Temperature Estimation Method Research Articles

Field test of machine-learning based mean radiant temperature estimation methods for thermal comfort-integrated air-conditioning control improvement and energy savings

Advanced control strategies for heating, ventilation, and air-conditioning (HVAC) systems aim to enhance both buildings' energy efficiency and occupant thermal comfort. Despite their potential, real-world demonstration studies on such advanced technologies are still lacking. This study presents a field demonstration of a real-time predicted mean vote (PMV)-based HVAC control strategy in a typical residential building under hot and dry climate conditions. This study introduces an advanced thermal comfort-based controller (TCC) for the PMV-based HVAC control. TCC continually assesses indoor and outdoor thermal conditions and adjusts the HVAC setpoint temperature to optimize real-time energy use while ensuring satisfactory indoor thermal comfort. Machine learning models for the PMV-based HVAC control system are employed to estimate a mean radiant temperature (MRT) point, which is one of the variables used to calculate real-time PMV values. Three machine learning models (i.e., linear regression, regression trees, and artificial neural network) are adopted in this study with non-stationary real-time input values, including times, pre-determined setpoints, and indoor and outdoor temperatures. The developed TCC is installed in a full-scale experimental house in Kuwait, which is under hot and dry climate conditions, to assess the house's indoor thermal comfort and AC energy efficiency performance. Results indicate that the TCC provides better thermal comfort performance compared to the non-TCC case, including up to a 60% improvement in PMV. The proposed TCC controlled by the machine learning methods demonstrates HVAC energy savings potential of over 20% while meeting the desired thermal comfort levels in the experimental building. These findings are expected to be valuable, as they can contribute to reducing cooling energy in residential buildings in hot and dry climate conditions.

Should the relationship between leaf margin and temperature be re‐defined for areas with colder climates?

AbstractAimThe well‐proven positive correlation between the increased proportion of entire‐leaved woody dicotyledonous species and increased mean annual temperature has been commonly used to estimate temperature in the past. However, in regions of colder climates, this relationship is not straightforward, questioning the accuracy of temperature estimation.LocationFinland, Poland, Germany.TaxonDicotyledons.MethodsThe floristic composition of 10 × 10 km squares in 20 km wide transects through Finland, Poland and Germany was analysed.ResultsAt higher temperatures, deciduous woody plants appeared to show the expected positive relationship between mean annual temperature and the proportion of entire leaf margins. However, we found a negative correlation within woody deciduous plants at higher latitudes with mean annual temperature values from approximately −2.5°C to +2–4°C and at all temperatures when all woody plants were included. Herbaceous species showed a weak relationship between morphology and temperature.Main ConclusionsThe hypothesis that the phenomenon was caused by a large percentage of entire‐leaved evergreen species that winter under snow cover was rejected. These results indicate that using the leaf margin analysis method for past temperature estimation is increasingly inaccurate at colder temperatures. Consequently, we recommend avoiding this method at locations where the mean annual temperature falls below 5°C.

Data-driven internal temperature estimation methods for sodium-ion battery using electrochemical impedance spectroscopy

As sodium-ion batteries (SIBs) move towards commercialization, safety monitoring of SIBs has become the next key issue, and how to avoid thermal runaway is one of the toughest challenges. The electrochemical impedance spectroscopy (EIS) method for the internal temperature estimation of lithium-ion batteries has received considerable attention due to its non-invasive detection and high accuracy. However, research on the impedance-temperature characteristics of commercial SIBs remains limited, and EIS methods suitable for estimating the internal temperature of SIBs require further investigation. In this study, four commercial 26,700 SIBs were tested, the EIS results of the batteries in various state-of-charge (SoC) states and at different temperatures were systematically investigated, and a method for estimating the internal temperature of SIBs on the basis of the combination of EIS and machine learning (ML) is proposed. Seven component parameters were extracted as features from the raw EIS data using equivalent circuit model fitting, and four features, which were found to be highly correlated with temperature, were further selected by correlation analysis. The mapping relationship between the extracted features and the internal temperature of the battery was established based on three ML regression models. Results demonstrate that the average estimation error of the multi-layer perceptron models for the internal temperature of the battery with unknown SoC states is only 1.086 °C. This paper fills the gap in the temperature characterization of EIS for SIBs and proposes an effective method for overcoming the cross-coupling of the battery EIS with the SoC and temperature within the framework of mechanical learning to estimate the internal temperature of SIBs.

Methods for temperature estimation and monitoring of permanent magnet: a technology review and future trends

Methods for temperature estimation and monitoring of permanent magnet: a technology review and future trends

The relationship between the body and air temperature in a terrestrial ectotherm.

