Estimation Of Cell Temperature Research Articles

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.

Photovoltaics literature survey (no. 182)

Photovoltaics literature survey (no. 182)

Photovoltaic module cell temperature estimation: Developing a novel expression

Energy efficiency is one of the most important issues of today. The limited reserves of fossil fuels make the use of renewable energy sources more attractive. Photovoltaic panels are one of the ways to utilize solar energy, which is one of the renewable energy sources.The instantaneous efficiency of photovoltaic panels is related to the cell temperature of the panels. In the current studies in the literature, there are empirical expressions that give the photovoltaic panel cell temperature, but the results obtained from these expressions do not overlap with each other. In order to obtain the instantaneous efficiencies of photovoltaic panels and, accordingly, the production values theoretically, there is a need for a global expression that gives the photovoltaic panel cell temperature. In this study, a global expression was developed that gives the photovoltaic panel cell temperature depending on the ambient temperature, solar radiation and wind speed. In addition, using the meteorological data of Kütahya for many years, expressions giving ambient temperature, solar radiation and wind speed were created.

Cell-Level PV Module Temperature Estimation Method

In this article, we analyze the characteristics of current–voltage (I–V) curves of photovoltaic (PV) modules in the hotspot state, determine characteristic quantities for identifying specific hotspot state information in the utilized module, and establish an inverse solution model for estimating the normal module temperature according to the electrical characteristics of the module. We combine this inverse solution model with a heat balance model to estimate the temperature of a faulty cell and obtain the overall temperature distribution in the module. Finally, infrared data on hotspot modules collected at a power plant in Hubei are used to verify the feasibility and accuracy of the temperature estimation algorithm. The experimental results show that the proposed PV cell temperature estimation method can determine the temperature distributions in different module areas and provide technical support for module-associated fire warnings.

A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, a cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB, such as state of charge, state of health and so on. High-capacity LIB packs, used in electric vehicles and grid-tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to researchers and industries towards developing a highly accurate, intelligent, adaptive, easy-to-implement and computationally efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.

Real-Time Under-Load Electrochemical Impedance Spectroscopy (EIS) Analysis and Modeling

