Prediction Of Core Temperature Research Articles

Benchmarking core temperature forecasting for lithium-ion battery using typical recurrent neural networks

One of the factors hindering the scaled application of lithium-ion batteries is their thermal safety issues. Real-time monitoring of core temperature holds promise in alleviating this concern. However, calibrating individual models for thousands of battery cells poses challenges and leads to big data issues. The maturing and rapidly advancing artificial intelligence technologies hold promise in addressing the task of core temperature prediction. In this study, we conduct a benchmark test using typical recurrent neural networks for core temperature prediction. The test encompasses three main aspects: data acquisition, data processing, and data analysis. Firstly, we conduct dynamic battery tests across a wide temperature range on a custom-built experimental platform. Subsequently, we employ big data techniques for data pre-processing, including filtering, normalization, and sliding window methods. Next, hyperparameter optimization is then performed using the Bayesian optimization with Tree-structured Parzen Estimator, followed by K-fold cross validation. Consequently, we obtain three optimal performing models based on typical recurrent neural networks. Finally, we extensively compare these models in terms of performance and complexity. According to the results, all data-driven methods achieve satisfactory model performance, with RMSE within 0.17 °C and MAE within 0.15 °C. Among the models with similar predictive performance conditions, gated recurrent unit exhibits the lowest model complexity and faster training speed. Meanwhile, we also validate the portability and generalizability of the forecasting models. This study lays the foundation for future research in this area and can provide guidance for engineering practical applications to ensure the safe operation of battery energy storage systems.

Multiobjective Approach to Schedule DAG Tasks on Voltage Frequency Islands

Scheduling a Directed Acyclic Graph (DAG) on voltage frequency islands involves dividing the available processing units into multiple islands with varying voltage and frequency levels, and then mapping the tasks of the DAG to the islands while minimizing the makespan and overall energy consumption. In this research work, a novel DAG task scheduling model is introduced with the assistance acquired from the deep learning paradigm. The proposed model includes four major phases: (a) DAG modelling, (b) Voltage frequency island partitioning, (c) core temperature prediction and (d) scheduling optimization. Initially, the Directed Acyclic Graph (DAG) model is designed. The nodes of DAG represent tasks and the edges represent dependencies between tasks. Then, in the Voltage frequency island partitioning, the available processing units into multiple voltage frequency islands. This can be done based on the power consumption of each unit, and the task requirements. Subsequently, the Recurrent Neural Network (RERNN) is trained to predict the core temperature of each voltage frequency island based on the multi-objectives like execution time, makespan, overall energy consumption. Then, the scheduling of the DAG on the voltage frequency islands is optimized using the Self-Improved Pelican Optimization Algorithm (SI-POA). The proposed SI-POA model is an extended version of the standard POA model. The SI-POA model is inspired by the behavior by the natural behavior of pelicans during hunting. In the scheduling optimization phase, the SI-POA algorithm to optimize the scheduling of the DAG on the voltage frequency islands while taking into account the predicted core temperature of each island based on the Recalling-enhanced recurrent neural network (RERNN) model. The goal is to minimize the makespan and overall energy consumption of the DAG while keeping the core temperature of each island within safe limits.

Enhanced Simulation of Infrared Heating of Thermoplastic Composites Prior to Forming under Consideration of Anisotropic Thermal Conductivity and Deconsolidation by Means of Novel Physical Material Models.

In recent years, thermoplastic composites have found their place in large business sectors and are in direct rivalry to thermoset matrix composites. In order to ensure efficient and lean processes, process modeling gains ever-growing attention. This work shows the computational fluid dynamics (CFD)-modeling of a typical heating step in a thermoforming process of a thermoplastic composite sheet. When heating thermoplastic composites, the heat conduction proceeds anisotropic, and the sheets are subject to thermal deconsolidation when heated above the melting temperature of the polymer matrix adding to the anisotropic effect. These effects are neglected in known process models and this study shows the first successful attempt at introducing them into CFD-modeling of the heating of thermoplastic composite sheets. Thus, the simulation requires temperature dependent values for the anisotropic thermal conductivity and the coefficient of linear thermal expansion, which are calculated with novel physical models which were developed solely for this cause. This alters the behavior of an isotropic CFD-model and allows the successful validation via laboratory experiments using glass fiber reinforced polypropylene (PP/GF) sheets with embedded thermocouples to check the internal temperature distribution when the sheet is heated to the designated forming temperature in a composite thermoforming press. The incorporation of this newly developed process model reduces the error in the core temperature prediction from close to 70 °C to 3 °C at the forming temperature.

