Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> D. Battery Discharge Modeling <s> This paper explores the recovery and rate capacity effect for batteries used in embedded systems. It describes the prominent battery models with their advantages and drawbacks. It then throws new light on the battery recovery behavior, which can help determine optimum discharge profiles and hence result in significant improvement in battery lifetime. Finally it proposes a fast and accurate stochastic model which draws the positives from the earlier models and minimizes the drawbacks. The parameters for this model are determined by a pretest, which takes into account the newfound background into recovery and rate capacity hence resulting in higher accuracy. Simulations conducted suggest close correspondence with experimental results and a maximum error of 2.65%. <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> D. Battery Discharge Modeling <s> 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. <s> BIB002 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> D. Battery Discharge Modeling <s> Lithium batteries are characterized by high specific energy, high efficiency and long life. These unique properties have made lithium batteries the power sources of choice for the consumer electronics market with a production of the order of billions of units per year. These batteries are also expected to find a prominent role as ideal electrochemical storage systems in renewable energy plants, as well as power systems for sustainable vehicles, such as hybrid and electric vehicles. However, scaling up the lithium battery technology for these applications is still problematic since issues such as safety, costs, wide operational temperature and materials availability, are still to be resolved. This review focuses first on the present status of lithium battery technology, then on its near future development and finally it examines important new directions aimed at achieving quantum jumps in energy and power content. <s> BIB003 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> D. Battery Discharge Modeling <s> The principles of rechargeable battery operation form the basis of the electronic network models developed for Nickel-based aqueous battery systems, including Nickel Metal Hydride (NiMH), and non-aqueous battery systems, such as the well-known Li-ion. These electronic network models are based on fundamental physical and (electro)chemical processes, occurring during battery operation and also during over(dis)charging in the case of the aqueous battery systems. This enabled us to visualize the various reaction pathways, including conventional and pulse (dis)charge behavior but also, for example, the self-discharge performance. <s> BIB004
Energy modeling methods that involve bypassing the device battery characteristics would not be reliable. Even though some technological advances has be made to improve density in battery capacity and charging cycles, it is not suitable to be used in the end products BIB003 . Thereby, the modeling of battery's nonlinearities BIB001 , BIB002 , and understanding the correlation between usage patterns and battery depletion will lead to successful discovery of optimal energy reduction strategies that will eventually help maximize the wasted energy consumption to improve the QoS of context-aware applications. In this regard, the topics such as extension of battery lifetimes, estimation of energy delivery or battery discharge, and optimal energy management have drawn much research interest in mobile computing. The examination of non-linear battery behaviors becomes crucial in terms of creating optimal sensor management systems. Correspondingly, battery lifetime mostly depends on energy consumption rate, discharge profile, i.e., usage pattern, and battery non-linearities. At the high energy consumption rate, the effective residual battery capacity degrades and results in having a shorter battery lifetime. However, any precautionary change in the usage pattern could extend the battery lifetime. More importantly, the physical non-linearities in the batteries could recover the lost capacity while energy consumption decreases. Accurately estimating the remaining battery capacity BIB004 and reporting of the battery state-of-charge becomes a difficult task due to the nonlinear battery behavior, and also the time-varying nature of mobile device operation that would cause different amount of power need at any time. The future research trend should investigate mobile device based battery behavior with respect to variant sensory operations in smart devices. Thereby, the linkage between battery discharge and power consumption caused by the sensors can be analyzed, and most importantly, a fine power efficiency can be objected to achieve while satisfying continuity of mobile device based context-aware services.