Charge Temperature Research Articles

Uncertainties of critical temperatures based on higher-order fluctuations of the largest fragment charge* *Supported by the National Natural Science Foundation of China (11875328, 12075327), the Key Laboratory of Nuclear Data foundation (JCKY2022201C157), and the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (22lgqb39)

The new signature of liquid-gas phase transition has been well indicated by the higher-order fluctuations of the largest fragment charge, but the uncertainties of critical temperatures based on this signature have not been revealed. This study extracts the critical temperatures of liquid-gas phase transition in nuclear reactions and investigates their uncertainties. Utilizing the isospin-dependent quantum molecular dynamics model in conjunction with the statistical model GEMINI enables us to describe the dynamical path from the initial to the final state. An isotope thermometer and a quantum fluctuation thermometer are employed to extract the nuclear temperature. The higher-order fluctuations of the largest fragment charge and critical temperatures are studied in 124Sn + 120Sn collisions ranging from 400 to 1000 MeV/nucleon and 124Sn + AZ collisions at 600 MeV/nucleon. Observations revealed that the pseudo-critical point is robustly indicated by the higher-order fluctuations of the largest fragment charge. The critical temperatures extracted by the isotope thermometer are relatively consistent, with an uncertainty of 15%, while those obtained by the quantum fluctuation thermometer are heavily influenced by the incident energy and mass number of target nuclei. The excitation energy and bound charge are used for event-sorting. These two ensembles represent the statistical properties of the initial and final states of the system, respectively. The initial-final correlations of statistical properties might lead to two phenomena. First, the size distribution of the largest fragment at the pseudo-critical point based on the ensemble is wide, while that based on ensemble exhibits bimodality, which is a typical characteristic in the liquid-gas coexistence of a finite system. Second, the temperature at the pseudo-critical point based on the ensemble is higher than that based on the ensemble. Furthermore, the projectile-like system exhibits a significant dynamical effect in its evolution path from the initial to final state, closely associated with the fluctuation of critical temperature.

Performance assessment and optimization of concentrated solar power plants with paired metal hydride-based thermochemical energy storage

The main objective of this study is to evaluate the innovative future concept of a concentrated solar power (CSP) plant, specifically focusing on a CSP-driven Rankine cycle. Additionally, a suggested energy storage solution for the analyzed CSP plant is the thermochemical energy storage (TCES) approach. The TCES system used paired metal hydrides integrated with phase change material (PCM). A comprehensive mathematical model has been developed to enable performance predictions for the entire CSP plant as well as each of its components. This model considers the thermal coupling between the solar field, the thermochemical storage system, and the power block in order to assess the overall performance of the CSP plant. Utilizing this successfully verified model with reference data, a parametric study is conducted on various operating parameters to identify optimal operating points for the storage system in relation to the overall efficiency of the CSP plant. Furthermore, the critical issue of appropriate and novel concepts for MHs-based TCES integration in CSP plants is discussed in detail. The CSP plant's performance was examined as a function of storage cycle duration, charging and discharging temperatures, melting PCM temperature, and thermal efficiency. Based on the findings, it has been observed that higher charging temperature and integration of PCM into the thermochemical energy storage (TCES) system can lead to a significant reduction of 13-fold in the required solar collector area and a notable increase of 77 % in the efficiency of the CSP plant. Thus, the proposed TCES system appears to be the most promising future heat storage system for CSP plants.

IOT BASED BATTERY MONITORING SYSTEM FOR ELECTRIC VEHICLE

The rapid adoption of electric vehicles (EVs) represents a critical step towards reducing greenhouse gas emissions and achieving sustainable transportation solutions. One of the key components in an EV is its battery, which plays a pivotal role in determining the vehicle's performance, range, and overall efficiency. Ensuring the health and longevity of EV batteries is essential for promoting EV adoption and optimizing their operational capabilities. This project presents an IoT-based Battery Monitoring System (BMS) designed to address the challenges associated with monitoring and managing the batteries in electric vehicles. The system leverages the power of Internet of Things (IoT) technology to continuously gather real-time data from the EV's battery pack. The IoT-based BMS consists of various components, including battery sensors, microcontrollers, wireless communication modules, and cloud-based data analytics. Battery sensors collect data on parameters such as voltage, current, temperature, and state of charge (SoC) from individual cells within the battery pack. In conclusion, this project presents an innovative IoT-based Battery Monitoring System that has the potential to revolutionize the way we manage and maintain electric vehicle batteries. Key Words: Battery pack, Electric vehicle Voltage, State of charge, Vehicle performance.

