeps Batteries Alive

Cited by 8 publications

References 38 publications

Learning optimal decisions for stochastic hybrid systems

Learning optimal decisions for stochastic hybrid systems

Battery Aging, Battery Charging and the Kinetic Battery Model: A First Exploration

Model Checking and Evaluating QoS of Batteries in MPSoC Dataflow Applications via Hybrid Automata

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