Validation of improved Two-node model for prediction of core temperature during bathing

Validation of improved Two-node model for prediction of core temperature during bathing

Optimization and comparison of models for core temperature prediction of mother rabbits using infrared thermography

Core temperature is a measurement used to evaluate and diagnose the health status of mother rabbits. The preferred method is to use an indwelling probe to measure the rectal temperature. This procedure can be stressful to rabbits, are time and labor-consuming. The objective of this paper was to develop the fastest, accurate and non-invasive method to measure the rectal temperature of mother rabbits using Infrared thermography (IRT) and machine learning (ML). Two hundred lactating mother rabbits (LR) and two hundred non-lactating mother rabbits (NLR) of the YPLU breed were used in the experiment and IRT data, rectal temperature and ambient temperature were recorded. The results showed that there was a significant correlation between the maximum infrared temperature of eyes (EMIT) and ears (RMIT) and rectal temperature (p < 0.05) and a significant correlation between the EMIT, RMIT and the ambient temperature (p < 0.01) in two kinds of mother rabbits. Least squares support vector machine (LS-SVM), Gaussian process (GP), and partial least squares (PLS) machine learning methods were used to establish models of mother rabbits’ core temperature. Using the over-sampling method to process the training set, three models of LS-SVM, GP, and PLS with improved generalization performance were obtained. The RMSE for the test set by LS-SVM, GP, and PLS methods were 0.13℃, 0.12℃ and 0.07℃ and the R2 were 0.94, 0.96 and 0.98, respectively. Compared with the results obtained from two other regressions, LS-SVM and GP, the PLS method exhibited the best performance. The prediction error of the test set is <0.2℃, which can better predict the core temperature of the mother rabbits.

Real-time core temperature prediction of prismatic automotive lithium-ion battery cells based on artificial neural networks

For modeling of the non-linear heat generation and thermal effects in Li-ion batteries, artificial neural networks are a great solution to represent the thermal behavior for the battery management system in an electric vehicle. Several studies have proven the high accuracy with large benefits in computing-time and complexity compared to detailed electrochemical–thermal models. Commonly used feedforward networks need to prove suitability for dynamic applications but are always limited due to missing information about the previous time steps or need external sensor information. In this work, a novel Nonlinear AutoRegressive with eXogenous (NARX)-network is developed and parameterized for a large 25Ah prismatic cell. The NARX is compared to a feedforward using the same general structure and input data in terms of training, validation behavior, long-term prediction and dynamic driving application. Both ANN approaches prove to be adequate for the temperature prediction with an accuracy within 1K during long-term prediction of 10h. Additionally, in a BEV application with real-time requirements the thermal models predicting the dynamic temperature behavior with high precision and robustness without even a temperature input in case of the NARX-approach.

Multistep temperature prediction for proactive thermal management on chip multiprocessors

In chip multiprocessors, the thermal hot spot prediction is vital to take proactive thermal management decisions for mitigating its impact on the performance of the application. Core temperature prediction for a range of future time steps is challenging because workload changes dynamically at runtime. This article presents an adaptive multistep temperature prediction (MSTP) algorithm, which uses iterative steps to generate core temperature predictions for a range of future time steps using the initial temperature information and the application workload profile obtained offline. MSTP considers the autocorrelation of temperature and addresses the nonlinear heteroscedasticity across observations. We evaluate the proposed MSTP on a real hardware platform and compare it to the model-based approach for a wide range of parallel benchmarks. Experimental results show that the proposed algorithm achieves more than 96% prediction accuracy and minimizes the heteroscedastic residual variance. In case of thermal hot spot prediction, we show that the performance throttling problem is alleviated using a process migration mechanism with a minimal migration overhead.

