Internal Temperature Estimation Research Articles

Online Internal Temperature Estimation for Lithium-Ion Batteries Using the Suppressed Second-Harmonic Current in Single- Phase DC/AC Converters

Online Internal Temperature Estimation for Lithium-Ion Batteries Using the Suppressed Second-Harmonic Current in Single- Phase DC/AC Converters

Sliding mode observer with adaptive switching gain for estimating state of charge and internal temperature of a commercial Li-ion pouch cell

Accurate estimation of the state of charge (SOC) and internal temperature is the essence of the battery management systems for lithium-ion batteries (LIBs). In this research, an improved sliding mode observer (SMO) is presented and evaluated for the estimation of SOC and internal temperature of LIBs by adapting the switching gain. The observer is meticulously designed, parametrized, and validated by combining modeling and experimentation on a commercial 64 Ah LIB pouch cell. The battery behavior is emulated by a coupled equivalent circuit model (CECM) composed of a dual-polarization and a novel thermal model. The proposed observer is showcased to estimate the SOC with an average error of <2 % even in the presence of a significant model mismatch. The results provide deep insight into the development process of the efficient and robust SMO observers for estimating the internal states of LIBs.

Internal Temperature Estimation of Lithium Batteries Based on a Three-Directional Anisotropic Thermal Circuit Model

In order to improve the accuracy of internal temperature estimation in batteries, a 10-parameter time-varying multi-surface heat transfer model including internal heat production, heat transfer and external heat transfer is established based on the structure of a lithium iron phosphate pouch battery and its three directional anisotropic heat conduction characteristics. The entropy heat coefficient, internal equivalent heat capacity and internal equivalent thermal resistance related to the SOC and temperature state of the battery were identified using experimental tests and the least square fitting method, and were then used for online calculation of internal heat production and heat transfer in the battery. According to the time-varying and nonlinear characteristics of the heat transfer between the surface and the environment of the battery, an internal temperature estimation algorithm based on the square root cubature Kalman filter was designed and developed. By iteratively calculating the estimated surface temperature and the measured value, dynamic tracking and online correction of the internal temperature of the battery can be achieved. The verification results using FUDS and US06 dynamic working condition data show that the proposed method can quickly eliminate the influence of initial temperature deviations and accumulated process errors and has the characteristics of a high estimation accuracy and good robustness. Compared with the estimation results of the adaptive Kalman filter, the proposed method improves the estimation accuracy of FUDS and US06 working conditions by 67% and 54%, respectively, with a similar computational efficiency.

Multi-cell sensorless internal temperature estimation based on electrochemical impedance spectroscopy with Gaussian process regression for lithium-ion batteries safety

Recently, fire incidents due to electrical vehicle (EV) lithium-ion battery (LIB)-based energy storages have been increasingly occurring. Preemptive diagnosis of the thermal runaway is essential for preventing such fires. However, precise and accurate temperature monitoring at the single cell level is required for all the batteries present in the EV pack. Electrochemical impedance spectroscopy (EIS) closely correlates with the battery's internal temperature (Tint) for sensorless temperature estimation, but is limited for multi-cell use because of the effect of the cell-to-cell differences on the EIS impedance values, causing inaccuracies in the use case of predefined models. This study developed a framework combining the EIS method with Gaussian process regression (GPR) machine learning for multiple LIBs cell temperature estimation. The intrinsic relationship linking the temperature, state of charge, state of health and temperature sensitivity with the impedance properties of the batteries at different frequencies was investigated. The results showed that from 51 extracted frequencies only single-point frequency measurements at 10 Hz were necessary to develop the temperature estimation model. The data at this frequency was subsequently utilized to train the GPR model. Five kernel functions were studied, trained, and validated experimentally. The root means square error (RMSE) for the most accurate kernel was 0.2 °C at the validation step, and the test RMSE for the estimation of a new cell was 0.79 °C, confirming the high accuracy of the model.