All batteries degrade, but the challenge comes in quantifying that degradation and ultimately predicting a battery's failure and/or end-of-life. Battery health and monitoring have become more imperative with larger deployment of lithium batteries in electric vehicle (EV) and energy storage system (ESS) applications. Research shows a strong correlation between battery degradation and AC impedance measured using electrochemical impedance spectroscopy (EIS). This indicates that AC impedance can be used as a proxy for battery health. Additional information, like a battery's internal temperature can also be extracted from EIS measurements. As such, there is a strong push to measure AC impedance of a battery in field diagnostic applications. However, EIS is typically performed at rest under very stable environmental conditions. This traditional method of interpreting EIS must be modified to account for changing environmental conditions and changing current loads like those seen in EV and ESS applications. This paper explores the prospect of using EIS in a real-time battery management system (BMS) with the primary focus on determining the limits for interpreting EIS under loaded current conditions. An experiment is conducted to evaluate using EIS measurements taken on a loaded battery for the purpose of temperature estimation.A set of experiments were conducted where EIS was performed on Samsung 18650 Nickel Manganese Cobalt (NMC) cells while the cell was subject to loaded conditions. These experiments consisted of load currents ranging from charging rates (C-rate) between C/5 to 2C, and discharging rates (D-rate) between D/5 to 2D. Test were conducted at temperatures in the range of 5°C to 35°C. Closer inspection of Figure 1 shows EIS spectra diverging for charging conditions at lower frequencies. This is believed to be caused by a drift in the battery’s SOC. During charging, lower frequencies in AC impedance tend to have lower magnitude. Conversely, higher AC impedance magnitude is observed during discharging. The EIS frequency at which the loaded impedance deviates from the unloaded impedance by >10% is determined. The curve in Figure 2 represents the lowest EIS frequency at which under load EIS spectra is deemed usable, meaning that the shaded region below the curve represents the unusable portion of the EIS spectra. Moreover, this relationship serves as groundwork upon which to correct for EIS under loaded conditions. It is believed that the relationship of frequency limit as a function of load current shown in Figure 2 is slightly skewed by battery heating as an artifact of the test procedure. As a result, this experiment will be repeated prior to the final paper to gather data that accounts for these heating effects. A method to correct for shifts in an under-load EIS spectra will also be presented in the final paper.The prospect of using an under-load EIS spectra for internal temperature prediction is also demonstrated on a lab scale. In this experiment, the Samsung cell was soaked in a thermal chamber at constant temperature for several hours. A series of 3D discharge pulses are then applied to the battery for various durations. EIS is performed back-to-back during the current pulses (i.e., under load) and during rest times. The EIS spectra are fed into a cell temperature estimation model based on a non-zero interrupt frequency (NZIF) method. Figure 3 shows how EIS spectra changes with temperature for both loaded and unloaded conditions. The details of the NZIF model are beyond the scope of this paper. Thermistors are placed on the surface of the battery to measure a reference temperature. The results of the NZIF-predicted internal temperature are contrasted with measured thermistor battery surface temperatures, and those results are shown in Figure 4. These results indicate that NZIF-predicted internal temperature rises and falls during discharge and rest events, consistent with expected temperature rise and thermistor-measured battery surface temperature.Preliminary analysis suggests that constant current loads only slightly skew no-load EIS measurements, and that most of the EIS spectra taken under load is still usable. Furthermore, the usable portion of an EIS spectra can be determined by an EIS frequency vs load current relationship. This paper demonstrated that EIS can be used for internal temperature prediction while the battery is subject to a loaded condition, which is potentially useful to improve safety and performance of battery packs in field applications. In future work, more EIS measurements will be taken under different loaded conditions to improve NZIF predictions of internal temperature. Nevertheless, these early results are extremely promising for the use of EIS in real-time BMS applications. Figure 1

A data-driven approach to estimate battery cell temperature using a nonlinear autoregressive exogenous neural network model

Abstract Battery cell temperature is a key parameter in battery life degradation, safety, and dynamic performance. Intense charging-discharging operations and high-ambient temperatures escalate battery cell temperature, which in turn accelerates its degradation. Therefore, accurate battery cell temperature estimation can play a significant role in ensuring the optimal operation of a battery energy storage system (BESS). In order to estimate battery cell temperature as accurate as possible, use of non-linear models is imperative due to the non-linear nature of the battery operation. This paper proposes a data-driven model based on a Non-linear Autoregressive Exogenous (NARX) neural network to estimate battery cell temperatures in a utility-scale BESS, considering strongly-correlated independent variables, e.g., charging-discharging current and ambient temperature. Due to different temperature and weather characteristics in each season, seasonal NARX models have also been derived and compared with the universal one. The proposed models’ performance has been verified using the field data collected from a grid-connected BESS within a PV plant. The simulation results show high accuracy of the proposed model compared to the measured data for both seasonal and universal models without considering the complexity of the large-scale battery and container thermal dynamics. In particular, in more than 95% of the time, the estimated values yield root mean squared errors (RMSE) below 1 ∘C in different conditions, which confirms the validity and accuracy of the proposed model. Moreover, seasonal models show better performance with 18% to 50% less RMSE on average (for 1 h to 24 h forward estimation) compared to the universal model.

A Multi-State Dynamic Thermal Model for Accurate Photovoltaic Cell Temperature Estimation

The photovoltaic (PV) cell temperature strongly affects the performance and efficiency of the entire PV module. Thus, the accurate estimation of the cell temperature plays an important role in the health monitoring and energy assessment of PV systems. This article proposes a multi-state dynamic thermal model for PV modules, considering the heat-transfer mechanisms between the module and its environments, as well as between layers. The proposed model is benchmarked against field measurements at Aalborg University, Denmark. The results demonstrate the effectiveness of the model to characterize the internal behavior of the PV module under varying weather conditions. The performance of the proposed thermal model is also compared with prior-art models, i.e., two benchmark models, a one-state thermal model and two typical empirical equation-based models. The comparison further confirms that the estimation of cell temperature using the developed model is more accurate, presenting a reliable prediction of power production for further monitoring and diagnosis.

