The pervasiveness of wireless sensor networks (WSNs) across different applications keeps increasing due to their versatility, although energy consumption is a huge constraint for these types of networks. Therefore, it is essential to incorporate consistent and reliable energy sources. Mobile nodes require independent sources, often composed of a battery with one or more energy harvesting devices (EHDs). The exigency for energy-efficiency improvements is a collateral effect of the growth of WSNs. Different methods have been proposed to improve the energy efficiency of these systems. This article focuses on the techniques of energy-efficiency developed in the medium access control (MAC) layer. The proposed solution incorporates EHDs and analytic information, such as the estimation of the network lifetime (NL), to achieve an energy efficient system, potentially self-sustainable. A widely used MAC-protocol technique for energy conservation is duty cycling, because it facilitates the control over the transitions of the active and sleep modes of the node, conserving energy if used efficiently. Due to the importance of MAC protocols in energy conservation, it is necessary to compile a review of the MAC protocols that address this issue, covering the traditional way of classifying them, the characteristics presented by each group and how they have been adapted to the appearance of new tools such as EHDs and the information obtained from the battery to estimate the lifetime of the sensor node.

Nowadays, electric vehicles such as cars and bicycles are increasing their popularity due to the rising environmental consciousness. The autonomy required by these means of transport has marked a significant and steady growth in the development of battery technologies. In this sense, it is crucial to estimate and prognosticate critical parameters of battery packs such as the State of Charge (SOC), the State of Maximum Power Available (SoMPA), and the Failure Time. All these indicators are relevant to determine if both the energy stored in the battery of electric vehicles and power specifications are sufficient to successfully complete a required route, avoiding battery preventive disconnection before arrival. In this regard, this paper presents a novel approach to estimate and prognosticate the SOC and SoMPA of Lithium-Ion batteries in the context of electromobility applications. The proposed method uses the formulation of an optimization problem to find an analytical relationship between the SOC and the SoMPA; whereas the battery pack is modeled in terms of both the polarization resistance and the SOC. Particle filtering algorithms are used to compute online estimates and prognostic results, while the characterization of the usage profile of the battery bank is achieved using probability-based models (Markov chains). The problem of battery monitoring for an electric bicycle is used as a case study to validate the proposed scheme, when driven in flat and sloped routes to generate different usage profiles. It is demonstrated that the proposed methodology allows to successfully prognosticate both SOC and SoMPA when the future discharge current profile is characterized in terms of probability-based models.

The use of energy storage devices, such as lithium-ion batteries, has become popular in many different domains and applications. Hence, it is relatively easy to find literature associated with problems of battery state-of-charge estimation and energy autonomy prognostics. Despite this fact, the characterization of battery degradation processes is still a matter of ongoing research. Indeed, most battery degradation models solely consider operation under nominal (or strictly controlled) conditions, although actual operating profiles (including discharge current) may differ significantly from those. In this context, this article proposes a lithium-ion battery degradation model that incorporates the impact of arbitrary discharge currents. Also, the proposed model, initially calibrated through data reported for a specific lithium-ion battery type, can characterize degradation curves for other lithium-ion batteries. Two case studies have been carried out to validate the proposed model, initially calibrated by using data from a Sony battery. The first case study uses our own experimental data obtained for a Panasonic lithium-ion cell, which was cycled and degraded at high current rates. The second case study considers the analysis of two public data sets available at the Prognostics Center of Excellence of NASA Ames Research Center website, for batteries cycled using nominal and 2-C (twice the nominal) discharge currents. Results show that the proposed model can characterize degradation processes properly, even when cycles are subject to different discharge currents and for batteries not manufactured by Sony (whose data were used for the initial calibration).

Battery energy systems are currently one of the most common power sources found in mobile electromechanical devices. In all these equipment, assuring the autonomy of the system requires to determine the battery state-of-charge (SOC) and predicting the end-of-discharge time with a high degree of accuracy. In this regard, this paper presents a comparative analysis of two well-known Bayesian estimation algorithms (Particle filter and Unscented Kalman filter) when used in combination with Outer Feedback Correction Loops (OFCLs) to estimate the SOC and prognosticate the discharge time of lithium-ion batteries. Results show that, on the one hand, a PF-based estimation and prognosis scheme is the method of choice if the model for the dynamic system is inexact to some extent; providing reasonable results regardless if used with or without OFCLs. On the other hand, if a reliable model for the dynamic system is available, a combination of an Unscented Kalman Filter (UKF) with OFCLs outperforms a scheme that combines PF and OFCLs.