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.

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.

Internal Temperature Estimation of Lithium-Ion Battery Based on Improved Electro-Thermal Coupling Model and ANFIS

Abstract Accurate estimation of the internal temperature of lithium-ion batteries plays an important role in the development of a suitable battery thermal management system, safeguarding the healthy and safe operation of batteries and improving battery performance. In order to accurately estimate the internal temperature of the battery, this paper proposes a method for estimating the internal temperature of lithium-ion batteries based on an improved electro-thermal coupling model and an Adaptive Network-Based Fuzzy Inference System (ANFIS). First, a parameterization method of the electrical model is proposed, and an electrical model whose parameters are affected by temperature and SOC is established. Second, to overcome the complex nonlinear modeling problem of lithium-ion batteries, the ANFIS thermal model is established. Then, an improved electro-thermal coupling model for lithium-ion batteries is established by combining the proposed electrical model and the ANFIS thermal model to improve the accuracy of estimating the internal temperature of the battery. Finally, the effectiveness of the proposed method is verified by simulation and experiment.

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.

Online Monitoring of Lithium-Ion Battery Internal Temperature Using PWM Switching Oscillations

Online monitoring of Lithium-ion (Li-ion) battery internal temperature by electrochemical impedance spectrum (EIS) is important for the system safe and reliable operation. However, it is challenging in the high temperature region due to the limited thermal sensitivity. Moreover, additional signal injection or disturbance for online EIS measurement may interact with the operating system. To address these challenges, a Li-ion battery internal temperature continuous monitoring method using the self-excited ultrahigh frequency (UHF) PWM switching oscillations (e.g., several hundred kHz) is proposed in this article. First, the UHF EIS characteristics of a Li-ion battery are explored to find a novel thermal sensitive electric parameter applicable for high temperature measurement. Then, an PWM switching oscillation based online battery temperature monitoring scheme is presented, including the UHF oscillation sensor, resonance impedance extraction algorithm, and battery internal temperature estimation procedure. Finally, experimental work was carried out, and the results demonstrate an excellent performance, such as high sensitivity over 90 °C, ms-level fast response, and noncontract continuous monitoring.

Recent progresses in state estimation of lithium-ion battery energy storage systems: A review

Battery storage has been widely used in integrating large-scale renewable generations and in transport decarbonization. For battery systems to operate safely and reliably, the accuracy of state estimation is extremely crucial in battery management system (BMS). However, due to the complex working conditions and electrochemical reactions, it is difficult to accurately estimate the internal state of batteries. This survey focuses on categorizing and reviewing some of the most recent estimation methods for internal states, including state of charge (SOC), state of health (SOH) and internal temperature, of which internal temperature estimation methods have been rarely reviewed in the existing literature. The main advantages and disadvantages of these methods are compared, with a focus on the new sensing technologies for internal state estimations. Then, the future prospects of battery state estimation technologies are discussed.

An online temperature estimation for cylindrical lithium-ion batteries based on simplified distribution electrical-thermal model

Accurate modeling of electrical-thermal properties is essential for safe use and efficient and reliable thermal management of Lithium-ion batteries. However, online estimation of the internal temperatures of the battery is very difficult due to the limitations of sensor testing methods. To address this issue, this paper proposes a simplified distributed electrical-thermal model of the cylindrical lithium-ion battery to realize the online temperature estimation. First, the cylindrical battery is divided into microcells according to its actual internal Archimedean spiral winding structure and established the electrical-thermal model for each microcell. Then, these microcells are then connected in parallel to form a multilayer electrical-thermal model to better capture the core and surface temperature evolution of the battery. Extensive experiments are conducted and illustrated that this method can estimate the core and surface temperature of the battery accurately under different current rates and dynamic test conditions at wider temperature ranges (from 0 °C to 55 °C). And the estimation maximum root mean square error is 0.9384 K. Moreover, a comparison with other models shows that this model can not only realize online internal and external temperature estimation but also have high accuracy.

Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries

The rapid development of new energy fields such as electric vehicles and smart grids has put forward higher requirements for the power management of battery integrated systems. Considering that internal temperature and parameter consistency are important factors affecting battery safety and state estimation accuracy, a lumped thermoelectric coupling model based on the multi-time scale effects of battery dynamics parameter is established in this paper. On this basis, a new multi-feature separation modeling idea is proposed and adopted to complete the development of the strong coupling adaptive asynchronous identification strategy to realize the solution of the model. Specifically, the high-frequency and low-frequency characteristics of the resistor–capacitor link under different time constants are distinguished on different time scales. Three sub-filters based on forgetting factor recursive least squares, extended Kalman filtering and joint Kalman filtering are used to realize the adaptive asynchronous synergistic estimation of battery high-frequency dynamics parameter, low-frequency dynamics parameter and internal temperature. In addition, the filters at different time scales are strongly coupled through the voltage response on the diffusion impedance, and the time scale drive under slow dynamics depends on the current distribution of the test conditions. The experimental results of two long-term cycles show that the proposed strategy exhibits excellent terminal voltage tracking effect and internal temperature estimation accuracy. Finally, the concept of parameter dispersion is proposed and discussed. Compared with the results under the traditional identification method, the proposed strategy reduces the maximum parameter dispersion by 51.9%.

Thermal Simulation and Detection of Breast Tumor Using Passive Acoustic Thermometry

Purpose: For over three decades, various researchers have aimed to construct a model of breast cancer. Most of them have used an infrared thermal model to stimulate breast cancer, but in this study, a novel estimation methodology is presented to detect the breast cancer tumor using the surface measurement obtained by Passive Acoustic Thermometer (PAT). PAT is a safe method for internal temperature estimation that works based on acoustic radiation of materials with a specific temperature. Materials and Methods: This article uses a simulation framework for breast tissue simulation and tumor detection using the PAT methodologies in different scenarios. This framework supports the generation of acoustic radiation, tissue modelling, signal processing, parameter estimation, and temperature reconstruction processes. The proposed framework estimates the temperature in the frequency domain and uses the frequency spectrum of the acquired ultrasound signals captured by a single transducer. Using the proposed framework, PAT has been evaluated in breast cancer detection. Results: According to the results, obtained from the temperature estimation in scenario 3, the sub-band estimation method, which is utilized in practical experiments in this field, shows different errors in each sub-band, making it difficult to select the true estimation. Therefore, a novel formulation is proposed that provides only one estimated temperature for breast tissue with a reasonable error (1.28 degrees) for tumor detection. Conclusion: The results show that it is possible to use this framework to evaluate the PAT in different scenarios for tumor detection. In fact, this method enhances the possibility of examination of different conditions and algorithms. It also reduces the cost of practical experiments.

Data‐Driven–Based Internal Temperature Estimation for Lithium‐Ion Battery Under Variant State‐of‐Charge via Electrochemical Impedance Spectroscopy

Internal temperature estimation is critical to the safe operation of lithium‐ion batteries (LIBs), and electrochemical impedance spectroscopy (EIS)‐based methods have been demonstrated to be promising. However, accurate internal temperature estimation under variant state‐of‐charge (SoC) is still challenging due to the combined impact of temperature and SoC on the EIS. Accordingly, this work proposes a novel EIS‐based internal temperature estimation approach, for which SoC‐insensitive EIS features are quantitatively selected and utilized for temperature estimation using support vector regression (SVR) with unknown SoC. First, the EIS feature selection is performed to select SoC‐insensitive features from the imaginary of impedance spectrum. Subsequently, an SVR‐based framework and a well‐trained SVR model are created to estimate the internal temperature of LIBs. The performance of the proposed model is validated by its lowest estimation error (0.57 °C) under known and unknown SoCs compared to that of the existing methodologies. The results confirmed that the proposed method holds the advantage in estimating the internal battery temperature with different SOCs.

