Estimation Of Core Temperature Research Articles

Core temperature estimation of lithium-ion battery based on numerical model fusion deep learning

Temperature has a critical impact on the lifespan and safety of lithium batteries. This paper proposes a battery core temperature estimation method based on numerical model fused with long short-term memory (LSTM) neural network. The proposed technique extracts features from the numerical model, estimates the volume-averaged temperature by electrochemical impedance spectroscopy (EIS), uses an LSTM neural network to learn thermodynamic parameters and complex calculations, which takes advantage of the strengths of each method, and achieves accurate core temperature estimation. The effects of state of charge (SOC) and temperature on EIS are explored, impedance properties are selected on the criteria of robustness and rapidity, and the estimation of the volume-averaged temperature is achieved using the imaginary part of the impedance. The proposed method can achieve root mean squared error (RMSE) of less than 0.28 °C and mean absolute error (MAE) of less than 0.23 °C. The proposed method has advantages of high estimation accuracy and does not require an electrothermal model. It also considers the effect of ambient temperature and has a good generalization capability.

ALMA-IMF

The stellar initial mass function (IMF) is critical to our understanding of star formation and the effects of young stars on their environment. On large scales, it enables us to use tracers such as UV or Hα emission to estimate the star formation rate of a system and interpret unresolved star clusters across the Universe. So far, there is little firm evidence of large-scale variations of the IMF, which is thus generally considered “universal”. Stars form from cores, and it is now possible to estimate core masses and compare the core mass function (CMF) with the IMF, which it presumably produces. The goal of the ALMA-IMF large programme is to measure the core mass function at high linear resolution (2700 au) in 15 typical Milky Way protoclusters spanning a mass range of 2.5 × 103 to 32.7 × 103 M⊙. In this work, we used two different core extraction algorithms to extract ≈680 gravitationally bound cores from these 15 protoclusters. We adopted a per core temperature using the temperature estimate from the point-process mapping Bayesian method (PPMAP). A power-law fit to the CMF of the sub-sample of cores above the 1.64 M⊙ completeness limit (330 cores) through the maximum likelihood estimate technique yields a slope of 1.97 ± 0.06, which is significantly flatter than the 2.35 Salpeter slope. Assuming a self-similar mapping between the CMF and the IMF, this result implies that these 15 high-mass protoclusters will generate atypical IMFs. This sample currently is the largest sample that was produced and analysed self-consistently, derived at matched physical resolution, with per core temperature estimates, and cores as massive as 150 M⊙. We provide both the raw source extraction catalogues and the catalogues listing the source size, temperature, mass, spectral indices, and so on in the 15 protoclusters.

Core body temperature estimation model with thermal contact resistance compensation

Continuous and accurate monitoring of core body temperature (CBT) is crucial for personal healthcare, clinical disease diagnosis, and enhancing athletic performance. Current CBT measurement methods typically ignore contact interface variation at the sensor-body interface, resulting in insufficient CBT measurement accuracy under daily wearable scenarios with varying sensor contact pressure. This study proposed a core temperature estimation model with thermal contact resistance (TCR) compensation, introducing the TCR factor into the computational equation to improve estimation accuracy in daily wearable scenarios. A numerical simulation of the temperature field between the human forehead and sensor was developed for model performance evaluation. Given the inconvenience of TCR measurement, this study proposed a method based on pressure/optical photoplethysmography (PPG) signals for updating thermal contact resistance. The mean absolute error of CBTER model under varying thermal contact resistance conditions was 0.26 °C, which was 27 % compared to traditional model errors in simulation. Using pressure updating methods, the result of human trials was 0.01±0.23 °C (with 95 % limits of agreement). Overall, the proposed method effectively compensated for errors in core body temperature estimation due to thermal contact resistance, holding potential for continuous core body temperature monitoring to meet healthcare needs scenarios.

