Battery Longevity Research Articles

Approach to patients with Assurity® and Endurity® pacemakers : Recommendations from the nucleus of the Working Group Electrophysiology and Rhythmology of the German Cardiac Society

In asubset of patients with pacemaker models Assurity® and Endurity® (Abbott, Sylmar, California, USA; worldwide outside the USA approximately 83,000devices), amistake during production may have occurred resulting in insufficient adhesion between the pulse generator header and device housing which in turn may allow moisture to enter the header. This may cause loss of telemetry, switch to back-up mode, reduced battery longevity, or in worst case loss of pacing. Until June 2022, these malfunctions were reported for 128 devices worldwide (0.15%); no permanent harm to patients due to this issue has been reported. The nucleus of the AGEP suggests the following recommendations: (1)Patients with adevice under safety advisory should be informed. (2)The risks for the patient in case of loss of stimulation should be assessed. Patients should be categorized into "likely pacemaker-dependent" (e.g., indication permanent atrioventricular [AV] block, no intrinsic rhythm at the last follow-up, percentage of ventricular pacing in the device memory > 90%), "unknown", or "likely not pacemaker-dependent" (e.g., indication sick sinus syndrome, intrinsic rhythm > 50 bpm at the last follow-up, percentage of ventricular pacing in the device memory < 1%). (3)In likely pacemaker-non-dependent patients, information about this issue should be provided together with an unchanged follow-up or afollow-up with shortened intervals (e.g., every 3months) and/or remote monitoring. (4)In patients with unknown risk if stimulation failure occurs, at least follow-up intervals should be shortened to, for example, 3months and/or remote monitoring should be initialized. In addition, risks and benefits of adevice exchange should be weighed against each other. (5)In patients who are likely pacemaker-dependent, device exchange is recommended.

Smart Energy Management Strategy for Microgrids Powered by Heterogeneous Energy Sources and Electric Vehicles’ Storage

The rapid growths of power demand and renewable resources have led to numerous challenges. Constructing more resilient microgrids (MGs) provides an opportunity to avoid dependency on the main grid. This article proposes an innovative Energy Management Strategy (EMS) for microgrids that uses non-conventional energy sources such as solar power, wind power, and the storage of electric vehicles (EVs). Numerous studies have been published on MG EMSs using storage; however, in real-time scenarios, predominant factors limit their straightforward implementation. In this article, an attempt is made to address key aspects of EV storage exploitation to support MGEMSs. Minimizing the total MG energy cost is the key objective, considering EV battery longevity and technical limitations. The proposed EMS was implemented in three layers: Optimal Storage Distribution (OSD), Optimal Power Usage (OPU), and EV Selection (EVS). A novel probabilistic approach was implemented in the EVS process (using a Fuzzy Logic Controller (FLC)) to minimize battery degradation. Various case studies were analyzed in a grid-connected MG by implementing the proposed EMS.

Towards Efficient Thermal Management within Intercalation Batteries through Electrolyte Convection

Ubiquitous in consumer electronics and emergent in transportation and stationary applications, lithium-ion batteries (LIB) are the state-of-the-art energy storage technology due to their energy density, roundtrip efficiency, and cycle life.1,2 While the past decade has seen a steady decline in battery price and concomitant increase in energy density due to a combination of materials development, manufacturing advances, and market scale,3,4 current LIBs are still unable to meet the often incongruous power and energy requirements of newer applications (e.g., fast charging of energy-dense batteries).5,6 In addition, these more extreme operating environments challenge battery longevity and safety, necessitating responsive balance-of-plant systems which include thermal management systems that control cell temperatures using heat transfer media. At elevated temperatures, accelerated solid-electrolyte interphase growth and component decomposition may lead to capacity/power fade,7,8 and, in the worst cases, thermal runaway and hazardous releases. Within the battery cell, temperature gradients lead to non-uniform electrode reaction distribution, and subsequently reduced cell performance and cycle life.8 Thus, typical LIB operating temperature ranges are constrained between 20 ℃ and 40 ℃, with minimal temperature differences across the cell. Most current thermal management systems rely on heat exchange through the surface or tab of the cell with a cooling media (air, liquid, phase-change materials, etc.).9,10 While generally sufficient under many of today’s applications (low C-rates), this approach can be challenged by newer applications, such as those that require high power input/output (EV fast charging, electric aviation), or need large battery formats (stationary storage systems).In this presentation, we will describe a novel concept of thermal management through forced convection of the electrolyte through the porous electrodes and separator. By leveraging battery simulation and dimensional analysis, we demonstrate that: (1) electrolyte convection provides efficient heat removal capability by carrying the generated heat out of the cell through the flowing medium; (2) the elimination of electrolyte concentration gradient by flow, and the resulting smaller ohmic resistance, concentration and activation overpotentials, help prevent cell temperature rise through reduced heat generation rate. Compared to current thermal management systems, this approach offers several important potential advantages, including (1) reduced internal temperature gradient, (2) rapid response time to temperature regulation, (3) simplifications to manufacturing, and ultimately, and (4) reduced system costs and improved battery safety.