Use of the MLX 90164 sensor and the ThingSpeak platform for internet of things-based animal body temperature check

The MLX 90614 sensor is an IR sensor used to measure temperature without contact. This sensor can measure the object's temperature and ambient temperature in the range of -40 oC – 125 oC. These sensors are widely used in areas such as room temperature measurement, machine temperature, ambient temperature, and body temperature. The MLX 90614 sensor is not yet fully usable directly to perform temperature measurements due to the program's simple defaults so that the measurement results are inaccurate. Therefore, optimization is needed to increase the precision value of the body's internal temperature estimation using this sensor. The method carried out in this study used 6 experimental sheep. Temperature checks are performed through a contact thermometer and compared to non-contact checks by MLX 90614 sensors. Results showed the average temperature of the contact thermometer was 39.28±0.09 oC while the MLX sensor yield was 35.78±1.38 oC. The average temperature difference was 3.5±1.48 oC. The results showed that the temperature difference correction factor needs to be included in the script code when running MLX 90614 sensor. The results of temperature checks by the sensor can be sent directly in real time to the ThingSpeak platform so that the results can be stored and accessed in different places easily. The use of MLX 90614 sensor combined with IoT concept using ThingSpeak can facilitate the process of non-contact body temperature check, and also make the data can be accessed dan stored easily.

A novel extended Kalman filter-based battery internal and surface temperature estimation based on an improved electro-thermal model

The lithium-ion battery temperature monitoring plays a prominent role in developing an appropriate battery thermal management system to ensure battery security, and improve battery performances, and it is also a vital fundamental module for the development of battery management system. To address this issue, this paper proposes a novel estimation procedure for the internal and surface temperatures of a lithium-ion battery cell based on an improved electro-thermal model (IETM). First, an electro-thermal model of lithium-ion battery is built up by combing the temperature-dependent electrical model and the two-state thermal model together. Then, the parameterization scheme of this IETM is carried out to facilitate the real-time estimation of the battery internal and surface temperatures. Furthermore, based on the extended Kalman filter (EKF), the battery IETM is employed to estimate both the battery internal and surface temperatures without requiring extra temperature sensors. Finally, the effectiveness and feasibility of the proposed battery temperature estimation method are demonstrated by performing the simulation and experiment verification.

Optimization of the lumped parameter thermal model for hard-cased li-ion batteries

Abstract Li-ion batteries are widely employed for electric vehicles. Real-time prediction of the internal battery temperature is crucial for accurate battery management system control. This paper intends to optimize the lumped parameter thermal model so that it is able to precisely present heat flow distribution and achieve broad applicability for online battery temperature estimation. Three lumped parameter thermal models for hard-cased Li-ion batteries are proposed, including the two-state lumped thermal model (2STM), five-state lumped thermal model (5STM), and improved five-state lumped thermal model (5STM+). The 5STM+ considers the heat transfers between the surface states of the 5STM. The model parameters are identified through solving linear equations and nonlinear curves fitting in the least square sense based on experimental data. Model applicability is confirmed by the evaluation of calculation accuracy under seven possible application situations. It is found that 2STM and 5STM behave excellently with uniform boundary conditions, but lead to significant discrepancy for local liquid cooling scenarios. 5STM+ provides high calculation accuracy under all proposed situations, covering natural convection, forced air convection, regional liquid cooling as well as localized heating. Meanwhile, the calculation time consumption is only a millisecond level. It is practical for online battery internal temperature estimation.