Assessing the validity of a novel core temperature monitoring tool at rest and during strenuous physical activity

Core temperature information is important for guiding prevention and treatment measures. Developing new physiological monitoring tools that provide reliable core temperature information is critical for heat injury prevention. The ThriveHRI sensor system is being developed as an efficient core monitoring tool in a smartwatch platform. The current study compared the ThriveHRI sensor/smartwatch to an Equivital LifeMonitor and a rectal thermistor. This study aimed to determine if the ThriveHRI sensor system provides an accurate and precise estimate of core temperature at rest and during physical activity, representing strenuous occupational tasks at elevated temperatures in healthy adults. Twenty-five healthy, physically active adults (N = 14 males; N = 11 females) between the ages of 19–45 years volunteered. Participants completed multiple rounds of deadlifting and treadmill walking in an environmental chamber set to 43.3°C and 50% relative humidity. Participants alternated between performing deadlifts and walking on the treadmill for 35 minutes. Core temperature was monitored continuously via a Datatherm rectal thermometer, Equivital Eq02+LifeMonitor, and a ThriveHRI heat watch. A significant difference in bias between devices was found for easy walking (t(21) = 5.55, p < 0.001, g = 1.01), deadlift (t(19) = 3.60, p = 0.002, g = 0.73), and treadmill (t(16) = 2.42, p = 0.028, g = 0.60). A significant difference in precision between devices was found for easy walking (t(21) = 4.23, p < 0.001, g = 1.21), but no significant difference in precision between devices was found for deadlift or treadmill (ps ≥ 0.067). This study demonstrates the agreeability between the Equivital EQ02+ LifeMonitor, ThriveHRI sensor, and the rectal thermometer remains consistent as core temperature increases and exposure to a heated environment is sustained.

Validity and reproducibility of the CALERA Research Sensor to estimate core temperature at different intensities of a cycling exercise in the heat

Recent heatwaves have highlighted the importance of accurate and continuous core temperature (TCORE) monitoring in sports settings. For example, accentuated rises in TCORE caused by physical exercises under environmental heat stress increase the risk of heat illnesses. Thus, using valid and reproducible devices is essential to ensure safe sports practice. In this study, we assessed the validity and reproducibility of the Calera Research Sensor (CRS) in estimating the TCORE of male and female participants during cycling exercise in a hot environment. Seven male (age: 36.2 ± 10.1 years) and eight female cyclists (age: 30.1 ± 5.0 years) underwent two identical cycling trials in a dry-bulb temperature of 32 °C and relative humidity of 60%. The protocol consisted of an initial 10-min rest followed by a 60-min exercise comprising 10 min at 20%, 25 min at 55%, and 25 min at 75% of maximal aerobic power, and an additional 25 min of post-exercise recovery. TCORE was recorded simultaneously every minute using a gastrointestinal capsule (TGi) and the CRS (TSENSOR). Bland–Altman analysis was performed to calculate bias, upper (LCS) and lower (LCI) concordance limits, and the 95% confidence interval (95%CI). The maximum acceptable difference between the two devices was predetermined at ±0.4 °C. A mixed linear model was used to assess the paired differences between the two measurement systems, considering the participants, trials, and environmental conditions as random effects and the cycling stages as fixed effects. An intra-class correlation coefficient (ICC) of 0.98 was recorded when analyzing data from the entire experiment. A non-significant bias value of 0.01 °C, LCS of 0.38 °C, LCI of −0.35 °C, and CI95% of ±0.36 °C were found. When analyzing data according to the participants’ sex, CRS reproducibility was high in both sexes: ICC values of 0.98 and 0.99 were reported for males and females, respectively. CI95% was 0.35 °C in experiments with males and 0.37 °C with females, thereby falling within the acceptable margin of difference. Therefore, CRS was considered valid (compared to TGi) and reproducible in estimating TCORE in both sexes at various intensities of cycling exercise in the heat.

Internal temperature estimation for lithium-ion batteries through distributed equivalent circuit network model

Lithium-ion cells experience significant internal thermal gradients during operation, with a direct impact on their safety, performance, cost and lifetime. The estimation of the internal temperature of cells is therefore particularly important. In this work, a 3D distributed electro-thermal model for internal temperature estimation is developed for a cylindrical cell (LG M50T, NMC811). The model is parameterized and comprehensively validated against experimental data for 21700 cells, including direct core temperature measurements. Multiple types of electrical load are considered, including constant current discharge, pulse discharge, drive cycle and instant discharge/charge switching. The developed model is used to estimate core temperature based on surface temperature measurement. The predictions are shown to have good accuracy at relatively low computational cost. We show that the widely adopted two-node lumped thermal estimation model is increasingly inaccurate for more aggressive discharges, when thermal gradients become higher. Compared to the standard two-node model, the distributed equivalent circuit network model predicts the effects of detailed internal cell structure (electrode, current collector, metal can and tab) and distributed internal heat generation. The results are of immediate interest to cell manufacturers and battery pack designers, while the modelling and parameterization framework is a useful tool for energy storage systems design.