Real-world long-term battery longevity of leadless pacemakers

Abstract Background Leadless pacemakers (LPs) have proven safe on the mid-term. Long-term safety will largely depend on the need for replacements with a concomitant risk of complications. As replacements are mainly due to battery depletion, battery longevity is, in essence, an important determinant of long-term safety. Mean battery longevity of the most often implanted LP (Micra VR) predicted by the manufacturer is 12 years, but mid- and long-term data is lacking. Purpose We sought to determine the long-term battery longevity of this LP in a real-world cohort. Methods Consecutive patients with an LP implantation at our tertiary hospital from January 2014 to September 2021 were included in a prospective cohort. Baseline characteristics and electrical parameters during all yearly follow-up visits were assessed. Results 105 patients (73 [70%] male, age at implantation 80±9 years) were included. Pacing indications were atrial fibrillation with slow conduction (34 [32%]), third degree AV block (35 [33%]), incomplete AV block (12 [11%]), sinus node disease (23 [22%]) and unexplained syncope (1 [1%]). At implantation, pacing threshold, impedance and R-wave amplitude were 0.76±0.64V at 0.24ms, 790±220Ω and 11.1±4.9mV, respectively, lower rate was programmed at 50–60/min in 88 (84%), &amp;lt;50/min in 8 (7.6%) and &amp;gt;60/min in 9 (8.6%) and rate-responsive pacing was programmed on in 51 (49%). Median follow-up was 25 months (range 2–88 months). Pacing percentage was 46% (IQR 12–98%) at year 0; 64% (10–99%) at year 5 and 91% (56–100%) at year 7. No battery failures were seen during follow-up. At 3 years, expected battery longevity was &amp;gt;8 years (maximum value) in nearly all patients (49/50 [98%]) and at 5 years in the majority (16/18 [89%]) of patients; at 7 years, expected battery longevity ranged from 4.1 to &amp;gt;8 years (Figure). This implies an expected battery longevity of &amp;gt;13 years in the majority of patients. In two subjects, expected battery longevity was below the maximum value within 5 years after implantation. In the first, latest follow-up visit was after 3 years and expected battery longevity was 3.0 years. An increase in pacing percentage of +70 percent points was seen during follow-up and there was a relatively high pacing capture threshold (1.5V@0.4ms). In the second, the latest follow-up visit was at 6 years, and expected battery longevity was 1.9 years. This was caused by a large increase in pacing percentage (+80 percentage points) and lower rate of 70/min; pacing threshold was 0.5V@0.24ms. We did not see clear overall differences in expected battery longevity at 5 years, when the patients were stratified per quartile pacing percentage. Of 34 deaths during follow-up, 31 (91%) had expected battery longevity &amp;gt;8 years at the last follow-up visit. Conclusions These long-term real-world data suggest that the battery longevity of the Micra LP will exceed the expected battery longevity. Data from larger registries is necessary to confirm these results. Funding Acknowledgement Type of funding sources: None.

Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques

With rapid development of clean energy vehicles, the health diagnosis and prognosis of lithium batteries remain challenging for practical applications. Accurate state-of-health (SOH) and remaining useful life (RUL) estimation provides crucial information for improving the safety, reliability and longevity of batteries. In this paper, a fusion of deep-learning model and feature signal analysis methods are proposed to realize accurate and fast estimation of the health conditions for battery cathodes. Specifically, the long short-term memory (LSTM) network and differential thermal voltammetry (DTV) are utilized to verify our fusion method. Firstly, the DTV feature signal analysis is executed based on battery charging and discharging data, based on which useful feature variables are extracted with Pearson correlation analysis. Next, the deep-learning model is constructed and trained with the LSTM as the core based on timeseries datasets constructed with features. Finally, the validation and error analysis of proposed model are provided, showing a max mean absolute error of 0.6%. The proposed method enables highly accurate models for SOH and RUL estimation that can be potentially deployed on cloud-end for offline battery degradation tracking.

Weight optimization and finite element analysis of automobile leaf spring: A design construction referred to electric vehicle

In the current era, an automobile vehicle company’s major focus is now on conserving natural resources, reducing consumption of fuel, and reducing the weight of vehicle components. To reduce greenhouse gas emissions and fuel consumption and mitigate global warming, an electric vehicle will be a viable option. However, the EV’s main issue is the vehicle’s battery longevity. More power is necessary to drive a vehicle. The best solution to the issue is to reduce the vehicle component weight. Electric Vehicle suspension systems are another sector in which these developments are performed on a regular basis. Substantial steps are being taken to improve the vehicle ride quality. In this study, static and dynamic analysis has been carried out by finite element analysis at Ansys workbench and taken structural steel material for mono leaf spring. In composite material, carbon fiber and glass fiber have been considered as reinforcement and resin epoxy has been taken as matrix for mono leaf spring. The composite material fabrication has been accomplished layer by layer at Ansys ACP. After analysis, this study has revealed that by using composite material a magnificent amount of weight reduction has been achieved without sacrificing load-bearing capability, strength, and rigidity.

O066 / #897 COST EFFICIENCIES FOR SPINAL CORD STIMULATION IN THE UNITED STATES: IMPACT OF BATTERY LONGEVITY FOR RECHARGE-FREE DEVICES: TRACK 4: CLINICAL USE OF SCS / DRG

With the advent of new recharge-free spinal cord stimulation (RF-SCS) devices battery longevity continues to improve. Extension of battery life is of particular importance for patients selected for RF-SCS. This study modeled the cost potential savings to the Medicare (US public payer perspective) associated with an RF-SCS battery life under specific programming parameters.

Buffering Volume Change in Solid-State Battery Composite Cathodes with CO2-Derived Block Polycarbonate Ethers.

Polymers designed with a specific combination of electrochemical, mechanical, and chemical properties could help overcome challenges limiting practical all-solid-state batteries for high-performance next-generation energy storage devices. In composite cathodes, comprising active cathode material, inorganic solid electrolyte, and carbon, battery longevity is limited by active particle volume changes occurring on charge/discharge. To overcome this, impractical high pressures are applied to maintain interfacial contact. Herein, block polymers designed to address these issues combine ionic conductivity, electrochemical stability, and suitable elastomeric mechanical properties, including adhesion. The block polymers have “hard-soft-hard”, ABA, block structures, where the soft “B” block is poly(ethylene oxide) (PEO), known to promote ionic conductivity, and the hard “A” block is a CO2-derived polycarbonate, poly(4-vinyl cyclohexene oxide carbonate), which provides mechanical rigidity and enhances oxidative stability. ABA block polymers featuring controllable PEO and polycarbonate lengths are straightforwardly prepared using hydroxyl telechelic PEO as a macroinitiator for CO2/epoxide ring-opening copolymerization and a well-controlled Mg(II)Co(II) catalyst. The influence of block polymer composition upon electrochemical and mechanical properties is investigated, with phosphonic acid functionalities being installed in the polycarbonate domains for adhesive properties. Three lead polymer materials are identified; these materials show an ambient ionic conductivity of 10 –4 S cm–1, lithium-ion transport (tLi+ 0.3–0.62), oxidative stability (>4 V vs Li+/Li), and elastomeric or plastomer properties (G′ 0.1–67 MPa). The best block polymers are used in composite cathodes with LiNi0.8Mn0.1Co0.1O2 active material and Li6PS5Cl solid electrolyte–the resulting solid-state batteries demonstrate greater capacity retention than equivalent cells featuring no polymer or commercial polyelectrolytes.

