Marijn R. Jongerden

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 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). 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.

Computing Optimal Schedules of Battery Usage in Embedded Systems

The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round-robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper, we compare these scheduling schemes with the optimal scheduling scheme produced with two different modeling approaches: an approach based on a priced-timed automaton model (implemented and evaluated in Uppaal Cora), as well as an analytical approach (partly formulated as nonlinear optimization problem) for a slightly adapted scheduling problem. We show that in some cases the results of the simple scheduling schemes (round-robin, and best-first) are close to optimal. However, the optimal schedules, computed according to both methods, also clearly show that in a variety of scenarios, the simple schedules are far from optimal.

Maximizing system lifetime by battery scheduling

The use of mobile devices is limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries over the load to exploit recovery properties usually extends the system lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper we compare these scheduling schemes with the optimal scheduling scheme produced with a priced-timed automaton battery model (implemented and evaluated in Uppaal Cora). We see that in some cases the results of the simple scheduling schemes are close to optimal. However, the optimal schedules also clearly show that there is still room for improving the battery lifetimes.

Model-based energy analysis of battery powered systems

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

Energy Resilience Modelling for Smart Houses

The use of renewable energy in houses and neighbourhoods is very much governed by national legislation and has recently led to enormous changes in the energy market and poses a serious threat to the stability of the grid at peak production times. One of the approaches towards a more balanced grid is, e.g., taken by the German government by subsidizing local storage for solar power. While the main interest of the energy operator and the government is to balance the grid, thereby ensuring its stability, the main interest of the client is twofold: the total cost for electricity should be as low as possible and the house should be as resilient as possible in the presence of power outages. Using local battery storage can help to overcome the effects of power outages. However, the resulting resilience highly depends on the battery usage strategy employed by the controller, taking into account the state of charge of the battery. We present a Hybrid Petri net model of a house (that is mainly powered by solar energy) with a local storage unit, and analyse the impact of different battery usage strategies on its resilience for different production and consumption patterns. Our analysis shows that there is a direct relationship between resilience and flexibility, since increased resilience, i.e., reserving battery capacity for backup, decreases the flexibility of the storage unit.

A Score Function for Optimizing the Cycle-Life of Battery-Powered Embedded Systems

An ever increasing share of embedded systems is powered by rechargeable batteries. These batteries deteriorate with the number of charge/discharge cycles they are subjected to, the so-called cycle life. In this paper, we propose the wear score function to compare and evalu- ate the relative impact of usage (charge and discharge) profiles on cycle life. The wear score function can not only be used to rank different usage profiles, these rankings can also be used as a criterion for optimizing the overall lifetime of a battery-powered system. We perform such an optimization on a nano-satellite case study provided by the company GomSpace. The scheduling of the system is modelled as a network of (stochastic) weighted timed games. In a stochas- tic setting, exact optimization is very expensive. However, the recently introduced Uppaal Stratego tool combines symbolic synthesis with statistical model checking and reinforcement learning to synthesize near- optimal scheduling strategies subject to possible hard timing-constaints. We use this to study the trade-off between optimal short-term dynamic payload selection and the operational life of the satellite.

Assessing the cost of energy independence

Battery management strategies that reserve a certain capacity for power outages are able to increase the energy independence of a smart home. However, such strategies come at a certain cost, since these storage strategies are less flexible and energy from the grid may have to be bought at a high price, even though locally produced energy is still available in the battery, but reserved for power outage periods. This paper evaluates the cost of energy independence in smart homes with local energy generation, local storage, stochastic grid outages and seasonal demand and production profiles. As case study we use a house with matching demand and production. We provide a seasonal dependent battery management strategy, that reduces the costs of surviving grid outages. Compared to a system that maximizes self-use, the costs to achieve a survivability of 99.999% are approximately 41 € per year for 1 MWh demand.

Ion beam analysis of oxidation and hydrogenation of switchable mirrors

Abstract Thin films of Y, La or rare earth (RE) hydrides exhibit a metal insulator transition between their di- and trihydride phase. After H loading lateral diffusion samples of these materials contain an overview of all hydride phases present in the thin film phase diagram. In this paper the thin film YH x system will be investigated. The unexpected presence of a lateral oxygen profile in the YH x sample necessitates a careful interpretation of local hydrogen concentration differences. In this paper the potential of ion beam analysis will be discussed with respect to the investigation of oxidation and hydrogenation of YH x switchable mirrors. The results of the measurements will be discussed in terms of differences between bulk- and thin-film-phase diagrams of YH x . The experimental methods used are 1 H ( 4 He, 1 H) 4 He elastic recoil detection at 5 MeV and 16 O( 4 He, 4 He) 16 O resonant backscattering around 3.036 MeV.

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

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.

Development of a Smart Grid Simulation Environment

With the increased integration of renewable energy sources the interaction between energy producers and consumers has become a bi-directional exchange. Therefore, the electrical grid must be adapted into a smart grid which effectively regulates this two-way interaction. With the aid of simulation, stakeholders can obtain information on how to properly develop and control the smart grid. In this paper, we present the development of an integrated smart grid simulation model, using the Anylogic simulation environment. Among the elements which are included in the simulation model are houses connected to a renewable energy source, and batteries as storage devices. With the use of the these elements a neighbourhood model can be constructed and simulated under multiple scenarios and configurations. The developed simulation environment provides users better insight into the effects of running different configurations in their houses as well as allow developers to study the inter-exchange of energy between elements in a smart city on multiple levels.