Contribution of wetted clothing to body energy exchange and heat stress

Quantifying the impact of clothing thermal and evaporation resistance is essential to providing representative boundary conditions for physiological modeling. In many models, sweat is assumed to drip off the skin surface to the environment and is not captured in clothing. In high metabolic rate and high temperature and humidity conditions the sweat produced by the body has the potential to saturate semipermeable clothing ensembles, changing the assumptions of the model. Workers, athletes and soldiers commonly wear encapsulating versions of such clothing to protect against environmental hazards. A saturated clothing model is proposed based on the ASHRAE two-node model using a saturated spot element in parallel with the existing method to account for sweat absorbed in the clothing. The work uses fundamental heat and mass transfer principles, modifying the existing formula using clothing measurements and basic assumptions. The effectiveness of the model is demonstrated by comparing the predictions of the original and proposed models, to the results of 21 soldiers exercising. The soldiers wore combat pants and shirt, helmet, gloves, shoes, socks, and underwear, and walked in a thermal chamber for 2 h at 42.2 °C dry bulb temperature, 54.4 °C wet bulb temperature, 20% relative humidity, and airspeed of 2 m/s. Core temperature, seven skin temperatures, heart rate, and total sweat loss were measured. The original model provides an average core temperature difference compared with the human subject results of 1.31 °C (SD = 0.557 °C) while the modified model improves the final prediction of core temperature to within an average of 0.15 °C (SD = 0.383 °C). The new model shows an improvement in the prediction of human core temperature under the tested conditions where dripping sweat will saturate clothing. The format can be used in multi-segmented thermal models and can continue to be developed and improved as more information on wetted clothing properties become available.

Combined electrochemical, heat generation, and thermal model for large prismatic lithium-ion batteries in real-time applications

Real-time prediction of the battery's core temperature and terminal voltage is very crucial for an accurate battery management system. In this paper, a combined electrochemical, heat generation, and thermal model is developed for large prismatic cells. The proposed model consists of three sub-models, an electrochemical model, heat generation model, and thermal model which are coupled together in an iterative fashion through physicochemical temperature dependent parameters. The proposed parameterization cycles identify the sub-models’ parameters separately by exciting the battery under isothermal and non-isothermal operating conditions. The proposed combined model structure shows accurate terminal voltage and core temperature prediction at various operating conditions while maintaining a simple mathematical structure, making it ideal for real-time BMS applications. Finally, the model is validated against both isothermal and non-isothermal drive cycles, covering a broad range of C-rates, and temperature ranges [−25 °C to 45 °C].

Mathematical prediction of core body temperature from environment, activity, and clothing: The heat strain decision aid (HSDA)

Physiological models provide useful summaries of complex interrelated regulatory functions. These can often be reduced to simple input requirements and simple predictions for pragmatic applications. This paper demonstrates this modeling efficiency by tracing the development of one such simple model, the Heat Strain Decision Aid (HSDA), originally developed to address Army needs. The HSDA, which derives from the Givoni-Goldman equilibrium body core temperature prediction model, uses 16 inputs from four elements: individual characteristics, physical activity, clothing biophysics, and environmental conditions. These inputs are used to mathematically predict core temperature (Tc) rise over time and can estimate water turnover from sweat loss. Based on a history of military applications such as derivation of training and mission planning tools, we conclude that the HSDA model is a robust integration of physiological rules that can guide a variety of useful predictions. The HSDA model is limited to generalized predictions of thermal strain and does not provide individualized predictions that could be obtained from physiological sensor data-driven predictive models. This fully transparent physiological model should be improved and extended with new findings and new challenging scenarios.

Human Core Temperature Prediction for Heat-Injury Prevention.

Previously, our group developed autoregressive (AR) models to predict human core temperature and help prevent hyperthermia (temperature > 39°C). However, the models often yielded delayed predictions, limiting their application as a real-time warning system. To mitigate this problem, here we combined AR-model point estimates with statistically derived prediction intervals (PIs) and assessed the performance of three new alert algorithms [AR model plus PI, median filter of AR model plus PI decisions, and an adaptation of the sequential probability ratio test (SPRT)]. Using field-study data from 22 soldiers, including five subjects who experienced hyperthermia, we assessed the alert algorithms for AR-model prediction windows from 15-30 min. Cross-validation simulations showed that, as the prediction windows increased, improvements in the algorithms' effective prediction horizons were offset by deteriorating accuracy, with a 20-min window providing a reasonable compromise. Model plus PI and SPRT yielded the largest effective prediction horizons (≥18 min), but these were offset by other performance measures. If high sensitivity and a long effective prediction horizon are desired, model plus PI provides the best choice, assuming decision switches can be tolerated. In contrast, if a small number of decision switches are desired, SPRT provides the best compromise as an early warning system of impending heat illnesses.