Wearable and ingestible technology to evaluate and prevent exertional heat illness: A narrative review.

Exertional heat illness remains a constant threat to the athlete, military service member, and laborer. Recent increases in the number and intensity of environmental heat waves places these populations at an ever increasing risk and can be deadly if not recognized and treated rapidly. For this reason, it is extremely important for medical providers to guide athletes, service members, and laborers in the implementation of awareness, education, and measures to reduce or mitigate the risk of exertional heat illness. Within the past 2 decades, a variety of wearable technology options have become commercially available to track an estimation of core temperature, yet questions continue to emerge as to its use, effectiveness, and practicality in athletics, the military, and the workforce. There is a paucity of data on the accuracy of many of these newer devices in the setting of true heat stroke physiology, and it is important to avoid overreliance on new wearable technology. Further research and improvement of this technology are critical to identify accuracy in the diagnosis and prevention of EHI.

Improved Coupled Electrothermal Model of Lithium-ion Battery for Accurate Core Temperature Estimation at High Current

Improved Coupled Electrothermal Model of Lithium-ion Battery for Accurate Core Temperature Estimation at High Current

Lumped-Mass Model-Based State of Charge and Core Temperature Estimation for Cylindrical Li-Ion Batteries Considering Reversible Entropy Heat

Lumped-Mass Model-Based State of Charge and Core Temperature Estimation for Cylindrical Li-Ion Batteries Considering Reversible Entropy Heat

Termorregulaçāo em corridas de longa distância: Revisāo bibliográfica

The literature review of the present study demonstrates that during physical exercise, the body produces heat through the working muscles, generating thermal stress. Through the analysis of articles mainly from the last five years, we concluded that to maintain stable body temperature, the body uses regulatory mechanisms dependent on environmental, task and personal parameters. In intense exercise and in hot environments, blood flow to the skin can increase significantly, and sweating is also increased. However, non-thermal reflexes during exercise cause the regulation of blood flow in the skin to be different from that at rest. The body adjusts the threshold for dilation of skin blood vessels to a higher core body temperature and modifies sensitivity to this temperature change. Regular training improves these adaptations, allowing athletes to dissipate heat more effectively. This review highlights the physiological mechanisms involved in thermal regulation during exercise, discusses the estimation of core temperature in humans, and presents new nutritional approaches to evaluate parameters to maintain stable core temperature.

Validity of heart rate derived core temperature estimation during simulated firefighting tasks

Rectal core temperature monitoring can help fire services mitigate heat injury but can be invasive and impractical. EQ02 + LifeMonitor provides a non-invasive estimation of core temperature. Therefore, the primary purpose of this study was to determine the validity of the EQ02 + LifeMonitor compared to the gold standard rectal thermometer core temperature assessment, as well as the potential influence of turnout gear on the estimated and physiological strain experienced during these activities. Thirteen participants completed simulated firefighting tasks with and without turnout gear, involving four rounds of a 5-min walk on a treadmill at 2.8 mph/2.5% grade and 20 deadlifts over 5 min in an environmental chamber set to 40.6 °C; 50% humidity. During each trial participants wore both an EQ02 + LifeMonitor and DataTherm II rectal thermometer. The results from the devices were statistically equivalent (p < 0.001), yet there was a statistically significant difference in the value (~ 0.1 °C; p < 0.001). There was a significant effect of devices [p < 0.001] and time [p < 0.001], but no interaction effect [p = 0.70] on core temperature drift. Estimated core temperature was marginally different from that measured via the DataTherm II. The EQ02 on average overestimated core temperature. Heart rate, rating of perceived exertion, and area under the curve of core temperature were significantly elevated due to turnout gear [ps < 0.025], but not core temperature skin temperature, or ventilatory rate [ps > 0.372]. These results suggest the EQ02 + LifeMonitor may be a viable, non-invasive alternative for assessing core temperature compared to rectal temperature monitoring, especially during rigorous, intermittent activities. Turnout gear does however increase heart rate, cumulative core temperature, and perceived exertion. Additionally, the validity of the estimated core temperature is not impacted by the use turnout gear. This is likely due to significant changes in heart rate, which allowed the heart-rate derived estimate of core temperature to remain consistent with changes in DataTherm II rectal temperatures.

