Time-varying Temperature Conditions Research Articles

A Fast Dielectric Response Measurement Instrument With Dynamic Power Supply Tracking and Active Suppression of Temperature Fluctuations

This paper proposes a fast frequency domain dielectric spectroscopy (FDS) test scheme for diagnosing oil-paper insulation. The high-voltage output section introduces dynamic power supply tracking (DPST) and cascading techniques to generate high-voltage excitation signals. The DPST can increase the amplifier's voltage slew rate (SR) from 13 V per microsecond to 36.3 V per microsecond without crossover distortion. In the micro-current measurement section, the error active suppression structure of bias current and bias current under time-varying temperature conditions is proposed to improve the temperature stability of the measurement circuit. The fast testing technology shortens the test period by 65% in the low-frequency band by using the method of multi-frequency signal synthesis and partial cycle waveform fitting. The experimental results show that the total harmonic distortion and noise (THD+N) of the output voltage of the instrument are less than or equal to 0.078%. The voltage range is 780 Vpp. The standard deviation of current is less than 1.5%, and the standard deviation of tan δ is less than 1.7%, which verifies the stability of the designed instrument.

Lithium-Ion Battery Cell Open Circuit Fault Diagnostics: Methods, Analysis, and Comparison

Battery fault diagnosis has great significance for guaranteeing the safety and reliability of lithium-ion battery (LIB) systems. Out of many possible failure modes of the series–parallel connected LIB pack, cell open circuit (COC) fault is a significant part of the causes that lead to the strong inconsistency in the pack and the reduction of pack life. Therefore, it is extremely important to diagnose COC faults in real time. Motivated by this fact, we propose Kirchhoff's law based method, short-time Fourier transform based method, the Pearson correlation coefficient based method, dual extended Kalman filter (DEKF) based method, and long short-term memory recurrent neural network based method for diagnosing COC fault. These diagnostic approaches do not rely on other sensor data except pack current and terminal voltages of modules composed of cells in parallel. Furthermore, several experiments on a 4S-3P battery pack are taken under time-varying ambient temperature conditions to evaluate and compare the computation cost, diagnostic delay, and accuracy of these approaches. Test results show that only the DEKF-based approach owns the weakest robustness, and Kirchhoff's law based method with the merit of the lowest diagnostic delay and computation cost is the most appropriate approach for online COC fault diagnosis.

A data-driven evaluation method for low-temperature performance of lithium-ion batteries

Energy storage system plays an important role in smoothing out the electricity supply from renewable energy and improving stability of the power system. At present, most energy storage systems are still battery energy storage systems (BESS). However, the time-varying temperature condition has a significant impact on discharge capacity of lithium-ion batteries. When lithium-ion battery operates in a low temperature environment, the discharge capacity of the battery decreases. Therefore, this paper develops a discharge capacity evaluation method for lithium-ion batteries at low temperature. Firstly, we analyze the battery discharge characteristics. On this basis, battery tests have been conducted and we proposed some health indicators. Finally input the measured data and health indicators into the machine learning model. The applicability and effectiveness of this method are analyzed through numerical results.

A novel streamlined particle‐unscented Kalman filtering method for the available energy prediction of lithium‐ion batteries considering the time‐varying temperature‐current influence

