Characterizing the degradation process of Lithium-Ion Batteries using a Similarity-Based-Modeling Approach

This article proposes a Similarity-Based-Modeling (SBM) approach capable of characterizing the degradation process of a lithium-ion (Li-ion) battery when discharged under different current rates and different State-of-Charge (SOC) ranges. The degradation process can be represented through a biexponential model. In this regard, it is possible to determine the equivalent cycle-by-cycle efficiency which has low values at the beginning of the degradation process until it reaches a higher and steady value. The lifespan of the batteries is analyzed through the use of Monte Carlo simulations which intends to represent a more realistic way of how the batteries are used.This article proposes a Similarity-Based-Modeling (SBM) approach capable of characterizing the degradation process of a lithium-ion (Li-ion) battery when discharged under different current rates and different State-of-Charge (SOC) ranges. The degradation process can be represented through a biexponential model. In this regard, it is possible to determine the equivalent cycle-by-cycle efficiency which has low values at the beginning of the degradation process until it reaches a higher and steady value. The lifespan of the batteries is analyzed through the use of Monte Carlo simulations which intends to represent a more realistic way of how the batteries are used

Procedure for Selecting a Transmission Mode Dependent on the State-of-Charge and State-of-Health of a Lithium-ion Battery in Wireless Sensor Networks with Energy Harvesting Devices

Diverse methods and considerations have been proposed to manage the available energy in an efficient manner in Wireless Sensor Networks. By incorporating Energy Harvesting Devices in these type of networks it is possible to reduce the dependency of the availability of the Energy Storage Devices, particularly the lithium-ion battery. Recently, the State-of-Charge and State-of-Health of the battery have been considered as inputs for the design of the Medium- Access-Control protocols for Wireless Sensor Networks. In this article, different guidelines are proposed for the design of Medium-Access-Control protocols used in Wireless Sensor Networks with Energy Harvesting Devices considering the State-of-Charge and State-of-Health as indicators for the estimation of the transmission time of the sensor node. The proposed guidelines consider different currents used during the transmission to estimate the State-of-Charge and Stateof- Health of the battery. The incorporation of these indicators aim to improve the decision-making process of the sensor node when transmitting.Diverse methods and considerations have been proposed to manage the available energy in an efficient manner in Wireless Sensor Networks. By incorporating Energy Harvesting Devices in these type of networks it is possible to reduce the dependency of the availability of the Energy Storage Devices, particularly the lithium-ion battery. Recently, the State-of-Charge and State-of-Health of the battery have been considered as inputs for the design of the Medium- Access-Control protocols for Wireless Sensor Networks. In this article, different guidelines are proposed for the design of Medium-Access-Control protocols used in Wireless Sensor Networks with Energy Harvesting Devices considering the State-of-Charge and State-of-Health as indicators for the estimation of the transmission time of the sensor node. The proposed guidelines consider different currents used during the transmission to estimate the State-of-Charge and Stateof- Health of the battery. The incorporation of these indicators aim to improve the decision-making process of the sensor node when transmitting

Lithium-Ion Battery End-of-Discharge Time Estimation and Prognosis based on Bayesian Algorithms and Outer Feedback Correction Loops: A Comparative Analysis

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.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

Particle-Filtering-Based State-of-Health Estimation and End-of-Life Prognosis for Lithium-Ion Batteries at Operation Temperature

We present the implementation of a particle-filtering-based framework that estimates the State-of-Health (SOH) and predicts the End-of-Life (EOL) of Lithium-Ion batteries, efficiently incorporating variations of ambient temperature in the analysis. The proposed approach uses an empirical state-space model, in which inputs are explicitly defined as the average temperature of operation and the output of an external module that detects self-recharge phenomena, on the other hand the output is a function that relates the current SOH and temperature with the Usable Capacity in that cycle. In addition, this approach allows to deal with data losses and outliers. In order to correct erroneous initial conditions in state estimates, an Outer Feedback Correction Loop is implemented. Finally, this framework is validated using degradation data from four sources: experimental degradation data from two Li-Ion 18650 cells, accelerated degradation data openly provided by NASA Ames Research Center, and artificially generated degradation data at different ambient temperatures.We present the implementation of a particle-filtering-based framework that estimates the State-of-Health (SOH) and predicts the End-of-Life (EOL) of Lithium-Ion batteries, efficiently incorporating variations of ambient temperature in the analysis. The proposed approach uses an empirical state-space model, in which inputs are explicitly defined as the average temperature of operation and the output of an external module that detects self-recharge phenomena, on the other hand the output is a function that relates the current SOH and temperature with the Usable Capacity in that cycle. In addition, this approach allows to deal with data losses and outliers. In order to correct erroneous initial conditions in state estimates, an Outer Feedback Correction Loop is implemented. Finally, this framework is validated using degradation data from four sources: experimental degradation data from two Li-Ion 18650 cells, accelerated degradation data openly provided by NASA Ames Research Center, and artificially generated degradation data at different ambient temperatures

Frame retransmission using a modified VST-TDMA access protocol in Picocell/WPAN

The VST-TDMA access protocol has shown to have many advantages over existing protocols in centralized (star) wireless network architectures. As most data-link layer protocols, it does not have a recovery feature as data integrity is supervised by the connection-oriented transport protocols. Nevertheless, VST-TDMA possesses the flexibility to easily incorporate a retransmission feature that allows the users to experience higher throughput when using applications that use the Transmission Control Protocol, which transports approximately 90% of all Internet traffic. Results show that depending on the wireless link reliability, i.e., the frame loss rate, the throughput can be improved by a factor of ten.The VST-TDMA access protocol has shown to have many advantages over existing protocols in centralized (star) wireless network architectures. As most data-link layer protocols, it does not have a recovery feature as data integrity is supervised by the connection-oriented transport protocols. Nevertheless, VST-TDMA possesses the flexibility to easily incorporate a retransmission feature that allows the users to experience higher throughput when using applications that use the Transmission Control Protocol, which transports approximately 90% of all Internet traffic. Results show that depending on the wireless link reliability, i.e., the frame loss rate, the throughput can be improved by a factor of ten