SoC-Modified Core Temperature Estimation of Lithium-Ion Battery Based on Control-Oriented Electro-Thermal Model

Lithium-ion batteries and their control technologies are the key points to electrification and intelligence of transportation. Dynamic thermal management is one of the key technologies for intelligent battery management systems. Real-time monitoring of information about the temperature characteristics inside the battery is important for effective and safe thermal management. This paper firstly constructs a distributed control-oriented electro-thermal coupling model which contains multidimensional internal information about the cell. Based on the proposed model, improved parameter identification methods are used to construct offline database of model parameters. The electrical and thermal parameters are identified by applying recursive least squares with variable forgetting factor (VFFRLS) and particle swarm optimization (PSO) separately. Finally, a SoC-modified core temperature estimation method is proposed, which adopts discrete-time nonlinear observer (DNLO) to modify SoC and adapative Kalman filter (AKF) to estimate core temperature. The method takes into account the sensitivity of the output results to nonlinear, time-varying battery systems. The results show that the root-mean-square error (RMSE) of SoC estimation is 1.75 % and the mean absolute error (MAE) is 0.65 % for the proposed temperature method under wide temperature points (-5, 25, 45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> C). The proposed core temperature estimation method possesses better robustness and universality, with RMSE of 0.61 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> C and MAE of 0.56 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^ {\circ }$</tex-math></inline-formula> C. Compared with the open-loop prediction method, the accuracy is improved about 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^ {\circ }$</tex-math></inline-formula> C under extreme loadfiles with uncertainty.

Validity Of Heart-Rate Derived Core Temperature Estimation During Simulated Firefighting

Validity Of Heart-Rate Derived Core Temperature Estimation During Simulated Firefighting

Heat stroke burden and validity of wearable-derived core temperature estimation during elite military training

ABSTRACT Objectives Exertional heat stroke (EHS) remains a significant health concern while training in hot environments, prompting the development of noninvasive wearable technology for monitoring estimated core temperature (ECT). The objective of this study was to review the effectiveness of an ECT device during elite military training by providing a case series of individuals who developed EHS. Methods This is a retrospective study of EHS cases during high-intensity training while wearing the ECT device, Zephyr BioharnessTM. Data was collected from January 2021 through September 2021 at the Air Force Special Warfare Training pipeline in San Antonio, TX. Rectal temperatures of EHS diagnoses, defined by central nervous system (CNS) dysfunction and rectal temperature approaching or >40°C (104°F), were compared to ECT reading via Zephyr BioharnessTM. Incidence rates and psychometric properties were calculated using R package. Results A total of 47,058 daily peak ECT measurements were collected among 1,364 trainees. A total of 499 trainees flagged as potential EHS by Zephyr BioharnessTM reading >39.7°C (103.5°F). The incidence of confirmed EHS was 0.8/1000 person-months. Of the 10 confirmed EHS cases (9 males, 1 female; age = 23.4 ± 2.7 yrs; BMI = 25 ± 2; body fat = 13 ± 5%), 8 trainees had a peak ECT reading below 39.7°C which resulted in a sensitivity of 20%, specificity of 98.9%, positive likelihood ratio (LR) of 18.93 (95% Confidence Interval [CI] 5.5–65.6), and a negative LR of 0.81 (95% CI 0.6–1.1). Conclusion ECT had substantial false positive and negative rates. Further studies are needed to validate this technology in other populations, and the algorithm used in this device needs to be refined to better capture the environmental and physical requirements in the special operations population.

Core Temperature Estimation Method for Lithium-Ion Battery Based on Long Short-Term Memory Model With Transfer Learning

Temperature is a crucial parameter that determines the safety and reliability of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. Estimating LIBs temperature for battery management system state monitoring and thermal control, especially the core temperature (CT), is essential. However, the CT cannot be obtained directly and must be estimated via other measurable variables. To this end, this article proposes a method to estimate the CT of LIBs based on the long short-term memory (LSTM) method combined with transfer learning (TL). Through this method, the relationship between CT and measured variables can be obtained. Moreover, the TL procedure, with its fine-tuning strategy, can substantially reduce the computation burden when used to estimate the CT of other LIBs via a reduced training set. In addition, the applicability of the proposed method is verified via a long period cycle test. The experimental results demonstrate that the proposed method can precisely estimate the LIB CT in ambient temperatures of between −10 °C and 55 °C in highly dynamic driving cycles and aging cycle tests, and the maximum root-mean-square error (RMSE) is 0.3302 °C. The LSTM-TL method has a high accuracy compared with other widely used methods.