Effective energy prediction is of great importance for the operational status monitoring of high-power lithium-ion battery packs. It should be embedded in the battery system performance evaluation, energy management, and safety protection. A new Streamlined Particle-Unscented Kalman Filtering method is proposed to predict the available energy of lithium-ion batteries, in which an Adaptive-Dual Unscented Transform treatment is conducted to realize the precise mathematical expression of its working conditions. For the accurate mathematical description purpose, an improved Synthetic-Electrical Equivalent Circuit modeling method is introduced into the internal effect equivalent process considering the influence of time-varying temperature and current conditions. As can be known from the experimental results, the proposed prediction method has a maximum estimation error of 2.27% and an average error of 0.80%, for the complex varying-current Beijing Bus Dynamic Stress Test. Under the Urban Dynamometer Driving Schedule working conditions, the available energy prediction has high accuracy with a maximum error of 1.83% and a voltage traction error of 3.28%. It provides vehicle-mounted available energy prediction schemes for effective management and safety protection of high-power lithium-ion batteries. Highlights A new Streamlined Particle-Unscented Kalman Filtering method is proposed to predict the available energy of lithium-ion batteries. Improved Synthetic-Electrical Equivalent Circuit modeling strategies are established to describe the nonlinear battery characteristics. Adopted predictive correction is investigated by considering the time-varying temperature and current influence. For effective convergence, an adaptive windowing function factor is introduced into the correction process with a maximum estimation error of 2.27% and an average error of 0.80% for the complex varying-current Beijing Bus Dynamic Stress Test working conditions. The vehicle battery available energy prediction is realized with a maximum error of 1.83% and a maximum voltage traction error of 3.28% for the Urban Dynamometer Driving Schedule working conditions.

Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition

Time-varying temperature condition has a significant impact on discharge capacity and aging law of lithium-ion battery. Consequently, a novel remaining useful life (RUL) prediction method for lithium-ion battery under time-varying temperature condition is developed in this paper. Firstly, a stochastic degradation rate model based on Arrhenius temperature model is proposed, and an interesting battery capacity conversion path from random temperature condition to reference temperature condition is established. Secondly, the aging model of lithium-ion battery under time-varying temperature condition is developed based on Wiener process, and a two-step unbiased estimation method based on maximum likelihood estimation (MLE) combined with genetic algorithm (GA) is proposed. Next, the random parameter is online updated under Bayesian framework. Then the probability density function (PDF) of the RUL for lithium-ion battery under time-varying temperature condition is derived. Finally, a case study is implemented to verify the effectiveness, and the results show that the proposed prediction method has higher accuracy and smaller uncertainty.

State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning

This study investigates accurate state of charge estimation algorithms for lithium-ion batteries based on the long short-term memory recurrent neural network and transfer learning. The long short-term memory network with the five typical layer topology is firstly constructed to learn the dependency of state of charge on measured variables. The transfer learning algorithm with fine-tuning strategy is then exploited to regulate the parameters of fully connected layer and share the knowledge of other layers. By this manner, the information from the source data can be applied to predict state of charge of other batteries with less training data. Additionally, a rolling learning method is developed to update the model parameters when the battery capacity is degraded. The precision and robustness of the proposed framework are comprehensively validated through comparative analysis of multitudinous sets of hyperparameters and methods. The experimental results manifest that the developed framework highlights precise estimation capability of state of charge at different aging states and time-varying temperature conditions. In addition, the proposed algorithm is verified feasible when transferred to different batteries based on only 30% training data.

Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures

Ambient temperature alters properties of lithium-ion batteries and affects the accuracy of estimation of state of charge (SOC), which is an important function to ensure the safety and reliability of electric vehicles. An accurate SOC estimation is critical under various temperatures. The existing methods have two problems: 1) need to perform time-consuming pre-experiments to investigate influence of various temperatures on the battery properties, and 2) use of the Thevenin model which is inaccurate at sub-zero temperatures. This study proposes an innovative lumped-battery model to improve the accuracy of both SOC estimation and battery modeling without pre-experiments. Two main causes of modeling errors by the Thevenin model are analyzed. Proposed model parameters are estimated using the recursive least squares, and the extended Kalman filter is used to estimate SOC in real time. Experiments are conducted under time-invariant and time-varying temperature conditions ranging from −10 °C to 30 °C. The results indicate that relative errors of battery modeling are less than 2.4% and that estimation errors of SOC are at most 0.4% under various temperatures. Therefore, the proposed method can be conveniently and widely applied to all-climate battery management systems to achieve a high accuracy of SOC estimation.