Coupling of the Models of Human Physiology and Thermal Comfort

A coupled model of human physiology and thermal comfort was developed in Dymola/Modelica. A coupling combines a modified Tanabe model of human physiology and thermal comfort model developed by Zhang. The Coupled model allows predicting the thermal sensation and comfort of both local and overall from local boundary conditions representing ambient and personal factors. The aim of this study was to compare prediction of the Coupled model with the Fiala model prediction and experimental data. Validation data were taken from the literature, mainly from the validation manual of software Theseus–FE [1]. In the paper validation of the model for very light physical activities (1 met) indoor environment with temperatures from 12 °C up to 48 °C is presented. The Coupled model predicts mean skin temperature for cold, neutral and warm environment well. However prediction of core temperature in cold environment is inaccurate and very affected by ambient temperature. Evaluation of thermal comfort in warm environment is supplemented by skin wettedness prediction. The Coupled model is designed for non-uniform and transient environmental conditions; it is also suitable simulation of thermal comfort in vehicles cabins. The usage of the model is limited for very light physical activities up to 1.2 met only.

Original article: Apparent thermal diffusivity estimation for the heat transfer modelling of pork loin under air/steam cooking treatments

SummaryApparent thermal diffusivity linear functions vs. product temperature were estimated for pork cooked under two different treatments (forced convection, FC and forced convection/steam combined, FC/S) at 100, 110, 120 and 140 °C by means of experimental time–temperature data and a developed finite‐difference algorithm. Slope and intercept of each function were employed to calculate apparent thermal diffusivity at 40, 55 and 70 °C. Generally, FC/S treatments gave significantly higher apparent thermal diffusivities in comparison with FC conditions. Apparent thermal diffusivities were used to develop a model for cooking time and final core temperature prediction on the basis of oven setting. The model was validated by means of additional cooking tests performed at different temperatures of those employed for model development. Root mean square error values lower than 3.8 °C were obtained comparing predicted and experimental temperature profiles. Percentage errors lower than 3.1% and 3.5% were, respectively, obtained for cooking times and final core temperatures.

Evaluation of two cold thermoregulatory models for prediction of core temperature during exercise in cold water

Cold thermoregulatory models (CTM) have primarily been developed to predict core temperature (T(core)) responses during sedentary immersion. Few studies have examined their efficacy to predict T(core) during exercise cold exposure. The purpose of this study was to compare observed T(core) responses during exercise in cold water with the predicted T(core) from a three-cylinder (3-CTM) and a six-cylinder (6-CTM) model, adjusted to include heat production from exercise. A matrix of two metabolic rates (0.44 and 0.88 m/s walking), two water temperatures (10 and 15 degrees C), and two immersion depths (chest and waist) were used to elicit different rates of T(core) changes. Root mean square deviation (RMSD) and nonparametric Bland-Altman tests were used to test for acceptable model predictions. Using the RMSD criterion, the 3-CTM did not fit the observed data in any trial, whereas the 6-CTM fit the data (RMSD less than standard deviation) in four of eight trials. In general, the 3-CTM predicted a rapid decline in core temperature followed by a plateau. For the 6-CTM, the predicted T(core) appeared relatively tight during the early part of immersion, but was much lower during the latter portions of immersion, accounting for the nonagreement between RMSD and SD values. The 6-CTM was rerun with no adjustment for exercise metabolism, and core temperature and heat loss predictions were tighter. In summary, this study demonstrated that both thermoregulatory models designed for sedentary cold exposure, currently, cannot be extended for use during partial immersion exercise in cold water. Algorithms need to be developed to better predict heat loss during exercise in cold water.