Comparison of different physical models for PV power output prediction

The electricity produced from renewable energy, in particular from wind and photovoltaic plants, has seen exponential rise in the last decade. Consequently, the prediction of power produced from these plants is fundamental for the reliability, safety and stability of the grid. This paper compares three physical models describing the PV cell (corresponding to three-, four- and five-parameter equivalent electric circuit) and two thermal models for the cell temperature estimation (NOCT and Sandia). The models were calibrated and tested towards ten monocrystalline and eight polycrystalline modules installed at SolarTechLab at Politecnico di Milano. The hourly error of the forecasted power output is usually lower than 15Wh, while NMAE% and WMAE% are in the range of 0.5% and 10%. Low errors, calculated with actual weather conditions, suggest that the implemented models are accurate, but they cannot be directly compared with other approaches which adopt weather forecasts. Results show that there is no clear advantage of using complex models, but the data used for the model calibration mostly affect the model accuracy. It was found that forecasted power output are more accurate using experimental data and Sandia’s thermal model in monocrystalline cells type, while for the polycrystalline the data from the manufacturer and NOCT have lower errors.

A method for estimating cell temperature at the maximum power point of a HCPV module under actual operating conditions

The operating cell temperature of a HCPV module or system is a key factor, because it directly affects efficiency and reliability. Hence, the accurate estimation of cell temperature in a HCPV module is crucial. Under real operating conditions, HCPV modules work at the maximum power point, because they are connected to an inverter. At the maximum power point, the cell temperature is lower than the cell temperature of open circuit, because solar cells are generating power which is not transformed into heat. At present, none of the existing methods are valid to estimate the operating cell temperature of a HCPV module connected to an inverter. In this paper, a procedure for estimating this temperature is introduced. The results show that the proposed method performs effectively in the estimation of the cell temperature in a HCPV module connected to an inverter. In addition, an analysis of the difference between the cell temperature of open circuit and the cell temperature at the maximum power point in a HCPV is conducted and a difference of up to 21°C has been found.

Comparison of PV Cell Temperature Estimation by Different Thermal Power Exchange Calculation Methods

A steady-state thermal model for calculate the temperature of a photovoltaic (PV) module has been developed for outdoor installation such as ground-mounted systems. The PV temperature is influenced by environmental variables such as: irradiance ambient temperature, intensity and direction of the wind, module design, orientation and mounting structure. As well as it is influenced by electrical parameters. In literature some single layer thermal balance consider only an overall heat transfer coefficient as a function of wind speed, neglecting the radiative thermal flux. In this paper, five thermal balance are compared and it is shown that the radiative term cannot be neglected, otherwise the PV temperature could be overestimated for low solar radiation intensity and it could be underestimated for high solar radiation intensity. For this reason, the percentage contribution of the heat exchanges, normalized as function of normal incoming solar radiation, are evaluated for wind speed within 1 m/s (natural convection).

The electrochemical cell temperature estimation of micro-tubular SOFCs during the power generation

In this study, new temperature monitoring method for solid oxide fuel cells (SOFCs) was proposed, and the demonstration using micro-tubular SOFCs has done. The fabricated micro-tubular SOFCs were operated under the two different conditions, and polarization during the power generation was measured. The cell resistance as a function temperature was also measured. Based on the temperature dependence of cell resistance and the measured ohmic drop during the power generation, the cell temperature was estimated. The estimated cell temperature clearly depended on operation condition and increased with increasing of the current density. The estimated cell temperature was also in nearly proportion to the temperature which was measured by a thermocouple, and it was conformed that this temperature monitoring method by electrochemical technique certainly functioned.