Technology of forming a wear-resistant thermite alloy layer based on the Fe-Cr-C system by self-propagating high-temperature synthesis

The technology of forming a thermite alloy layer on the basis of Fe-Cr-C system on a metal basis by self-propagating high-temperature synthesis is offered, which allows to obtain cast functionallayers withphysico-mechanical and exploitativeproperties. The optimal technological parameters of the forming a wear-resistant layer of thermite alloy process are determined: the amount of metal filler with the maximum yield of suitable alloy, the heating temperature of the thermite charge and the mold to obtain additional heat, the temperature ranges of the melt to melt the metal base with further formation of the functional layer. Metallographic studies of the obtained wear-resistant layer of thermite allo[y showed that the zone of formation of the functional layer is characterized by the stability of the macrostructure and the positive effect of non-metallic inclusions in the form of Al2O3, which influences the formation of chromium carbides in the obtained alloy by creating the effect of inoculative modification of thermite alloy.

Exacerbated High‐Temperature Calendar Aging of SiOx‐Graphite Electrode Induced by Interparticle Lithium Crosstalk

AbstractSilicon oxide‐graphite (SiOx‐G) composites are promising anode materials for building practical high‐energy Li‐ion batteries. To acquire long and safe operation of battery, extensive efforts are made to maintain stable Li storage of SiOx‐G against materials aging and the accompanied performance fade. While previous studies mostly focus on the cycling aging, the calendar aging occurred during battery storage at a high state of charge or high temperature has not received sufficient attention. In this work, a mechanism study on the calendar aging chemistry of fully lithiated SiOx‐G electrodes in half‐cells both at ambient and high temperature (60 °C) is performed. Unmodified SiOx is employed as active materials to inspect the change of thermodynamics properties in the bulk and at interfaces. By excluding the interference from cathode, it is revealed that besides aggravated parasitic reactions happening at interface, Li migration from the lithiated graphite to the vicinal SiOx particles is also responsible for calendar aging of SiOx‐G electrodes, and high‐temperature storage notably accelerates the aging process. This work enriches the fundamental understandings about the multifactor‐coupled aging process of anode materials and sheds lights on rational materials design toward improved calendar life of a high‐energy rechargeable battery.

Deep learning-based prediction of lithium-ion batteries state of charge for electric vehicles in standard driving cycle

Significant climatic shifts occurred during this period, resulting in threatening consequences for people's lives and industries. Global warming is one of the most serious consequences of these climate changes, and it has disastrous consequences for daily human existence. The utilization of public transportation has been encouraged at the societal level as a solution. An all-electric vehicle is evaluated in this research paper. Using two various types of regular driving cycles, we were able to evaluate the battery performance of electric vehicles (EVs). The variables influencing vehicle performance, such as battery state of charge (SOC), energy consumption, and battery functioning temperature, are investigated. The results demonstrate that the rate of urban traveling significantly impacts travel efficiency and the range. In addition, owing to the significance of battery capacity, the influences of different variables on forecasting battery state of charge were assessed in the second step. The results show that the driving behavior and acceleration rate of the vehicle influence the SOC of the battery. The results of this study also showed that city driving has a significant effect on Ev performance in the travel distance range.

Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks

Battery-powered electric vehicles (EVs) are poised to accelerate decarbonization in nearly every aspect of transportation. However, safety issues of commercial lithium-ion batteries related to the faults and failures in real-world applications are still serious concern. Even a small increase in risk during the battery's operational lifetime may evolve into a safety hazard-fire and explosion, named as thermal runaway, after long-term incubation. Modelling and predicting the evolution of nonlinear multiscale electrochemical systems is challenging due to uncertainties in materials and manufacturing processes, dynamic environmental and operating conditions, as well as a lack of high-quality datasets. This challenge is further complicated when solving real-life physical problems with missing and noisy data and uncertain boundary conditions. In this study, we address these challenges by developing a specialized Transformer network architecture called BERTtery (Bidirectional Encoder Representations from Transformers for batteries) based on field data of EVs. By using charging voltage and temperature curves from early cycles before exhibiting symptoms of battery, the two-tower Transformer with temporal-wise encoder and channel-wise encoder is demonstrated as a powerful tool to capture early-warning signals across multiple spatio-temporal scales under a wide range of operating conditions. The method reliably predicts the evolution of faults in battery systems using only data provided by the onboard sensor measurements of battery performance.

A fast data-driven battery capacity estimation method under non-constant current charging and variable temperature

Data-driven methods have been widely used in capacity estimation of lithium-ion batteries. Non-constant current charging and variable-temperature operating scenarios are inevitable in real applications. However, existing classical constant current charging based capacity estimation methods may not be applied to such scenarios. To this end, a data-driven method adapted to non-constant current charging is proposed in this work. A single health feature collected within 3 min is combined with Gaussian process regression (GPR) to achieve fast and transferable battery capacity estimation. To validate the effectiveness of the proposed method, ten batteries are performed for aging tests using a non-constant current charging protocol. The validation results show that the proposed method's average capacity estimation error (mean absolute percentage error) is only 0.65 %. The estimation error is reduced by more than 30 % for the new temperature condition. Compared with the three existing classical methods, the capacity estimation accuracy of the proposed method is improved by 69 %, 82 %, and 68 %, respectively. Additional experiments demonstrate the generality of the proposed approach for different battery chemistries and charging protocols. This work opens the door to battery capacity estimation for non-constant current charging scenarios.

AdS/BCFT Correspondence and Horndeski Gravity in the Presence of Gauge Fields: Holographic Paramagnetism/Ferromagnetism Phase Transition

AbstractThis paper presents a dual gravity model for a (2+1)‐dimensional system with a limit on finite charge density and temperature, which will be used to study the properties of the holographic phase transition to paramagnetism‐ferromagnetism in the presence of Horndeski gravity terms. In our model, the non‐zero charge density is supported by a magnetic field. As a result, the radius indicates a localized condensate, as the Horndeski gravity parameter is increased, that is represented by γ. Furthermore, such condensate shows quantum Hall‐type behavior. This radius is also inversely related to the total action coefficients of our model. It is observed that increasing the Horndeski parameter decreases the critical temperature of the holographic model and leads to the harder formation of the magnetic moment at the bottom of the black hole. However, when removing the magnetic field, the ferromagnetic material presents a disorder of its magnetic moments, which is observed through the entropy of the system. The authors also found that at low temperatures, spontaneous magnetization and ferromagnetic phase transition.

Application of layer view factor method in high temperature thermal storage packed bed

This study provides a comprehensive evaluation of radiation effects on the thermal behavior of a packed bed using a discrete model for heat transfers integrating a novel method called Layer View Factor for view factors estimation, which significantly decreases computational time by less than 9000 times compared to the parallel ray tracing method. The model is validated and strongly aligned with experimental data at 823K, with a maximum mean bias and root mean square errors of ±4.5K and 10K, respectively. Analyses of temperature profiles and thermocline lengths for charging, discharging, and stand-by processes at a charge temperature of 1473K were performed, finding a thermal flattening effect with radiation, leading to lower peak temperatures and quicker thermocline length evolutions. Notably, the stand-by process exhibited the largest impact, with a maximum temperature difference of up to 100K compared to the non-radiation case. Moreover, analysis of the temperature and energy of discharge found that the maximum discharge temperature drops below 90% of the charge temperature when radiation is included. Finally, efficiency analysis of the processes showed differences of up to 2% between having or not the radiation effect, aiming its role as a redistribution mechanism in the packed bed’s temperature.