Stage of Charge Estimation of Lithium-Ion Battery Packs Based on Improved Cubature Kalman Filter With Long Short-Term Memory Model

Accurate estimation of the state of charge (SOC) of lithium-ion battery packs remains challenging due to inconsistencies among battery cells. To achieve precise SOC estimation of battery packs, first, a long short-term memory (LSTM) recurrent neural network (RNN)-based model is constructed to characterize the battery electrical performance, and a rolling learning method is proposed to update the model parameters for improving the model accuracy. Then, an improved square root-cubature Kalman filter (SRCKF) is designed together with the multi-innovation technique to estimate the battery cell's SOC. Next, to cope with inconsistencies among battery cells, the SOC estimation values from the maximum and minimum cells are combined with a smoothing method to estimate the pack SOC. The robustness and accuracy of the proposed battery model and the cell SOC estimation method are verified by exerting the experimental validation under time-varying temperature conditions. Finally, real operation data are collected from an electric-scooter (ES) monitoring platform to further validate the generalization of the designed pack SOC estimation algorithm. The experimental results manifest that the SOC estimation error can be limited to 2% after convergence.

Dynamic analysis of sustainable biogas-combined-cycle plant: Time-varying demand and bioenergy with carbon capture and storage

Anaerobic-digestion degrades organic wastes, producing biogas to drive bioenergy turbines reducing fossil-fuel dependence. This work evaluates the biogas chain fromwaste-to-bioenergy, using models for dynamic simulation of biogas processing, bioenergy generation, and carbon capture. To do this, a dynamic anaerobic digestion model from the literature was expanded to implement dependence on time-varying external temperature and feedstock conditions and to introduce aspects of transient power plants with time-dependent electricity demand. Results demonstrate that external temperature and feedstock oscillations promote dynamic cycles in biogas production which have to be handled in a dynamic biogas-combined-cycle power plant with post-combustion carbon capture to match costs and transient electricity demand. Consequently, dynamic biogas-storage and bioenergy-storage were implemented to cope with demand/production fluctuations. Bioenergy-storage is accomplished via compressed-air-storage during electricity surplus periods to drive pre-heated turboexpanders during deficit periods. The biogas-combined-cycle, without carbon capture and bioenergy-storage, achieves 15 years of payback-time and 2.6 MMUSD net value. On the other hand, the sustainable configurations with/without bioenergy storage – both bioenergy with carbon-capture and storage systems achieving 1030 kg/h of negative emissions – reach feasibility for CO2 prices of 75 USD/tCO2 attaining net values of 2.02 and 0.18 MMUSD with 21 and 31 years of payback-time respectively.This implementation demonstrates that bioenergy storage with carbon negative emissions is feasible for small-scale biogas-fired combined-cycle plants.

Method of reducing thermal-induced errors of a fiber optic gyroscope by adding additional winding layers.

Thermal-induced errors have a significant impact on the environmental adaptability of a fiber optic gyroscope (FOG). Reasonable winding methods can reduce and offset the thermal-induced errors. However, complex methods put higher requirements on winding accuracy. By adding additional winding layers on the outer surface of the fiber coil, an improved winding method to reduce the temperature error of FOG is proposed in this paper. Simulations in temperature-control conditions and time-varying temperature conditions are performed. Simulation and experimental results show that additional winding layers lead to a satisfactory reduction of thermal-induced rate errors. With parameter estimation and error compensation, thermal-induced errors can be further reduced.