Ectotherms make up the majority of terrestrial biodiversity, so it is important to understand their potential responses to climate change. Often, models aiming to achieve this understanding correlate species distributions with ambient air temperature. However, this assumes a constant relationship between the air temperature and body temperature, which determines an ectotherm's thermal performance. To test this assumption, we develop and validate a method for retrospective estimation of ectotherm body temperature using heat exchange equations. We apply the model to predict the body temperature of wild field crickets (Gryllus campestris) in Northern Spain for 1985-2019 and compare these values to air temperature. We show that while air temperature impacts ectotherm body temperature, it captures only a fraction of its thermal experience. Solar radiation can increase the body temperature by more than 20°C above air temperature with implications for physiology and behaviour. The effect of solar radiation on body temperature is particularly important given that climate change will alter cloud cover. Our study shows that the impacts of climate change on species cannot be assumed to be proportional only to changing air temperature. More reliable models of future species distributions require mechanistic links between environmental conditions and thermal ecophysiologies of species.

Accurate state of temperature estimation for Lithium-Ion batteries based on square root cubature Kalman filter

Accurate state of temperature estimation for lithium-ion batteries is essential to ensure its safe and efficient operations. In this study, a state of temperature estimation framework for cylindrical lithium-ion batteries is proposed based on square root cubature Kalman filter algorithm, wherein the internal and surface temperatures of power battery are accurately estimated. Firstly, the electro-thermal coupling characteristics of lithium-ion battery is characterized based on an equivalent circuit model, Bernardi battery heat generation model and two-state lumped thermal model. On this basis, the discrete time-domain characteristics of internal and external temperature of the battery are addressed. Then, the genetic algorithm and forgetting factor recursive least squares algorithm are respectively employed to identify the circuit parameters and thermal physical parameters, and the internal and surface temperatures prediction is achieved based on square root cubature Kalman filter algorithm. The feasibility of the proposed temperature estimation method is verified at different charge and discharge process under wide range ambient temperature of -20 °C to 60 °C. The experimental results manifest that the proposed method can accurately online estimate the internal temperature and surface temperature with the mean absolute error of less than 1.16 °C.

EKF-MME based temperature estimation method for hydraulic brake by wire system

EKF-MME based temperature estimation method for hydraulic brake by wire system

A Gate Degradation Independent Online Junction Temperature Estimation Method for SiC MOSFETs Based on Residual Resistance

A Gate Degradation Independent Online Junction Temperature Estimation Method for SiC MOSFETs Based on Residual Resistance

Energy model based sensorless estimation method for operational temperature of braking resistor onboard metro vehicles

PurposeThis study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor.Design/methodology/approachIn this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton's law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner.FindingsIn this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical.Originality/valueThe proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

Thermo-sensitive electrical parameters of high-power IGBTs based on gate-emitter voltage measurement during switching delay intervals

High-power insulated gate bipolar transistors (IGBTs) are widely used for wind power conversion and electric traction. Accurate estimation of IGBT junction temperature and active thermal control improve the reliability of power converters in such applications. Among the various available methods for junction temperature estimation, thermo-sensitive electrical parameter (TSEP) based estimation is simple, fast and amenable to real-time implementation. Evaluation of many TSEPs (e.g. forward voltage, threshold voltage, peak dv/dt, peak di/dt etc.) require measurement of multiple electrical quantities in the power circuit and/or in the gate-drive circuit. The measurement systems need to have high precision and/or high bandwidth as well. Moreover, the quantities in the power circuit require isolated measurement systems. This paper explores possible new TSEPs, that could be obtained through a single measurement of low-voltage quantity, namely, the gate-emitter voltage. Since the gate-emitter voltage varies primarily during turn-on and turn-off delay intervals, these switching intervals and their parameters are studied over a wide range of operating conditions. Since many practical converters are expected to operate at an ambient temperature as low as −35 °C, the experimental study is carried out over a wide temperature range from −35 °C to 125 °C. Further, to ensure wider applicability of the findings of the study, four IGBTs of different makes are considered for the experimental investigations. The experimental results bring out four different parameters pertaining to turn-on delay and another four parameters pertaining to turn-off delay, which are potential TSEPs. Of these eight TSEPs, five of them can be evaluated based on measurement of gate-emitter voltage only, and four of these have not been reported in the literature previously. In addition, the experimental data are useful for development of temperature-sensitive device models for simulation, performance assessment and design of gate-drive circuit for different temperatures, and for temperature-sensitive compensation of inverter dead-time effect.