Emission and in-cylinder combustion characteristics of a spark ignition engine operated on binary mixtures of gas and liquid fuels

In this study, three different gas fuels such as hydrogen, propane, methane and six different liquid fuels such as, benzene, gasoline, toluene, hexane, methanol and ethanol have been combined and combustion characteristics have been simulated for a spark ignition engine (SIE). Variation of in-cylinder temperature, in-cylinder pressure, CO, CO2 and NO has been investigated. The gas fuel ratios in the mixtures were changed between 0 and 100% by mass. It was investigated that how the addition of gaseous fuels to liquid fuels at different ratios affects the combustion characteristics and emission formation. The gas fuel ratios considerably affected emission and combustion specifications of the engine. Increasing liquefied gas fuel ratio in the mixture leads to decrement in the temperature of charge. The maximum pressure first increases with increasing gas fuel ratio and then start to minimize after maximum pressure. The maximum peak pressure values are observed at 50% gasoline and 50% methane, 75% gasoline and 25% propane, 50% gasoline-50% hydrogen as 48.1 bar, 46 bar and 62.4 bar for different gas fuel mixtures. The minimum peak pressure value is displayed at 100% methanol as 44.06 bar. The maximum temperature value is observed at 100% benzene as 2582 K and its minimum value seems at 100% hydrogen as 2059 K. The maximum mass fractions of CO, CO2, NO are observed at 100% benzene as 0.0271, 0.161, 0.0076, the minimum mass fraction of CO2 is observed at 100% methane as 0.095. The minimum mass fraction of CO is observed at 100% methanol as 0.01243. The minimum mass fraction of NO is observed at 100% hydrogen as 0.00085. The fuels are ranked using a multi-criteria decision-making algorithm called AHP after evaluating the values of all performance criteria and their respective sub-criteria, including in-cylinder pressure, in-cylinder temperature, NO, CO2 and CO to get preference rank between fuels.

Latent Thermal Energy Storage for Solar Industrial Drying Applications

Solar heat is an attractive alternative in industrial processes. However, the intermittent and stochastic nature of solar energy necessitates the use of heat storage systems to bridge the gap between heat production and demand. This study introduces a validated numerical analysis approach to investigate the performance of latent storage tanks filled with spherical phase-change materials. A 1D thermal model is developed to describe the charging processes of adipic acid as PCM. The study examines the performance characteristics of latent heat storage in terms of stored energy and duration through parametric investigations. For mass flow rates ranging from 600 to 1000 kg/h, storage durations were found to vary from 440 to 582 min. The storage duration decreased significantly from approximately 1150 min at a charging temperature of 160 °C to 470 min at a charging temperature of 240 °C. The bed porosity affected the storage process, with a porosity of 0.5 achieving a thermal energy storage of around 344 MJ but requiring a longer charging time of about 610 min. Higher heating rates allowed for lower storage durations, with storage durations of approximately 460 min for a heating rate of 3 °C/min, compared to 660 min, for a heating rate of 0.5 °C/min.

Summer thermal and energy performances assessment of a modular hydronic thermal barrier wall for ultra-low energy buildings - A field experimental study