Frequency domain dielectric response of oil gap in time-varying temperature conditions

The frequency domain dielectric response method has been one of the most effective approaches for the condition assessment of transformers. Prior to conducting the test, the transformer should be taken offline and given adequate time to reach internal temperature equilibrium in order to diminish any temperature-related effects. This can greatly increase the maintenance time; moreover, there is not enough time for some special transformers to reach this internal temperature equilibrium; for example the traction transformers used in the railway industry. The oil-paper insulation in transformers was composed of oil gap, oil-impregnated insulation paper (board). The study related to the dielectric response of the oil gap under time-varying temperature conditions is the basis for studies regarding oil-paper insulation. In this work, the frequency domain dielectric spectrums of oil gap were tested in three cases with different temperature change rates, and the currents in the three cases were tested when the oil gap was applied with low frequency excitations. A current model of the oil gap has been presented here, which is applicable to the time-varying temperature conditions. The current model was verified by comparing the simulated and measured currents. During the verification process, the parameters used in the current model were obtained on the basis of experimental tests. When combined with the established model, a method was developed to obtain the modified results in time-varying temperature conditions; this method was verified by comparing the results calculated from the proposed model with experimentally measured values. In the modification process, the parameters used in the current model were obtained by directly analyzing the tested frequency domain dielectric spectrum of oil gap, rather than through experimental tests, which is more suitable for the maintenance of field transformers. Lastly, the frequency domain dielectric spectrum of oil gap tested in time-varying conditions can be modified based on the proposed model and method.

Numerical solution of a microbial growth model applied to dynamic environments

The Baranyi and Roberts model is one of the most frequently used microbial growth models. It has been successfully applied to numerous studies of various microorganisms in different food products. Under dynamic conditions, the model is implicitly formulated as a set of two coupled differential equations which could be numerically solved using the Runge–Kutta method. In this study, a simplified numerical solution of the coupled differential equations was derived and used to simulate microbial growth under dynamic conditions in Microsoft Excel. As expected, the results obtained were the same as those from solving the coupled differential equations using a MATLAB Solver. In addition, model parameters were accurately identified by fitting the numerical solution to simulated growth curves under dynamic (time-varying) temperature conditions using the Microsoft Excel Solver.

The fundamentals of thermal-mass diffusion analogy

The fundamentals in thermal-mass diffusion analogy are developed and the misunderstandings are clarified. The continuity of the “wetness” or, more appropriately, the “fractional saturation”, w=C/Csat, is fundamentally proven using the equality principle of chemical potential. The validity of the analogy is independent of the linearity of the sorption isotherm. The mass diffusion equation with fractional saturation as the field variable is valid for source solute that is in either gaseous, liquid, or solid phase; and for absorbents that could be a combination of gaseous, liquid, and solid phases. Mass diffusion under complex conditions such as spatially non-uniform temperature and temporally varying source density/pressure can be readily modeled. However, discontinuity in fractional saturation could occur along the interface of two absorbents when diffusion occurs under a time-varying temperature condition. A novel technique – the internal source technique – has been developed that enables modeling of diffusion under such a condition using the standard thermal module in commercial finite element software.

A Pilot-Scale Reactor for the Study of Gas Emissions from Composting

Composting is generally accepted as an environmentally benign process for organic waste disposal. However, when not properly managed, composting can result in the emission of toxic and environmentally hazardous gases. Due to the potential negative consequences of composting, there is a need to gain a better understanding of the physical conditions that affect these volatile emissions in order to better control them. The objective of this project was to construct a pilot-scale compost reactor as a platform to study the potential impact of temperature, O2 concentration, airflow rate, and moisture content on the composting process. The pilot-scale reactor was able to control the temperature and O2 concentration inside the compost using an automated control algorithm. FTIR spectroscopy was used to continually measure the concentrations of CO2 and CH4 under time-varying temperature and O2 concentration conditions. This functionality will form the basis of future work to correlate physical process parameters with the volatile emissions from the compost process.

