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

Rationale and design of the Coronary Microvascular Angina Cardiac Magnetic Resonance imaging (CorCMR) diagnostic study: the CorMicA CMR sub-study

Introduction: Angina with no obstructive coronary artery disease (ANOCA) is a common syndrome with unmet clinical needs. Microvascular and vasospastic angina are relevant but may not be diagnosed without measuring coronary vascular function. The relationship between cardiovascular magnetic resonance (CMR)-derived myocardial blood flow (MBF) and reference invasive coronary function tests is uncertain. We hypothesise that multiparametric CMR assessment will be clinically useful in the ANOCA diagnostic pathway. Methods/analysis: The Stratified Medical Therapy Using Invasive Coronary Function Testing In Angina (CorMicA) trial is a prospective, blinded, randomised, sham-controlled study comparing two management approaches in patients with ANOCA. We aim to recruit consecutive patients with stable angina undergoing elective invasive coronary angiography. Eligible patients with ANOCA (n=150) will be randomised to invasive coronary artery function-guided diagnosis and treatment (intervention group) or not (control group). Based on these test results, patients will be stratified into disease endotypes: microvascular angina, vasospastic angina, mixed microvascular/vasospastic angina, obstructive epicardial coronary artery disease and non-cardiac chest pain. After randomisation in CorMicA, subjects will be invited to participate in the Coronary Microvascular Angina Cardiac Magnetic Resonance Imaging (CorCMR) substudy. Patients will undergo multiparametric CMR and have assessments of MBF (using a novel pixel-wise fully quantitative method), left ventricular function and mass, and tissue characterisation (T1 mapping and late gadolinium enhancement imaging). Abnormalities of myocardial perfusion and associations between MBF and invasive coronary artery function tests will be assessed. The CorCMR substudy represents the largest cohort of ANOCA patients with paired multiparametric CMR and comprehensive invasive coronary vascular function tests. Ethics/dissemination: The CorMicA trial and CorCMR substudy have UK REC approval (ref.16/WS/0192). Trial registration number: NCT03193294

Low-dose alteplase during primary percutaneous coronary intervention according to ischemic time

Background: Microvascular obstruction affects one-half of patients with ST-segment elevation myocardial infarction and confers an adverse prognosis. Objectives: This study aimed to determine whether the efficacy and safety of a therapeutic strategy involving low-dose intracoronary alteplase infused early after coronary reperfusion associates with ischemic time. Methods: This study was conducted in a prospective, multicenter, parallel group, 1:1:1 randomized, dose-ranging trial in patients undergoing primary percutaneous coronary intervention. Ischemic time, defined as the time from symptom onset to coronary reperfusion, was a pre-specified subgroup of interest. Between March 17, 2016, and December 21, 2017, 440 patients, presenting with ST-segment elevation myocardial infarction within 6 h of symptom onset (<2 h, n = 107; ≥2 h but <4 h, n = 235; ≥4 h to 6 h, n = 98), were enrolled at 11 U.K. hospitals. Participants were randomly assigned to treatment with placebo (n = 151), alteplase 10 mg (n = 144), or alteplase 20 mg (n = 145). The primary outcome was the amount of microvascular obstruction (MVO) (percentage of left ventricular mass) quantified by cardiac magnetic resonance imaging at 2 to 7 days (available for 396 of 440). Results: Overall, there was no association between alteplase dose and the extent of MVO (p for trend = 0.128). However, in patients with an ischemic time ≥4 to 6 h, alteplase increased the mean extent of MVO compared with placebo: 1.14% (placebo) versus 3.11% (10 mg) versus 5.20% (20 mg); p = 0.009 for the trend. The interaction between ischemic time and alteplase dose was statistically significant (p = 0.018). Conclusion: In patients presenting with ST-segment elevation myocardial infarction and an ischemic time ≥4 to 6 h, adjunctive treatment with low-dose intracoronary alteplase during primary percutaneous coronary intervention was associated with increased MVO. Intracoronary alteplase may be harmful for this subgroup. (A Trial of Low-Dose Adjunctive Alteplase During Primary PCI [T-TIME]; NCT02257294

The coronary microvascular angina cardiovascular magnetic resonance imaging trial: rationale and design

Background: Coronary microvascular dysfunction may cause myocardial ischemia with no obstructive coronary artery disease (INOCA). If functional testing is not performed INOCA may pass undetected. Stress perfusion cardiovascular MRI (CMR) quantifies myocardial blood flow (MBF) but the clinical utility of stress CMR in the management of patients with suspected angina with no obstructive coronary arteries (ANOCA) is uncertain. Objectives: First, to undertake a diagnostic study using stress CMR in patients with ANOCA following invasive coronary angiography and, second, in a nested, double-blind, randomized, controlled trial to assess the effect of disclosure on the final diagnosis and health status in the longer term. Design: All-comers referred for clinically indicated coronary angiography for the investigation of suspected coronary artery disease will be screened in three regional centers in the United Kingdom. Following invasive coronary angiography, patients with ANOCA who provide informed consent will undergo noninvasive endotyping using stress CMR within 3 months of the angiogram. Diagnostic study: Stress perfusion CMR imaging to assess the prevalence of coronary microvascular dysfunction and clinically significant incidental findings in patients with ANOCA. The primary outcome is the between-group difference in the reclassification rate of the initial diagnosis based on invasive angiography versus the final diagnosis after CMR imaging. Randomized, controlled trial: Participants will be randomized to inclusion (intervention group) or exclusion (control group) of myocardial blood flow to inform the final diagnosis. The primary outcome of the clinical trial is the mean within-subject change in the Seattle Angina Questionnaire summary score (SAQSS) at 6 months. Secondary outcome assessments include the EUROQOL EQ-5D-5L questionnaire, the Brief Illness Perception Questionnaire (Brief-IPQ), the Treatment Satisfaction Questionnaire (TSQM-9), the Patient Health Questionnaire-4 (PHQ-4), the Duke Activity Status Index (DASI), the International Physical Activity Questionnaire- Short Form (IPAQ-SF), the Montreal Cognitive Assessment (MOCA) and the 8-item Productivity Cost Questionnaire (iPCQ). Health and economic outcomes will be assessed using electronic healthcare records. Value: To clarify if routine stress perfusion CMR imaging reclassifies the final diagnosis in patients with ANOCA and whether this strategy improves symptoms, health-related quality of life and health economic outcomes. Clinicaltrials.gov: NCT0480581

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

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

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