Hydronic thermal barriers with ultra-low carbon emission attributes are progressively gaining prominence in the competition of thermal insulation solutions for opaque envelopes of low-energy buildings due to their excellent thermal performance and low-grade energy utilization potential. This paper introduces a novel approach involving a modular hydronic thermal barrier wall (MHTB) that incorporates preset pipe cavities and replaceable filling material. This proposition seeks to address the challenges confronted by conventional hydronic thermal barrier walls (CHTBs), specifically in terms of internal heat transfer enhancement and rapid assembly/dismantling. On-site thermal performance tests and energy-saving evaluations were conducted in summer conditions based on three scaled-down rooms with MHTB, CHTB and a conventional external insulation wall as the south walls. The results showed that the MHTB could significantly reduce heat passing through the south wall in summer, resulting in a decreased cooling load attributed to the wall and subsequently lowering indoor temperatures. At a charging temperature of 17.0 °C, the MHTB exhibited a reduction of 11.3 °C in interior surface temperature compared to the conventional external insulation wall, accompanied by a corresponding increase of 2.76 kWh·m−2 in cumulative heat flux. In addition, the MHTB could effectively mitigate inhomogeneous heat transfer within the wall, enhancing the overall heat transfer effect. Across the four investigated conditions, the MHTB with a steel grit-sand mixture as the filling material demonstrated an approximate 4.7% increase in cumulative heat flux and a 12.2% enhancement in cumulative cooling quantity compared to the CHTB. Overall, the findings substantiated the efficacy of the MHTB system and offered a crucial point of reference for advancing research in optimizing the performance and modular construction of hydronic thermal barrier walls.

Experimental study on charging energy efficiency of lithium-ion battery under different charging stress

Accurate measurement of the energy efficiency of lithium-ion batteries is critical to the development of efficient charging strategies. Energy efficiency is discussed in published work from the perspective of cell design, more than that, the insufficient probe of stresses influencing the energy efficiency of commercialized batteries while the conclusions coupled with the effect of battery temperature rise have limited engineering guidance value. Few papers specify test profiles for energy efficiency baseline. This paper designs a charging energy efficiency (CEE) test profile to present an offline map of baseline value for commercial ternary lithium-ion batteries. The time-frequency domain impedance characteristics are analyzed based on the distribution of relaxation times, providing a mechanistic explanation of the CEE evolution. The contributions of temperature, capacity-rate (C-rate), and state of charge (SOC) to the CEE are quantified by global sensitivity analysis and range statistics. The findings show that the proposed profile decouples the effect of temperature rise under forced convection conditions, and has an error of less than 0.04 % for 3 cycles compared to the FreedomCAR guidance of at least 10 cycles. The CEE-SOC curve shape is similar to “M”, which is attributed to the impedance curve shape of “W”, breaking the previous inherent experience of “lower impedance in the middle SOC”. The median of the Map is 0.964, concomitantly the outlier points are concentrated below 0.887 at 10 °C. The global sensitivity analysis carried out by the Sobol method and the range statistical indicates that the importance ranked is C-rate > Temperature > SOC, with the range statistical of each stress being 11.8 %, 10.223 %, and 7.51 %, respectively, as well as the total-order Sobol indexes being 0.56, 0.439, and 0.066, which is beneficial for optimizing the operating SOC interval of lithium-ion batteries and guiding the development of fast charging strategies.

Research on particle clogging during groundwater recharge of ground source heat pump systems

Ground source heat pump systems are effective ways to utilize shallow geothermal energy resources, but particle clogging can cause problems during groundwater recharge. In this research the recharge head, temperature of recharge water and recharge particle charge were selected as influencing factors, and the weights of the influencing factors were calculated by combining experiments and numerical simulations. Computerized tomography (CT). was used to obtain an internal pore grid of the medium with high precision, and a mathematical model was proposed to describe the movement of particles in the internal pores of the saturated porous medium. A mathematical model was constructed to simulate particle deposition under the simultaneous action of multiple influencing factors. The results showed that it was feasible to obtain a micron-scale (μm). pore grid by nondestructive testing and then apply it to describe the movement of particles in the pores of a real medium. According to the weight analysis, the temperature of recharge water greatly affects the occurrence and development of groundwater recharge particle clogging in ground source heat pump systems, and this influencing factor should be considered in the construction and operation of these systems.