Core Temperature Estimation Method for Lithium-ion Battery Based on Deep Learning Method with Particle Swarm Optimization

Core Temperature Estimation Method for Lithium-ion Battery Based on Deep Learning Method with Particle Swarm Optimization

A method for the lifetime sensorless estimation of surface and core temperature in lithium-ion batteries via online updating of electrical parameters

Online temperature estimates are essential to the thermal monitoring and control of battery cells for battery management systems (BMSs). Due to hardware limitations, there has been a surge in interest in sensorless approaches for both surface and core temperatures. On this account, several methods have been proposed in the literature for the coestimation of state of charge (SOC) and temperature via RC-based electrical and thermal models and Extended Kalman Filters (EKFs). However, the stability and reliability of these schemes over the complete cell lifetime, when the effects of battery aging become apparent, have not been addressed thoroughly. In this article, a dual state-parameter estimation is carried out on an enhanced equivalent circuit model to coestimate the SOC and SOH on a commercial nickel-rich, silicon–graphite cell throughout its entire lifetime. A thermal model has been characterized based on the previous electrical model for the estimation of surface and core temperature of the cell. The continuous updating and correction of electrical parameters prove to be critical for temperature estimations to remain accurate in the long run, yielding a root mean square error (RMSE) in surface temperature below 1.2 °C for as long as 800 cycles.

Hybrid Electrical Circuit Model and Deep Learning-Based Core Temperature Estimation of Lithium-Ion Battery Cell

Aiming to improve the reliability and durability of lithium-ion battery (LIB) systems, a hybrid electrical circuit model (ECM) and two-dimensional grid long short-term memory (2-D GLSTM) neural network (NN)-based cell core temperature estimation technique is proposed in this article to leverage their respective strengths. The ECM is used to estimate the total heat generation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> ) inside the cell using the measured physical parameters such as voltage, current, and temperature. Furthermore, the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> and other external measurements are used by the 2-D GLSTM NN (deep learning (DL) approach) to estimate the core temperature of the battery cell. An experimentally validated Kalman filter (KF) and equivalent electrothermal model (EETM) are used to generate a large quantity of training and testing data, under a wide range of dynamic loading and ambient temperature to validate the proposed concept. The results showed that the proposed technique can achieve root-mean-squared-error of less than 1% with unknown test data which is quite satisfactory for practical applications. A comparative study with the state-of-the-art techniques in core temperature estimation showed that the proposed scheme performed better. The highly accurate prediction results under a comprehensive operating condition confirm the reliability and generalization ability with solid robustness to the external uncertainties.

Heat Stroke Burden And Validity Of Wearable-derived Core Temperature Estimation During Elite Military Training

Heat Stroke Burden And Validity Of Wearable-derived Core Temperature Estimation During Elite Military Training

A modified Kalman filter-based model for core temperature estimation during exercise and recovery with/without personal cooling interventions

One of the most important parameters to consider while analysing heat related illness is the human body core temperature. It is therefore required to monitor real-time core temperature in an ambulatory situation for industrial workers, athletes, military personnel, and a variety of other people. As conventional methods of monitoring core temperature are unsuitable for ambulatory use or prohibitively expensive, it is necessary to develop a reliable, inexpensive and non-invasive method for real-time measurement of core temperature. Kalman filter based models for core temperature estimation are found promising to meet these requirements. In this work, we have highlighted one of the major shortcomings of the existing extended Kalman filter model and proposed a modification. The modified extended Kalman filter based model has better performance compared to the existing model in estimating the core temperature as it considers two separate measurement models for exercise and recovery periods. The proposed modified model is validated using human trials data which includes participants wearing regular clothing and different types of personal cooling clothing. It is found that the proposed modified model can predict core temperature more accurately (bias= −0.03 to −0.06°C, LoA=±0.14 – ±0.32°C, and RMSE=0.09–0.16°C) as compared to the existing model (bias=−0.03 – 0.08°C, LoA=±0.26 – ±0.33°C, and RMSE=0.14°C–0.18°C). Personal cooling intervention minimises core temperature rise significantly and thus helps in heat stress mitigation. It is observed that the proposed modified model can estimate core temperature more accurately for cooling intervention cases and intermittent working cases as compared to the existing model. It can be used effectively in an ambulatory setting, but for situations where higher accuracy is required and invasiveness of the method is not a concern, direct contact method of core temperature is always preferred as the proposed model has varying accuracy levels between individuals.