State-of-charge estimation to improve energy conservation and extend battery life of wireless sensor network nodes

Wireless sensor networks are pervasive systems that continuously demonstrate increase in growth by branching into diverse applications. The state of charge is an indicator that conveys the amount of energy available in the battery, information that contributes to better decision-making and energy-efficient protocols by creating smart cross-layer designs. WSN research trends portray the importance of energy-efficient systems by prioritizing energy efficiency over other arguably equally important aspects as throughput, channel utilization, latency, etc. This demonstrates the impact of improving the energy conservation techniques and extending the battery life of the sensor nodes. By using Bayesian inference, more specifically particle filtering, it is shown that the state of charge can be accurately estimated within the linear region of the voltage-SOC curve. Battery discharge experiments are compared to simulations of the voltage-SOC evolution behavior using a state-space representation model, which showed good agreement between the results. The SOC estimation obtained by the particle filter yields essential information that can, and should, be incorporated into MAC protocols.Wireless sensor networks are pervasive systems that continuously demonstrate increase in growth by branching into diverse applications. The state of charge is an indicator that conveys the amount of energy available in the battery, information that contributes to better decision-making and energy-efficient protocols by creating smart cross-layer designs. WSN research trends portray the importance of energy-efficient systems by prioritizing energy efficiency over other arguably equally important aspects as throughput, channel utilization, latency, etc. This demonstrates the impact of improving the energy conservation techniques and extending the battery life of the sensor nodes. By using Bayesian inference, more specifically particle filtering, it is shown that the state of charge can be accurately estimated within the linear region of the voltage-SOC curve. Battery discharge experiments are compared to simulations of the voltage-SOC evolution behavior using a state-space representation model, which showed good agreement between the results. The SOC estimation obtained by the particle filter yields essential information that can, and should, be incorporated into MAC protocols

Spatio-temporal probabilistic modeling based on Gaussian mixture models and neural gas theory for prediction of criminal activity

Criminal risk models are used to assist security forces both in the identification of zones with high of criminal activity for better resource allocation and prediction of future criminal events for the prevention of new crimes. In this sense, spatio-temporal models are widely employed by their capacity of characterizing the criminal risk inside of a zone of interest and updating the model to new crime data. This paper improves an existing method based on spatio-temporal probabilistic risk functions. The spatial probabilistic characterization uses geo-referenced information of criminal incidents related to public services to approximate a risk function based on a Gaussian Mixture Model (GMM). The temporal characterization is supported by Importance Sampling methods and Neural Gas theory to incorporate the information from new measurements, in a recursive manner, updating the spatial probabilistic risk function. Finally, we propose a prediction scheme for criminal activity that also uses Neural Gas Theory, in conjunction with hypothetical future criminal events sampled from a GMM that characterizes the spatial distribution associated with recent criminal activity. The time index related to each hypothetical future crime event is probabilistically characterized using an exponential distribution. Results using real data and the defined performance indexes show an improvement both in the temporal updating as well as the proposed prediction approach.Criminal risk models are used to assist security forces both in the identification of zones with high of criminal activity for better resource allocation and prediction of future criminal events for the prevention of new crimes. In this sense, spatio-temporal models are widely employed by their capacity of characterizing the criminal risk inside of a zone of interest and updating the model to new crime data. This paper improves an existing method based on spatio-temporal probabilistic risk functions. The spatial probabilistic characterization uses geo-referenced information of criminal incidents related to public services to approximate a risk function based on a Gaussian Mixture Model (GMM). The temporal characterization is supported by Importance Sampling methods and Neural Gas theory to incorporate the information from new measurements, in a recursive manner, updating the spatial probabilistic risk function. Finally, we propose a prediction scheme for criminal activity that also uses Neural Gas Theory, in conjunction with hypothetical future criminal events sampled from a GMM that characterizes the spatial distribution associated with recent criminal activity. The time index related to each hypothetical future crime event is probabilistically characterized using an exponential distribution. Results using real data and the defined performance indexes show an improvement both in the temporal updating as well as the proposed prediction approach

State-of-charge estimation to improve decision making by MAC protocols used in WSNs

Energy conservation is a topic of great interest in wireless sensor networks (WSNs). Various techniques have been proposed to minimise the energy consumption. One approach is to design medium access control (MAC) protocols capable of adjusting the sensor node cycle according to the available energy in the battery. The state of charge (SOC) is an indicator of the available energy stored in the battery before discharging. This work proposes a simplified battery model to estimate the SOC and compares the accuracy and computational load of the algorithm as metrics for the implementation of the MAC protocol design.Energy conservation is a topic of great interest in wireless sensor networks (WSNs). Various techniques have been proposed to minimise the energy consumption. One approach is to design medium access control (MAC) protocols capable of adjusting the sensor node cycle according to the available energy in the battery. The state of charge (SOC) is an indicator of the available energy stored in the battery before discharging. This work proposes a simplified battery model to estimate the SOC and compares the accuracy and computational load of the algorithm as metrics for the implementation of the MAC protocol design

Characterization of the degradation process of lithium-ion batteries when discharged at different current rates

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)

Improvements of Energy-Efficient Techniques in WSNs: A MAC-Protocol Approach

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 paper 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, 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.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 paper 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, 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