Dynamic approach for assessing food quality and safety characteristics: The case of processed foods

Thermal processes applied to foods focus on destruction of target microorganisms. Besides these processes often occur under time-varying temperature environments, microbial inactivation is commonly assessed under isothermal conditions. The development of mathematical models that describe microbial kinetics in dynamic conditions is important for processes design. Another process that occurs under time-varying temperature conditions is frozen storage of foods.Thermal inactivation of Listeria innocua (surrogate of L. monocytogenes) in parsley, and quality degradation of vegetables under frozen conditions were used as case-studies.Developed models that include time- temperature effects accurately described inactivation/degradation of the characteristics analyzed both under isothermal and dynamic conditions.

Stress-adaptive responses by heat under the microscope of predictive microbiology

In previous studies the microbial kinetics of Escherichia coli K12 have been evaluated under static and dynamic conditions (Valdramidis et al. 2005, 2006). An acquired microbial thermotolerance following heating rates lower than 0.82 degrees C min(-1) for the studied micro-organism was observed. Quantification of this induced physiological phenomenon and incorporation, as a model building block, in a general microbial inactivation model is the main outcome of this work. The microbial inactivation rate observed (k(obs)) under time-varying temperature conditions is studied and expressed as a function of the heating rate (dT/ dt). Hereto, a model building block related to the microbial physiology (k(phys)) under stress conditions is developed. Evaluation of the performance of the developed mathematical approach depicts that physiological adaptation is an essential issue to be considered when modelling microbial inactivation. Consideration, at a mathematical level, of microbial responses resulting in physiological adaptations contribute to the reliable quantification of the safety risks during food processing. By taking into account the physiological adaptation, the microbiological evolution during heat processing can be accurately assessed, and overly conservative or fail dangerous food processing designs can be avoided.

Surface decontamination of beef inoculated with Salmonella Typhimurium DT104 or Escherichia coli O157:H7 using dry air in a novel heat treatment apparatus

To determine the effectiveness of a novel dry air decontamination apparatus in the deactivation of Salmonella serotype Typhimurium DT104 or Escherichia coli O157:H7 on beef surfaces. A laboratory scale dry air decontamination apparatus, capable of producing repeatable and known heating time-temperature cycles on food surfaces was used in decontamination trials. Beef samples were surface inoculated with 7-8 log10CFU cm(-2) of S. Typhimurium DT104 or E. coli O157:H7 and heated at 60, 75, 90 and 100 degrees C using fast and slow heating rates and subsequently held at these temperatures for up to 600 s. A substantial reduction in pathogen numbers was achieved at higher temperatures (90 and 100 degrees C, 4.18-6.06 log10CFU cm(-2)) using both heating rates, but cell survival at these temperatures was also observed. At the lower temperatures, deactivation was small at 60 degrees C in particular it was less than one log unit after 3 min heating. No significant differences were observed when total reductions in pathogen counts were compared for all the temperature/heat up time combinations tested. During slow heating at 90 degrees C, and both heating rates at 100 degrees C, the pattern of deactivation of S. Typhimurium DT104 or E. coli O157:H7 was triphasic. This study has shown that heating meat surfaces with dry air can achieve substantial reductions in S. Typhimurium DT104 or E. coli O157:H7. As surface decontamination of beef surfaces with dry air had a negative effect on beef colour and appearance, such a decontamination apparatus would be unsuitable for producing meat for retail sale but it could be used to produce safer meat for use in the catering trade. This study provides researchers and food processors with data on the dynamic changes in S. Typhimurium DT104 and E. coli O157:H7 counts on intact beef surfaces during heating with dry air under realistic (time-varying) temperature conditions.

4001 生分解性高分子nPLAの非弾性挙動への温度、ひずみ速度、負荷履歴の効果と数値解析(S25-1 非弾性変形,S25 材料・構造の非弾性挙動・高温強度・損傷評価)

The development of reliable constitutive equations for solid polymers has become of interest recently due to the growing use of polymeric materials in structure, and especially, biodegrability polymers such as polylactic acid, PLA are noticeable from the environmental-friendly view point. In this study, monotonic compressive deformation tests were conducted under time-varying strain rate and temperature conditions of 1.1×10^<-2>〜10^<-5>s^<-1> and 10〜60℃ for nanocomposite-processed PLA (nPLA). Experimental stress-strain responses were compared with those of typical engineering polymers: PE, PP and PD, and some important deformation characteristics are revealed regarding stiffness, work-hardening and softening, overshoot phenomena in stress jumping conditions, stress-strain responses at elevated temperature, and inapplicability of time-temperature equivalence. The viscoplastic constitutive model based on overstress, VBO has reproduced basic experimental results successfully.