Feasibility of leadless left ventricular septal pacing with the WiSE-CRT system to target the left bundle branch area: A porcine model and multicenter patient experience

The WiSE-CRT system delivers leadless endocardial left ventricular (LV) pacing to achieve cardiac resynchronization therapy. The electrode is conventionally placed on the lateral wall, but implanting on the LV septum may have advantages, including capture of the left bundle branch, and improved battery longevity owing to reduced distance from the transmitter. The purpose of this study was to assess the feasibility of leadless LV septal pacing via the WiSE-CRT system. Two pigs underwent electrode implantation on the LV septum with subsequent anatomical and histological examination. Eight patients underwent implantation of the WiSE-CRT system with deployment of the electrode on the LV septum via an interatrial transseptal approach. Deployment of the electrode on the LV septum was successful in both animals. Histological examination demonstrated electrode tines in close proximity to Purkinje tissue. WiSE-CRT implantation with an LV septal electrode was successful in all patients. Biventricular capture was confirmed, with a significant reduction in QRS duration (187.1 ± 33.8 ms vs 149.5 ± 15.7 ms; P = .009). Temporary LV pacing achieved further QRS reduction (139.8 ± 12.4 ms), and in 4 patients the peak LV activation time in lead V5/V6 was <90 ms, suggesting left bundle branch capture. At early follow-up, the median LV pacing percentage was 98.5% and 5 patients (62.5%) improved symptomatically. The transmitter-to-electrode distance was lower than the distance to the lateral wall during acoustic window screening (8.8 ± 1.6 cm vs 11.9 ± 1.5 cm; P = .002). Leadless LV septal pacing with the WiSE-CRT system to target the left bundle branch appears feasible. Further study is required to assess the efficacy and safety of this technique.

MgO Heterostructures: From Synthesis to Applications.

The energy storage capacity of batteries and supercapacitors has seen rising demand and problems as large-scale energy storage systems and electric gadgets have become more widely adopted. With the development of nano-scale materials, the electrodes of these devices have changed dramatically. Heterostructure materials have gained increased interest as next-generation materials due to their unique interfaces, resilient structures and synergistic effects, providing the capacity to improve energy/power outputs and battery longevity. This review focuses on the role of MgO in heterostructured magnetic and energy storage devices and their applications and synthetic strategies. The role of metal oxides in manufacturing heterostructures has received much attention, especially MgO. Heterostructures have stronger interactions between tightly packed interfaces and perform better than single structures. Due to their typical physical and chemical properties, MgO heterostructures have made a breakthrough in energy storage. In perpendicularly magnetized heterostructures, the MgO’s thickness significantly affects the magnetic properties, which is good news for the next generation of high-speed magnetic storage devices.

Constructing Favorable Microstructures in Solid-State Organic Cathodes Via Mechanical Property Manipulation

