Improved multiple feature-electrochemical thermal coupling modeling of lithium-ion batteries at low-temperature with real-time coefficient correction.

Monitoring various internal parameters plays a core role in ensuring the safety of lithium-ion batteries in power supply applications. It also influences the sustainability effect and online state of charge prediction. An improved multiple feature-electrochemical thermal coupling modeling method is proposed considering low-temperature performance degradation for the complete characteristic expression of multi-dimensional information. This is to obtain the parameter influence mechanism with a multi-variable coupling relationship. An optimized decoupled deviation strategy is constructed for accurate state of charge prediction with real-time correction of time-varying current and temperature effects. The innovative decoupling method is combined with the functional relationships of state of charge and open-circuit voltage to capture energy management effectively. Then, an adaptive equivalent-prediction model is constructed using the state-space equation and iterative feedback correction, making the proposed model adaptive to fractional calculation. The maximum state of charge estimation errors of the proposed method are 4.57% and 0.223% under the Beijing bus dynamic stress test and dynamic stress test conditions, respectively. The improved multiple feature-electrochemical thermal coupling modeling realizes the effective correction of the current and temperature variations with noise influencing coefficient, and provides an efficient state of charge prediction method adaptive to complex conditions

Spatial Overlay Analysis of Geochemical Singularity Index α-Value of Porphyry Cu Deposit in Gangdese Metallogenic Belt, Tibet, Western China

The statistical modeling with ILR-RPCA-back CLR has two problems when dealing with the closure effect of geochemical data. Firstly, after performing isometric logratio (ilr) transformation, robust principal component analysis (RPCA) is employed for processing. The double-plot diagram illustrates that the element sequence transformation occurs in the first and second principal components, while the unique principal component remains unattainable. Secondly, by transforming both the score and load into the centered logratio (CLR) space using the U matrix, it is possible to obtain a score result that corresponds to the original order of elements according to the CLR = ILR·U formula. However, for obtaining a load result that corresponds to the original order of elements, an alternative formula “CLR = UT·ILR” must be used instead. In order to determine the optimal element assemblage for porphyry copper deposits, this study conducted statistical analysis on mineral assemblages from discovered deposits in the Gangdese metallogenic belt and identified Cu, Mo, Au, Ag, W, and Bi as key elements associated with porphyry copper deposits. Subsequently, by analyzing the singularities of the composite elements, the spatial overlay of the combined element is carried out, and concentration-area (C-A) fractal filtering is applied to identify the anomaly and background areas. To facilitate comparison, we conducted an analysis of various mineral and ore deposit types, revealing the following findings: (1) Combination elements exhibit superior recognition capability than single elements in porphyry copper deposits; (2) Skarn-type copper deposits unrelated to porphyry show a high degree of dissimilarity compared to those related to porphyry; (3) this method offers advantages over the single element method in evaluating porphyry gold deposits by reducing anomaly levels and initial investment during the evaluation stage for porphyry copper anomalies; (4) However, this method has limited ability in distinguishing between porphyry copper and molybdenum deposits

Do greenhouse gas emissions affect financial performance? : an empirical examination of Australian public firms

Previous studies that have attempted to relate corporate environmental performance to financial performance have generated conflicting results. This paper presents the findings of a study on the relationship between greenhouse gas (GHG) emissions and the financial performance of Australian corporations. Using multiple regression models and data from a sample of 69 Australian public companies, this paper finds a positive correlation between GHG emissions and corporate financial performance. By testing the statistical significance of GHG emission factors in determining company Tobin's q, this study finds that a stronger Tobin's q often correlates with higher GHG emissions across all industry sectors. This finding is contrary to evidence found in previous studies conducted in other countries. The positive correlation found in this study could be explained with reference to the unique economic structure and development of Australia, particularly its dominant mining industry.

An improved random drift particle swarm optimization-feed forward backpropagation neural network for high-precision state-of-charge estimation of lithium-ion batteries.

A predictive model with high accuracy and stability of the state of charge (SOC) estimation for lithium-ion batteries plays a significant role in electric vehicles. An improved random drift particle swarm optimization-feed forward backpropagation neural network (IRDPSO-FFBPNN) is established in this paper. Basically, a three-layer FFBPNN is established, and its learning process is analyzed in detail. Then, to avoid the particle out-of-control, inducting weight parameter σ to achieve dynamic control weight convergence. What's more, the cross-reorganization of data is proposed to enhance the utilization. Finally, a further performance comparison with other networks is made under different working conditions to prove the effectiveness of the IRDPSO-FFBPNN. The experimental results showed that the maximum SOC error of the IRDPSO-FFBPNN is 0.1021% in 45s, 0.1237% in 116s under BBDST and DST with different temperatures, respectively, which performed better both in terms of time-consumption and accuracy

The Effect of J-Groove on Vortex Suppression and Energy Dissipation in a Draft Tube of Francis Turbine

