Battery Modeling

The use of mobile devices is often limited by the capacity of the employed batteries. The battery lifetime determines how long one can use a device. Battery modeling can help to predict, and possibly extend this lifetime. Many different battery models have been developed over the years. However, with these models one can only compute lifetimes for specific discharge profiles, and not for workloads in general. In this paper, we give an overview of the different battery models that are available, and evaluate these models in their suitability to combine them with a workload model to create a more powerful battery model. \u

Battery Aging and the Kinetic Battery Model

Batteries are omnipresent, and with the uprise of the electrical vehicles will their use will grow even more. However, the batteries can deliver their required power 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 battery models that describe the degradation exist. However, these are complex models that require detailed knowledge. These models are in general computationally intensive, which does not make them well suited to be used in a wider context. A model better suited for this is the Kinetic Battery Model. In this paper, we this model would change due to battery degradation, by the results of our experimental degradation analysis. In our analysis we see that the degradation takes place in two phases. After the first phase of slow degradation, the battery suddenly starts to degrade rapidly

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.\ud 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

Experimental validation of the recovery effect in batteries for wearable sensors and healthcare devices discovering the existence of hidden time constants

Wearable sensors and healthcare devices use small lightweight batteries to power their typical operations of monitoring and tracking. It becomes absolutely vital to effectively utilise all the available battery charge for device longevity between charges. The electrochemical recovery effect enables the extraction of more power from the battery when implementing idle times in between use cycles, and has been used to develop various power management techniques. However, there is no evidence concerning the actual increase in available power that can be attained using the recovery effect. Also, this property cannot be generalised on all the battery chemistries since it is an innate phenomenon, relying on the anode/cathode material. Indeed recent developments suggest that recovery effect does not exist at all. This paper presents experimental results to verify the presence and level of the recovery effect in commonly used battery chemistries in wearable sensors and healthcare devices. The results have revealed that the recovery effect significantly does exist in certain batteries, and importantly we show that it is also comprised of two different time constants. This novel finding has important implications for the development of power management techniques that utilise the recovery effect with application in a large range of battery devices

Which battery model to use?

The use 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 extent. The usage pattern of a device can be well modelled with stochastic workload models. However, one still needs a battery model to describe the effects of the power consumption on the state of the battery. Over the years many different types of battery models have been developed for different application areas. In this study we give a detailed analysis of two well-known analytical models, the kinetic battery model (KiBaM) and the so-called diffusion model. We show that the KiBaM is actually an approximation of the more complex diffusion model; this was not known previously. Furthermore, we tested the suitability of these models for performance evaluation purposes, and found that both models are well suited for doing battery lifetime predictions. However, one should not draw conclusions on what is the best usage pattern based on only a few workload traces

Lifetime improvement by battery scheduling

The use of mobile devices is often limited by the lifetime of its battery. For devices that have multiple batteries or that have the option to connect an extra battery, battery scheduling, thereby exploiting the recovery properties of the batteries, can help to extend the system lifetime. Due to the complexity, work on battery scheduling in the literature is limited to either small batteries or to very simple loads. In this paper, we present an approach using the Kinetic Battery Model that combines real-size batteries with realistic random loads. The results show that, indeed, battery scheduling results in lifetime improvements compared to the sequential usage of the batteries. The improvements mainly depend on the ratio between the average discharge current and the battery capacity. Our results show that for realistic loads one can achieve up to 20% improvements in system lifetime by applying battery scheduling

Evaluation of Battery Lifetimes using Inhomogeneous Markov Reward Models

The usage of mobile devices like cell phones, navigation systems, or laptop computers, is limited by the lifetime of the included batteries. This lifetimes 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. The state-dependent reward rates thereby correspond to the power consumption of the attached device and to the available charge, respectively. We develop new numerical algorithms for the computation of the distribution of the consumed energy and show how different workload patterns inuence the overall lifetime of a battery

Lifetime Improvement by Battery Scheduling

The use of mobile devices is often limited by the lifetime of their batteries. For devices that have multiple batteries or that have the option to connect an extra battery, battery scheduling, thereby exploiting the recovery properties of the batteries, can help to extend the system lifetime. Due to the complexity, work on battery scheduling in literature is limited to either small batteries or to very simple loads. In this paper, we present an approach using the Kinetic Battery Model that combines real size batteries with realistic random loads. The results show that, indeed, battery scheduling results in lifetime improvements compared to the sequential useage of the batteries. The improvements mainly depend on the ratio between the average discharge current and the battery capacity. Our results show that for realistic loads one can achieve up to 20% improvements in system lifetime by applying battery scheduling