Organic electrode materials, due to their low environment footprint and high material-level specific capacities, have become competitive alternatives to inorganic materials for solid-state batteries (SSB) in recent years. Additionally, the soft nature of organic compounds ensures consistent and intimate contact with solid sulfide electrolytes during cycling, which is beneficial for battery longevity. However, the low-modulus organic materials and high-modulus sulfide electrolytes, upon mixing and compression, would form unfavorable composite microstructure where sulfide particles are encapsulated by organics and cannot form an efficient ion conduction path. This mismatch in mechanical property prevents a high fraction of organic compounds to be used in a solid-state cathode, limiting the energy density of organic solid-state batteries.Here we report the formation of favorable microstructures of organic cathodes by “softening” the sulfide electrolytes. Solvent treatment of the sulfide electrolyte Li6PS5Cl more than halves its modulus from 28.6 ± 8.5 GPa to 16.0 ± 1.6 GPa. Thermal gravimetric analysis, Raman spectroscopy, and X-ray diffraction were used to elucidate the evolution of the sulfide electrolyte during the softening and recovering process. The organic cathode formed by mixing organic materials with this softened electrolyte shows a favorable microstructure where the electrolyte forms a percolated domain. As a result, the utilization of an organic material, pyrene-4,5,9,10-tetraone (PTO), is increased by 133.6% and 90.8% compared with cells with a re-hardened and the pristine Li6PS5Cl, respectively. The mass fraction of PTO can be improved from 20 to 40wt% while maintaining high utilization (85.7%). Our exploration of softened electrolyte builds the correlation among structure, mechanical property, microstructure engineering and battery performance, and the strategy is applicable to other active materials with low modulus.

A Low-Complexity and High-Resolution Beamformer for Portable Medical Ultrasound Imaging.

So far, researchers have proposed various methods to improve the quality of medical ultrasound imaging. However, in portable medical ultrasound imaging systems, features, such as low cost and low power consumption for battery longevity, are very important. Hence, most of the proposed algorithms have not been proper substitutes for the delay and sum (DAS) algorithm in portable clinical applications due to their high computational complexity and cost. In this article, a new algorithm is presented concentrating on reducing the computational complexity based on a technique that separates the signal from the correlated interferences to overcome the negative characteristics, particularly for portable applications such as high price, high power consumption, and off-axis clutters in the azimuth direction. Also, the proposed algorithm yields a higher contrast compared to that of the DAS algorithm while achieving a similar computation complexity order of O ( n ) similar to the DAS algorithm. Furthermore, the performed simulations confirm that the proposed method is able to achieve a better resolution almost twice as that of the filtered delay multiply and sum (F-DMAS) algorithm with the same sidelobe level.

Towards Long Lifetime Battery: AI-Based Manufacturing and Management

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.

A leadless pacemaker matched with a vasovagal syncope: how long can it last?

Guidelines recommend that cardiac pacing should be considered in patients suffering from frequent vasovagal syncopal (VVS) episodes. Studies have demonstrated the safety and efficacy of leadless pacemakers (LP) in cardioinhibitory vasovagal populations specifically, rendering them a reasonable alternative to transvenous pacing in these patients. However, due to the paucity of data on extraction and the number of concomitant LPs that can be safely implanted, there are concerns regarding LPs' battery longevity, especially in younger patients who may require decades of pacing therapy. This is a retrospective analysis of the first 100 LPs implanted at a tertiary cardiac centre in the UK. Demographical data and device parameters at implant and follow-ups were obtained from the hospital's medical records. The battery life of the LPs in the VVS patients was compared to that of patients with other pacing indications. Ninety patients were included in the analysis. 14 patients (15.6%) had VVS, and 76 patients (84.4%) had other indications for pacing. Mean ages were 34 ± 13 years and 62 ± 20 years for the VVS and the other group, respectively. The estimated total battery life was 15.22 ± 0.35 and 13.65 ± 2.97 years in the VVS and the other indications group respectively (p=.04). There were no complications in the VVS group. LPs provide a promising treatment for patients with vasovagal syncope with reassuring battery performance at the short/intermediate term. Further longer-term follow-up data are needed to identify the true battery potential in this patient cohort.

Residual Life Prediction of Lithium Batteries Based on Data Mining.

Lithium-ion batteries are an important part of smartphones, and their performance has a great impact on the life of the phone. The longevity of lithium-ion batteries is key to ensuring their reliability and extending their useful life. This paper built a lithium battery life prediction model and grey model MDGM(1,1) based on data mining. Then, experimental data were selected for testing, and the prediction error reached 10.5% at the minimum. It showed that the prediction model had higher precision and could provide help for the prediction and development of mobile phone battery life.

Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle

The driving trip pattern is of great significance in hydrogen consumption and battery Longevity of the plug-in fuel cell hybrid electric vehicles (PFCHEV). However, the traditional energy management strategy failed to consider the uncertainty of driving patterns. To overcome this drawback, a deep Q-learning network based trip pattern adaptive (DQN-TPA) battery longevity-conscious strategy is proposed in this study. To begin with, the trip pattern recognition based Learning Vector Quantization Neural Network is devised for pattern identification, and the adaptive-equivalent consumption minimizes strategy (A-ECMS) is conducted to improve the hydrogen consumption. Then, a TPA longevity-conscious strategy is developed and compared with the conventional multi-criteria (MC) optimization strategy to investigate the discrepancy brought by the pattern adaptation. And finally, in combination with the above efforts, an improved DQN-TPA based battery longevity-conscious strategy has been established accordingly. The advances are confirmed by the validation results that, the A-ECMS makes an 11.76% promotion in fuel economy by taking the deviation among different driving patterns into concern. The TPA strategy shows more adaptiveness than the MC optimization strategy in which, the effective Ah-throughput is 5.17% lower than MC-based while keeping the same economy. Further improvement can be achieved by the modified DQN-TPA based approach by remedying the imperfection of TPA-based recognition delay and performing the economy and durability conscious actions with 5.87% further reduction of effective Ah-throughput without observably sacrificing the fuel economy. Furthermore, the effectiveness and adaptiveness of the proposed strategy are validated by the Hardware-in-the-Loop experiments. Both the numerical validation and semi-physical validation results indicate that the DQN-TPA based approach made it possible to develop the battery longevity-conscious strategy capable of significantly adapting various driving patterns and improving the hydrogen consumption and battery durability performance of the PFCHEV.

Clinical Longevity of 106,462 Rechargeable and Primary Cell Spinal Cord Stimulators: Real World Study in the Medicare Population

IntroductionSpinal cord stimulators (SCS) are available with either primary cell (PC) or rechargeable cell (RC) batteries. Although RC systems are proposed to have a battery longevity upward of nine years, in comparison with four years for PC systems, there are few studies of longevity of SCS in the real world. Materials and MethodsThis was an observational, nonrandomized, retrospective study of Medicare beneficiaries who received neurostimulator implants in the outpatient hospital. This study used Medicare fee-for-service claims data from 2013 to 2020. The clinical longevity of the implantable pulse generator (IPG), defined as the duration from implant until removal for any reason, was compared between PC and RC devices. Life distribution analysis was used to approximate device lifespan. The secondary analysis separated removals into explant or replacements. The statistics were adjusted for relevant clinical covariates. ResultsA total of 25,856 PC and 79,606 RC systems were included in the study. At seven years after implant, 53.8% of PC IPGs and 55.0% of RC IPGs remained in use. The life distribution modeling analysis projected a median lifespan of 8.2 years for PC and 9.0 years for RC devices. The rate of explant was lower for PC devices (19.2%) than for RC devices (22.0%, hazard ratio (HR) = 0.96, p = 0.082), whereas the rate of replacements was higher for PC devices (33.7%) than for RC devices (29.5%, HR = 1.31, p < 0.001). An analysis of the battery type used in device replacements showed an increasing adoption of PC devices over time. ConclusionsThis large, retrospective, real-world analysis of Medicare claims data demonstrated that the clinical longevity of neurostimulator devices is similar for PC and RC batteries. In the past, clinicians may have defaulted to RC devices based on the assumption that they provided extended battery life. Considering this longevity data, clinicians should now consider the choice between PC and RC devices based on other individual factors pertinent to the patient experience and not on purported longevity claims.

Battery Thermal Management System for Electric Vehicles: A Brief Review

Battery is the heart of an electric vehicle. The global growth of electrification in the automotive market makes improvements in battery health and longevity a vital aspect to consider to accommodate this growing demand. This paper presents a qualitative literature review on different battery thermal management systems (BTMS) for electrified vehicles. Different works in the literature were examined to determine the types of BTMS to be considered and their main characteristics. As a result, we listed different types of BTMS with their main characteristics. This brief review can support the research about battery thermal management systems as a summary of the state-of-the-art on this topic.

Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions

Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and accurate algorithms for State of Health (SoH) monitoring on the Battery Management System. The task is challenging due to the complexity and multitude of the factors contributing to the battery capacity degradation, especially because the different degradation processes occur at various timescales and their interactions play an important role. Data-driven methods bypass this issue by approximating the complex processes with statistical or machine learning models: they rely solely on the available cycling data, while remaining agnostic to the underlying real physical processes. This paper proposes a data-driven approach which is understudied in the context of battery degradation, despite being characterised by simplicity and ease of computation: the Multivariable Fractional Polynomial (MFP) regression. Models are trained from historical data of one exhausted cell and used to predict the SoH of other cells. The data are provided by the NASA Ames Prognostics Center of Excellence, and are characterised by varying loads which simulate dynamic operating conditions. Two hypothetical scenarios are considered: one assumes that a recent observed capacity measurement is known, the other is based only on the nominal capacity of the cell. It was shown that the degradation behaviour of the batteries under examination is influenced by their historical data, as supported by the low prediction errors achieved (root mean squared errors ranging from 1.2% to 7.22% when considering data up to the battery End of Life). Moreover, we offer a multi-factor perspective where the degree of impact of each different factor to ageing acceleration is analysed. Finally, we compare with a Long Short-Term Memory Neural Network and other works from the literature on the same dataset. We conclude that the MFP regression is effective and competitive with contemporary works, and provides several additional advantages e.g. in terms of interpretability, generalisability, and implementability.

A leadless pacemaker matched with a vasovagal syncope: how long can it last?

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Dr.Mohamed ElRefai is receiving an unrestricted grant from Boston Scientific Introduction Although a benign condition, patients suffering from frequent vasovagal syncopal episodes need treatment to improve their quality of life. ESC Guidelines consider pacing for patients &amp;gt;40 years old with documented symptomatic asystolic pauses &amp;gt;3 seconds or asymptomatic pauses &amp;gt;6 seconds. Caution is advised with use of pacing in younger patients, due to long-term complications associated with transvenous pacemakers (TV-PPM). The ESC guidelines recommend that Leadless pacemakers (LP) should be considered as an alternative to TV-PPM when there is no appropriate venous access or if the risk of device infection is high, also on a case-by-case basis using shared decision making. Studies on the use of LPs for cardioinhibitory vasovagal population specifically, concluded that the safety and efficacy of LPs are comparable with TV-PPM, demonstrating a high level of symptom improvement at follow up making them a reasonable option in these patients. In addition, observational studies demonstrated that LPs are associated with better quality of life than TV-PPM. Purpose Current practice is to implant another LP after the end of life of the old device without extracting it. There are concerns regards battery longevity specially with the paucity of data on device extraction and the number of concomitant LPs that can be implanted safely, a concern in younger patients’ population potentially requiring decades of pacing therapy. We aim to evaluate the performance of the LPs in the relatively younger vasovagal syncope patients with anticipated very low pacing dependency, with emphasis on battery longevity. Methods We conducted a retrospective analysis of the first 100 LP implanted at our tertiary referral centre for cardiac devices in the UK. Demographical data as well as devices parameters at implant and latest follow ups were obtained from the patients’ medical electronic records. We compared the performance of the LPs between patients with vasovagal syncope vs other pacing indications with emphasis on battery longevity in both groups. Results A total of 90 patients were included in our analysis after excluding 10 patients due to lack of follow-up data. 14 patients (15.6%) had vasovagal syncope as indication for pacing and 76 patients (84.4%) had other indications. Mean ages were 34±13 years and 62±20 years for the vasovagal and the other group, respectively. There were no complications in the vasovagal group. Pacing % at the last follow up was 1±1 in the vasovagal group and 37±43 for the other group. There was a significant difference in the estimated total battery life between both groups, 15.22±0.35 and 13.65±2.97 years in the vasovagal and the other group respectively. Conclusion LPs provide a safe and a reliable treatment for young patients with vasovagal syncope with promising battery performance at the short/intermediate term. Further longer-term follow up data is needed to identify the true battery potential in this patient cohort.