Comparison and analysis of hyperspectral temperature data in directed energy deposition

Directed energy deposition is an additive manufacturing process that allows the production of near net shape structures. Moreover, the process can also be applied for the repair of high value components. To obtain structures with consistent good characteristics, the directed energy deposition process requires the implementation of a control system. The currently applied approaches for control that are discussed in the literature have specifically focused on melt-pool temperature control. Pyrometers have been used for such purposes; however, they provide only a single scalar value without any spatial information. In this paper, the implementation of a high-speed hyperspectral camera-based system is discussed with a high spatial resolution unlike the pyrometers. Different calibration and temperature estimation procedures for this camera-based system are evaluated and analyzed. The number of effective wavelengths needed for temperature estimation will be discussed in detail and provide an outlook on the potential of this hyperspectral camera-based system. In addition to the number of wavelengths, another important aspect of the temperature estimation methods is the stability with respect to disturbances. Within this paper, the impact of the nominal laser power will be evaluated on the stability of the temperature signals for a control system.

Temperature Estimation in Lithium-Ion Cells Assembled in Series-Parallel Circuits Using an Artificial Neural Network Based on Impedance Data

Lithium-ion cells are widely used in various applications. For optimal performance and safety, it is crucial to have accurate knowledge of the temperature of each cell. However, determining the temperature for individual cells is challenging as the core temperature may significantly differ from the surface temperature, leading to the need for further research in this field. This study presents the first sensorless temperature estimation method for determining the core temperature of each cell within a battery module. The accuracy of temperature estimation is in the range of ΔT≈1 K. The cell temperature is determined using an artificial neural network (ANN) based on electrochemical impedance spectroscopy (EIS) data. Additionally, by optimizing the frequency range, the number of measurement points, input neurons, measurement time, and computational effort are significantly reduced, while maintaining or even improving the accuracy of temperature estimation. The required time for the EIS measurement can be reduced to 0.5 s, and the temperature calculation takes place within a few milliseconds. The setup consists of cylindrical 18,650 lithium-ion cells assembled into modules with a 3s2p configuration. The core temperature of the cells was measured using sensors placed inside each cell. For the EIS measurement, alternating current excitation was applied across the entire module, and voltage was measured individually for each cell. Various State of Charge (SoC), ambient temperatures, and DC loads were investigated. Compared to other methods for temperature determination, the advantages of the presented study lie in the simplicity of the approach. Only one impedance chip per module is required as additional hardware to apply the AC current. The ANN consists of a simple feedforward network with only one layer in the hidden layer, resulting in minimal computational effort, making this approach attractive for real-world applications.

Uncertainty Quantification of Deep Learning–Based Statistical Downscaling of Climatic Parameters

Abstract This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future. Significance Statement The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty.

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.

Data-driven thermal state estimation for in-orbit systems via physics-informed machine learning

Thermal analysis of spacecraft systems is a critical process for mission operations. However, knowing the temperature distribution of entire systems is not easy due to the uncertainty of the thermal mathematical model (TMM) and limited temperature sensors. This paper proposes a temperature estimation method using physics-informed machine learning (PIML). The PIML-based thermal analysis allows us to estimate the actual temperature distribution by seamlessly bridging the limited observations and the TMM. To evaluate the estimation accuracy of the proposed method, we conducted a numerical experiment using a pseudo small satellite model consisting of 100 nodes. The proposed method was applied to three different model error cases and was found to improve temperature estimation accuracy in all cases. In addition, the impact of the number of temperature sensors and their placement on estimation accuracy was investigated.

A Novel Junction Temperature Estimation Method Independent of Bond Wire Degradation for IGBT

The insulated-gate bipolar transistor (IGBT) junction temperature is crucial for condition monitoring, reliability assessment, and health management. However, the existing IGBT temperature monitoring methods are affected by the aging of IGBT bond wires. A novel junction temperature estimation method independent of bond wire degradation for IGBT is proposed in this paper. On-state voltage drop and turn-on gate voltage overshoot are analyzed for dependence on the junction temperature and bond wire degradation. These two temperature and bond wire monitoring electrical parameters are combined by the Artificial Neural Network (ANN), to eliminate the effect of bond wire degradation. A double pulse test platform is built to verify the influence of temperature and bond wire degradation on on-state voltage drop and turn-on gate voltage overshoot, which agrees with the theoretical analysis. Lots of experimental data at various collector-emitter voltages, load currents, IGBT junction temperature, and bond wire degradation are tested. A BP neural network is trained for junction temperature monitoring and its optimal number of hidden layers is studied. The experimental results show that the junction temperature estimation method based on the neural network can effectively eliminate the effect of bond wire degradation. The estimation error is around 4.219°C. The proposed method has the advantages of high accuracy and a wide application range for junction temperature monitoring.