An electric-thermal coupling modeling method for lithium-ion battery using the state of charge normalization calculation method

The improved equivalent circuit model for Lithium-ion battery (LIB) has gained popularity in engineering due to its practical and straightforward structure, encompassing core parameters of interest such as open-circuit voltage, internal resistance, temperature, and state of charge (SOC). However, the nonlinear and coupled nature of electrical and thermal parameters makes it challenging to solve these parameters online in a battery management system. Thus, this paper proposes a battery model to decouple the electric-thermal coupling relationship, comprising analytical sub-models for open-circuit voltage, internal resistance and thermal parameters. The proposed method involves designing sets of off-line experiments under constant-current battery conditions and normalizing SOC calculation by utilizing a coulomb efficiency coefficient. The electrical and thermal parameters are then decoupled using off-line parameter identification to obtain open-circuit voltage, internal resistance, and temperature data under specific conditions. An open circuit voltage modeling method is proposed based on the Nernst equation and fitting method. Then, an internal resistance model of the battery is also modeled based on the Arrhenius equation and fitting method. In addition, according to the principles of heat generation and heat transfer, a thermal model is constructed and its model parameters are identified. Through the verification of internal resistance and terminal voltage under different ambient temperature and current conditions, the results illustrate that the modeling method proposed in this paper is able to accurately estimate the open-circuit voltage, internal resistance, and temperature based on online operational data of the battery.

Comparison of Electrostatic Spray Drying, Spray Drying, and Freeze Drying for Lacticaseibacillus rhamnosus GG Dehydration

Spray drying (SD) is extensively used to encapsulate lactic acid bacteria in large-scale industrial applications; however, bacteria combat several harms that reduce their viability. In this study, a novel technique called electrostatic spray drying (ESD) was used to explore the benefits and disadvantages of using electrostatic charge and lower temperatures in the system. Freeze drying (FD) was used as a reference. The effect of different encapsulation agents, like maltodextrin, arabic gum, and skim milk, on the viability of Lacticaseibacillus rhamnosus GG (LGG) was investigated. The initial cell concentration, particle size distribution, aspect ratio, sphericity, scanning-electron-microscopy images, moisture content, water activity, glass transition, rehydration abilities, and survival during storage were compared. Skim milk was proven to be the best protectant for LGG, regardless of the drying process or storage time. A huge reduction in cell numbers (4.49 ± 0.06 log CFU/g) was observed with maltodextrin using SD; meanwhile, it was protected with minimum loss (8.64 ± 0.62 log CFU/g) with ESD. In general, ESD preserved more LGG cells during processing compared to SD, and provided better stability than FD and SD during storage, regardless of the applied voltage. The ESD product analysis demonstrated an efficient LGG preservation, close to FD; therefore, ESD presented to be a promising and scalable substitute for SD and FD.

Experimental and numerical investigation of thermally induced convection along a high-temperature borehole heat exchanger

Thermally induced convection in the vicinity of borehole heat exchangers (BHE) in porous media affects the heat transfer behaviour between the BHE and the surrounding ground and thereby their performance for building climatization, heat extraction and heat storage. This study presents a combined experimental–numerical analysis of the qualitative and quantitative impacts of convection on heat transfer for a laboratory-scale BHE analogue over a wide temperature range. For this purpose, a grouted coaxial high-temperature BHE was installed within a fully saturated porous sand medium in a cylindrical container of 1.4 m3 volume under spatially extensive temperature monitoring. Four transient heat charging/discharging experiments were performed at different charging temperatures of 30, 50, 70, and 90 °C and analysed for the onset and magnitude of convection. After minimal calibration, a numerical model accurately reproduced the experimental data of all experiments and helped quantify the contribution of convection to the total heat transfer at each temperature level. For heat charging at 30 °C, the heat transfer was dominated by conduction. At higher temperatures, convection increased the total heat-transfer rate by up to 35% in the experimental setup. In contrast, for heat discharge, due to thermal stratification in the sand medium, convection reduced the heat transfer rate and heat recovery by up to −30% and −35%, respectively. Rayleigh numbers, buoyant flow velocities, and the centre of heat were derived from the monitoring and simulation data and consistently indicated the onset of convection for the experimental setup at a BHE operating temperature of 50 °C. The Rayleigh number was found to be closely correlated with the magnitude of the convective flow. Overall, the results suggest that the performance of BHEs in cooling applications may benefit from thermal convection, while the performance of BHEs for thermal energy storage may deteriorate markedly at high operating temperatures.