A Simulation Framework for Passive Acoustic Thermometry of Nonhomogeneous Materials

Purpose: Internal temperature is a significant factor for medical diagnosis. There are several thermometric methods, including IR, MRI, and active ultrasonic thermometry, which have limitations for clinical applications. The new method in this field called Passive Acoustic Thermometry (PAT), which enhanced some of this limitation. PAT is a safe method for internal temperature estimation that works based on acoustic radiation of materials with a specific temperature. Several experimental studies have been carried out so far in the field of PAT. While, to the best of our knowledge, there is no simulation-based research for nonhomogeneous materials reported yet. In this article (for the first time) we proposed a simulation framework for evaluating the PAT methodologies in nonhomogeneous materials; also we proposed a new formulation for temperature estimation in PAT algorithm. Materials and Methods: This framework supports the generation of acoustic radiation, signal processing, parameter estimation, and temperature reconstruction processes. At the moment the proposed framework estimates the temperature in the frequency domain and uses the frequency spectrum of the acquired ultrasound signals captured by a single transducer. Using the proposed framework, we tried to implement the previously practical experiments and the results of the simulation are consistent with those of the practical experiments. Also, we proposed the formulation that improves the error of temperature estimation. Results: We study 6 scenarios, including 2 environments with a target at 3 different temperatures. The average error of the proposed formulation in two different nonhomogeneous materials for three different temperatures is less than 0.25°C. Conclusion: The results show that the proposed formulation is the best estimation in the formula that has been introduced until now and compare with the previous study the accuracy is enhanced 54% (from 0.79 to 0.36 deg.). Therefore, the proposed formula enhanced PAT accuracy for temperature estimation. Also, the results show that it is possible to use this framework to evaluate the PAT in different scenarios. Therefore, this method enhances the possibility of examination of different conditions and algorithms. It also reduces the cost of practical experiment.

An Enhanced Online Temperature Estimation for Lithium-Ion Batteries

This article presents an enhanced internal temperature-estimation method for lithium-ion batteries using a 1-D model and a dual Kalman filter (DKF). The cylindrical battery cell is modeled by a 1-D thermal model with three nodes. This model provides a more accurate representation of the temperature distribution, resulting in more detail of the temperature field. With the newly developed 1-D model, an enhanced temperature-estimation method is developed by including the internal resistance identification and SOC estimation in the temperature-estimation process. Experiments and simulations are conducted to evaluate the robustness and accuracy of the temperature estimation. The estimated temperature using the 1-D model with random initial values is compared with the surface temperature from experiments, which shows excellent robustness against random initial values. High estimation accuracy is demonstrated by the comparison between the estimated temperature field and the simulated temperature field from a high-fidelity 3-D model. Experimental results show that the DKF method provides better stability than the single Kalman filter, and the accuracy of the internal temperature estimation is improved by the equivalent thermal conductivity identification that considers the anisotropy of thermal conductivity in different directions.

A simplified mathematical model for estimating gas temperature and velocity under natural smoke exhaust in sloping city tunnel fires

In the previous study, the prediction of gas temperature and velocity in the naturally-ventilated inclined tunnels under traffic blockage has been well concerned (Y. Zhou et al., Sustain. Cities Soc. 45, 2019, 258–270). In this paper, we continue to pay attention to the estimation of internal gas temperature and velocity under natural smoke extraction in both single-slope and gable-slope tunnel fires, a more hazardous scenario. A series of simplified mathematical models based on the equations of continuity, energy and pressure balance was proposed to estimate the gas temperature and velocity in sloping tunnel fires, in which the influences of fire source location, tunnel slope and tunnel geometry were taken into account. In addition, corresponding numerical simulations based on full-size tilted urban tunnel fires were performed using Computational Fluid Dynamics (CFD) method. The results show that the variation trends between CFD and analytical results are basically the same, and the iterative values of gas temperature and velocity coincide well with the simulations. A fast and convenient method for the estimation of gas temperature and velocity in sloping tunnel fires has been proposed, and the results of this work can provide a reference for the optimal design and management of natural smoke extraction systems, and help to ensure the fire safety of single-slope and gable-slope city tunnels.