The vortex rope in the draft tube is considered as the major contributor to pressure pulsation at partial load (PL) conditions, which causes the hydro unit to operate unstably. Based on the prototype Francis turbine HLA551-LJ-43 in the laboratory, J-grooves are designed on its conical section in this paper. We used numerical simulation to study the effect of the J-grooves on vortex suppression and energy dissipation in the draft tube. Four typical operating conditions were chosen to analyze the vortex suppression; the corresponding flow ratios Q* are 100%, 82%, 69%, and 53%, respectively. Entropy production theory is used to calculate the energy losses and assess the effect of the J-groove on energy dissipation under part-load conditions. By comparing entropy production, circumferential and axial velocity components, swirl intensity, pressure pulsation, and vortex distribution in a draft tube with and without J-grooves at different operating conditions, it can be concluded that the entropy production on the wall containing a conical section with J-grooves is obviously smaller than that without J-grooves, the effects of J-grooves on reducing circumferential velocity component Vu, pressure pulsation, and weakening vortex intensity and vortex rope in the conical section are obvious, especially at part load and deep part-load operating conditions. Using J-grooves shows better performance on vortex control and energy dissipation in the draft tube of a Francis turbine at partial load conditions

A Comparative Study of the Properties of Recycled Concrete Prepared with Nano-SiO2 and CO2 Cured Recycled Coarse Aggregates Subjected to Aggressive Ions Environment

This research focused on the modification effects on recycled concrete (RC) prepared with nano-SiO2 and CO2 cured recycled coarse aggregates (RCA) subjected to an aggressive ions environment. For this purpose, RCA was first simply crushed and modified by nano-SiO2 and CO2, respectively, and the compressive strength, ions permeability as well as the macro properties and features of the interface transition zone (ITZ) of RC were investigated after soaking in 3.5% NaCl solution and 5% Na2SO4 solution for 30 days, respectively. The results show that nano-SiO2 modified RC displays higher compressive strength and ions penetration resistance than that treated by carbonation. Besides, we find that ions attack has a significant influence on the microcracks width and micro-hardness of the ITZ between old aggregate and old mortar. The surface topography, elemental distribution and micro-hardness demonstrate that nano-SiO2 curing can significantly decrease the microcracks width as well as Cl− and SO42− penetration in ITZ, thus increasing the micro-hardness, compared with CO2 treatment

An improved weighting coefficient optimization-particle filtering algorithm based on Gaussian degradation model for remaining useful life prediction of lithium-ion batteries.

Establishing a capacity degradation model accurately and predicting the remaining useful life of lithium-ion batteries scientifically are of great significance for ensuring safety and reliability throughout the batteries' whole life cycle. Aiming at the problems of "particle degradation" and "sample poverty" in traditional particle filtering, an improved weighting coefficient optimization - particle filtering algorithm based on a new Gaussian degradation model for the remaining useful life prediction is proposed in this research. The main idea of the algorithm is to weight the selected particles, sort them according to the particle weights, and then select the particles with relatively large weights to estimate the filtering density, thereby improving the filtering accuracy and enhancing the tracking ability. The experimental verification results under the National Aeronautics and Space Administration data show that the improved weighting coefficient optimization - particle filtering algorithm based on the Gaussian degradation model has significantly improved accuracy in predicting the remaining useful life of lithium-ion batteries. The RMSE of the B05 battery can be controlled within 1.40% and 1.17% at the prediction starting point of 40 cycles and 70 cycles respectively, and the RMSE of the B06 battery can be controlled within 2.45% and 1.93% at the prediction starting point of 40 cycles and 70 cycles respectively. It can be seen that the algorithm proposed in this study has strong traceability and convergence ability, which is important for the development of high-reliability battery management systems

A novel 2-RC equivalent model based on the self-discharge effect for accurate state-of-charge estimation of lithium-ion batteries.

Accurate lithium-ion battery state-of-charge estimation and effective equivalent modeling are important for real-time status monitoring and safety control of lithium-ion batteries. To solve the problem of low accuracy of the second-order RC equivalent model, based on a large number of experimental analyses on the ternary lithium-ion battery, the traditional second-order RC equivalent model was improved, and the self-discharge effect was incorporated into the equivalent model establishment. By measuring the change of the open-circuit voltage of the lithium-ion battery within 30 days in the resting state, the identification of the characteristic parameters of the self-discharge circuit is completed. The experimental results show that, compared with the traditional second-order RC equivalent model, the Self-Discharge-2-RC(SD-2-RC) equivalent model can better simulate the working state of the lithium-ion battery. The maximum error between the analog voltage and the real voltage is less than 0.03V, and its accuracy can be up to 99.3% or more. Based on the accurate establishment of the equivalent model, the Adaptive Extended Kalman filter algorithm is used to estimate the SOC. The algorithm has a fast convergence speed and a good tracking effect. The estimation accuracy can reach more than 96%, and the accurate estimation of the state of charge is realized. This research provides a theoretical basis for the establishment of a more accurate lithium-ion battery equivalent circuit mode