Sensorless Temperature Estimation for Li-Ion Battery Cells: An Overview, Practical Considerations, Challenges and Future Trends

Sensorless temperature estimation methods for batteries can be classified into three categories: analytical methods, observer-based methods and data-driven methods. In general, analytical methods are easy to derive and implement but have a limited performance due to their open-loop nature. Observer-based methods have a high performance due to their closed-loop nature but demand accurate dynamic and measurement models with up-to-date parameters for accurate estimation. Data-driven methods are extremely stable and robust due to their massive parallel structure, but they demand huge amount of data for training. This paper presents a comprehensive review of state-of-the-art sensorless temperature estimation methods for lithium-ion (Li-ion) batteries and demonstrates the advantages and limitations of each. A comparison between the presented methods in terms of their performance and implementation requirements is carried out. In addition, practical considerations, challenges and future trends are discussed.

Robust Rotor Temperature Estimation of Permanent Magnet Motors for Electric Vehicles

As the mainstream powertrain of electric vehicles, permanent magnet motors are facing the challenge of durability and thermal failure. Therefore, the real-time rotor temperature monitoring plays a critical role, however, it is hard to online measure with sensors. To this end, a rotor temperature estimation method based on the lumped-parameter thermal networks and dual H infinity filters is proposed. Firstly, the lumped-parameter thermal network of three nodes, such as the stator, rotor and bearing, is numerically formulated to determine the power loss. Accordingly, the discretized state-space expressions are specified for the time-step iterative solution. Then, to address the uncertainty of model parameters, the dual H infinity filters are used in the rotor temperature estimation process. Finally, the simulation and experimental tests are performed to validate the effectiveness and the real-time executability of the proposed method. The test results show that the proposed method can well track the actual temperature tendency with estimation errors of less than 7.5 °C. Compared with the existing methods, the worst-case estimation accuracy has been improved by at least 25 %; besides, the proposed method presents good robustness against the parameter uncertainty; meanwhile, the higher estimation convergence is made in the face of huge model deviations.

A novel Kalman-filter-based battery internal temperature estimation method based on an enhanced electro-thermal coupling model

Estimating the internal temperature of lithium-ion batteries (LIBs) in battery management systems (BMS) is critical as it enables the system to monitor and manage the actual temperature of LIBs, thereby ensuring their long-term performance, safe operation and prevention of thermal runaway. However, the existing BMS can only monitor the surface temperature of the battery. To accurately estimate the internal temperature of LIBs during normal operation, this paper proposes an enhanced electro-thermal coupling model to calculate the internal temperature of LIBs. This model combines an improved second-order resistance-capacitance equivalent circuit model with a two-state thermal model to provide a more precise calculation of the internal temperature of LIBs. The model's parameters are determined using the Trust-Region Reflective based nonlinear least squares method, and the Kalman-filter algorithm is used to estimate the internal temperature of the battery based on the model. The proposed method has been demonstrated to be effective through experimental results, with a maximum root mean square error of 0.5705 °C at the battery testing conditions of Standard Charge, Federal Urban Driving Schedule (FUDS), Dynamic Stress Test (DST) and Constant Current Discharge (CC-Discharge). Due to its low cost and simplicity, this method is suitable for practical engineering applications.

A Large-Scale Identification Approach for Thermal Parameters of Multichips IGBT Modules Based on LLSO-SQP Algorithm

This paper proposes a large-scale optimization approach for identifying thermal parameters of multi-chip IGBT modules. State-space equation, in which the coefficient matrix comprises the thermal resistance and capacitance, is provided to represent the compact three-dimensional thermal network model. Then, the level-based learning swarm optimization (LLSO) algorithm is firstly utilized to identify large-scale thermal parameters. Additionally, to solve the inefficient convergence problem, the optimization results obtained from the LLSO is provided as the initial value of the sequential quadratic programming (SQP) algorithm to find the global optimal solution. Hence, the proposed LLSO-SQP algorithm can identify the large-scale thermal parameters efficiently and accurately. The training dataset for the algorithm is derived from the transient temperature response of a finite element model (FEM) of the IGBT module under power step excitation. Since only one-time simulation is in-demand, this approach needs less computational effort than others. The identified thermal network model is utilized to estimate junction temperature profiles taking a two-level inverter as a case study. In comparison to that of the experiment and FEM, the results validate the feasibility and accuracy of the junction temperature estimation method based on the compact three-dimensional thermal network model.