Predicting the reduction in microbes on the surface of foods during surface pasteurisation—the ‘BUGDEATH’ project

The prime objective of the BUGDEATH project was to produce accurate predictive models of the reductions in microbial numbers that can be achieved on the surface of foods during surface pasteurisation processes. These models will enable a wide range of food manufacturers to design more effective and efficient surface pasteurisation treatments than can be produced with current microbial death models and data. During the project test apparatus was built and delivered to partners that can create ‘rapid’ heating processes, where surface temperatures rise from 5 to 100 °C in less than 1 min, can be held at a set temperature and then cooled rapidly. Slower heating and cooling processes can be carried out to compare the effects of heating and cooling times on bacterial death. Both dry and wet (steam) heating were possible. The partners carrying out microbiological trials used the test apparatus. Bacterial death was monitored by viable counts and also by specialist techniques including use of a low light-level camera and bacteria tagged with lux genes. Using organisms which have been treated by adding lux genes to make them glow (bioluminescence), and applying them to the food surface to be treated, the scientists can quickly measure changes taking place in the bacteria. If the bioluminescence fades when the food is treated then the process is effective. The bacteria glow brightly when healthy, fade when expiring and stop glowing the moment they stop metabolizing. The major aim of the project was to create a user-friendly heat transfer and microbial death model. These models were validated against published data and data provided by the partners. The programme simulates inactivation kinetics of microorganisms on food surfaces, during dry and wet pasteurisation treatments under constant and time-varying temperature conditions. On the basis of selection of a heating regime of the medium, the programme allows accurate prediction of food surface temperature and simulates the microbial load content along the whole process time. Input data and simulated values can be visualised in graphics or data tables. Printing, exporting and saving file options are available. It includes a useful database of foods (i.e. beef and potato) and related thermal properties, microorganisms (i.e. Listeria monocytogenes and Salmonella) and corresponding inactivation kinetic parameters. The software can be used to simulate results during pasteurisation treatments. The pull off simulations can be valuable to a wide variety of companies in the food industry for developing appropriate and safe processes. The software has also the potential of being exploited for educational purposes. The project involved 8 partners from 5 countries and was coordinated by FRPERC from the University of Bristol, UK. As part of the project the research team recruited an Industrial Advisory Group (IAG) to ensure that the project was relevant and useful to the European food industry.

Integrated approach on heat transfer and inactivation kinetics of microorganisms on the surface of foods during heat treatments—software development

The objective of this work was to create a software application (Bugdeath 1.0) for the simulation of inactivation kinetics of microorganisms on the surface of foods, during dry and wet pasteurisation treatments. The program was developed under the Real Basic © 5.2 application, and it is a user-friendly tool. It integrates heat transfer phenomena and microbial inactivation under constant and time-varying temperature conditions. On the basis of the selection of a heating regime of the medium, the program predicts the food surface temperature and the change in microbial load during the process. Input data and simulated values can be visualised in graphics or data tables. Printing, exporting and saving file options are also available. Bugdeath 1.0 includes also a useful database of foods (beef and potato) and related thermal properties, microorganisms ( Salmonella and Listeria monocytogenes) and corresponding inactivation kinetic parameters. This software can be coupled to an apparatus developed under the scope of the European Project BUGDEATH (QLRT-2001-01415), which was conceived to provide repeatable surface temperature-time treatments on food samples. The program has also a great potential for research and industrial applications.