Thermal analysis of modified solar-driven dual-evaporation Kalina system

In order to fulfill the further cascade utilization of mid-grade heat source and high efficient energy storage, the concept of dual-evaporation pressure has been adopted into a modified Kalina co-generation system, with the spiral baffle inserting the whole space of the temperature difference storage tank. The relative static/dynamic mathematics simulation has been conducted, to optimize the whole system. The influences of key parameters on the thermodynamic performance of the cycle have been discussed, including circulation multiple, super-heat degree as well as work and basic concentrations of solution. The results show that higher value of circulation multiple represents a negative factor for the system, and the optimal value is adopted as 5. While under the same heat source temperature, lower super-heat degree means a higher turbine inlet temperature and pressure, which will increase the thermal performance of the whole cycle. In addition, there exists optimal working pairs for the cycle under discussed condition. When work concentration is set as 0.375, 0.4, 0.425, 0.45, 0.475, the optimal matching basic concentration is 0.227, 0.244, 0.26, 0.272, 0.293, respectively. The optimal case study of proposed cycle shows that the thermal and exergy efficiency are found to be 32.66 % and 74.49 %, which are 20.1 %/33.3 % and 1.1 %/27.2 % higher than those of dual-evaporation pressure or conventional Kalina cycle. The cycle acquires higher thermal efficiency by realizing proper ammonia concentration and suitable temperature-difference in phase change processes to match heat source and cooling water, with the set of two evaporators to make the discharge temperature of heat source lower (92℃). What’s more, by forming a spiral channel in the large space of the cylinder, the fluid is evenly distributed and stayed in a laminar flow state, in which way the effective and stable temperature stratification is realized, stably providing the fluid with higher temperature. The collector efficiency appears lower at noon, higher at morning and night. The highest collect efficiency could be 64.7 %, with the lowest be 53.7 %. The charging temperature of storage tank could be 416 ℃, and the charging time could be around 2 h. The appearance of storage tank inserting spiral baffle is considerably better than that of the traditional double tank, with lower charging/discharging time (25.2 %/20 % shorter in time) and more intense temperature difference (5.1 % higher in charging temperature). In general, with the increase of the super-heat degree as well as the circulation multiple, the solar-to-power efficiency decreases rapidly, which could be up to 19.9 %.

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and PSO-GRNN algorithm

As a critical index in ensuring the safe and reliable deployment of lithium-ion batteries (LIBs), the estimation performance of state-of-health (SOH) seriously affects the popularization of electric vehicles (EVs). However, for the existing SOH estimation methods based on machine learning algorithms, the health features are manually extracted mainly based on personal experience, without algorithm-based automatic preferential selection. Thus, this study proposes a battery SOH estimation method based on the feature-importance ranking strategy and generalized regression neural network optimized by particle swarm optimization (PSO-GRNN). Firstly, 19 vital features were extracted from charging voltage, charging current, temperature, and incremental capacity (IC) curves. Secondly, the importance indexes for these features were calculated and optimally ranked by fusing the machine learning algorithm (random forest) and statistical analysis (Pearson correlation coefficient). Accordingly, different combinations of high-ranking features were compared to determine the best number of the final feature set. Thirdly, the SOH estimation model is built by the PSO-GRNN algorithm with optimal 7 high-ranking features. Based on the publicly available NASA datasets, the proposed SOH estimation model was validated on the data from a single-cell battery. Furthermore, the datasets from three cells with different cycling test conditions were utilized, where the data from two cells and the other cell were alternately selected as the training and test sets, respectively. The SOH estimation errors (squared absolute error and mean squared error) for single-cell and multi-cell experiments were less than 1 % and 3 %, respectively. Experimental results show that the proposed method is robust and can achieve accurate and lightweight SOH estimation.