An Integrated Flow–Electric–Thermal Model for a Cylindrical Li-Ion Battery Module with a Direct Liquid Cooling Strategy

An integrated model is constructed for a Li-ion battery module composed of cylindrical cells by coupling individual first-order equivalent circuit models (ECMs) with a 3D heat transfer model, also considering the fluid flow dynamics of the applied cooling liquid, and bench-marked against experimental data. This model simulates a representative unit of the battery module with direct liquid cooling in a parallel configuration. Instead of assigning specific values to the featured parameters involved in the ECMs, they are here defined as 4D arrays. This makes it possible to simultaneously consider the effect of the state of charge, current rate, and temperature on the battery dynamics, making the model more adaptive, versatile, and connectable to the battery cell electrochemistry. According to the simulation results, the model employing state-dependent battery properties fits better with the experimental cooling results. Additionally, the temperature uniformity of the module with a parallel cooling configuration is improved compared to a serial configuration. However, the increase of the absolute core temperature cannot be directly controlled by the surface cooling due to the slow heat transport rate across the battery material. The simulations also provide directions for the modification of module design, to the potential benefit of battery pack developers

Implementing intermittent current interruption into Li-ion cell modelling for improved battery diagnostics

A novel electroanalytical method, the intermittent current interruption (ICI) technique, has recently been promoted as a versatile tool for battery analysis and diagnostics. The technique enables frequent and continuous measurement of battery resistance, which then undergoes statistical analysis. Here, this method is implemented for commercial Li-ion cylindrical cells, and combined with a physics-based finite element model (FEM) of the battery to better interpret the measured resistances. Ageing phenomena such as solid electrolyte interphase (SEI) formation and metallic Li plating on the surface of the negative graphite particles are considered in the model. After validation, a long-term cycling simulation is conducted to mimic the ageing scenario of commercial cylindrical 21700 cells. The large number of internal resistance measurements obtained are subsequently visualized by creating a ‘resistance map’ as a function of both capacity and cycle numbers, providing a straight-forward image of their continuous evolution. By correlating the observed ageing scenarios with specific physical processes, the origins of ageing are investigated. The result shows that a decrease of the electrolyte volume fraction contributes significantly to the increase of internal resistance and affect the electrolyte diffusivity properties. Additionally, effects of porosity and particle radius of the different electrodes are investigated, providing valuable suggestions for battery design

On finite-time anti-saturated proximity control with a tumbling non-cooperative space target

For the challenging problem that a spacecraft approaching a tumbling target with non-cooperative maneuver, an anti-saturated proximity control method is proposed in this paper. First, a brand-new appointed-time convergent performance function is developed via exploring Bezier curve to quantitatively characterize the transient and steady-state behaviors of the pose tracking error system. The major advantage of the proposed function is that the actuator saturation phenomenon at the beginning can be effectively reduced. Then, an anti-saturated pose tracking controller is devised along with an adaptive saturation compensator. Wherein, the finite-time stability of both the pose and its velocity error signals are guaranteed simultaneously in the presence of actuator saturation. Finally, two groups of illustrative examples are organized and verify that the close-range proximity is effectively realized even with unknown target maneuver

Nonlinear Covariance Analysis-Based Robust Rendezvous Trajectory Design by Improved Differential Evolution Method

This paper presents a robust trajectory design method for approaching and rendezvous with a space target considering multi-source uncertainties. A nonlinear covariance analysis method based on the state transition tensor is presented to formulate the propagation of uncertainties including environment parameter uncertainty, actuator error, sensor noise, navigation error and initial state dispersion of the closed-loop GN&C system. Then, the robust trajectory design problem is defined based on the quantified effect of the uncertainties, and an improved self-adaptive differential evolution algorithm is presented to solve the robust trajectory design problem with uncertainties. Finally, four groups of numerical simulations are carried out to show that the designed robust trajectories can satisfy the final state dispersion constraint under multi-source uncertainties

Nonlinear Covariance Analysis-Based Robust Rendezvous Trajectory Design by Improved Differential Evolution Method

This paper presents a robust trajectory design method for approaching and rendezvous with a space target considering multi-source uncertainties. A nonlinear covariance analysis method based on the state transition tensor is presented to formulate the propagation of uncertainties including environment parameter uncertainty, actuator error, sensor noise, navigation error and initial state dispersion of the closed-loop GN&C system. Then, the robust trajectory design problem is defined based on the quantified effect of the uncertainties, and an improved self-adaptive differential evolution algorithm is presented to solve the robust trajectory design problem with uncertainties. Finally, four groups of numerical simulations are carried out to show that the designed robust trajectories can satisfy the final state dispersion constraint under multi-source uncertainties

An unbiased method of measuring the ratio of two data sets

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio $R$ between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, the decay rate of gravitational potential and $E_G$ to test gravity. However, simply taking the ratio of the two data sets is biased, since it renders (even statistical) errors in the denominator into systematic errors in $R$. Furthermore, it is not optimal in minimizing statistical errors of $R$. Based on the Bayesian analysis and the usual assumption of Gaussian error in the data, we derive an analytical expression of the posterior PDF $P(R)$. This result enables fast and unbiased $R$ measurement, with minimal statistical errors. Furthermore, it relies on no underlying model other than the proportionality relation between the two data sets. Even more generally, it applies to the cases where the proportionality relation holds for the underlying physics/statistics instead of the two data sets directly. It also applies to the case of multiple ratios ($R\rightarrow {\bf R}=(R_1,R_2,\cdots)$). We take the lensing ratio as an example to demonstrate our method. We take lenses as DESI imaging survey galaxies, and sources as DECaLS cosmic shear and \emph{Planck} CMB lensing. We restrict the analysis to the ratio between CMB lensing and cosmic shear. The resulting $P(R$), for multiple lens-shear pairs, are all nearly Gaussian. The S/N of measured $R$ ranges from $5.3$ to $8.4$. We perform several tests to verify the robustness of the above result

An unbiased method of measuring the ratio of two data sets

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio $R$ between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, the decay rate of gravitational potential and $E_G$ to test gravity. However, simply taking the ratio of the two data sets is biased, since it renders (even statistical) errors in the denominator into systematic errors in $R$. Furthermore, it is not optimal in minimizing statistical errors of $R$. Based on the Bayesian analysis and the usual assumption of Gaussian error in the data, we derive an analytical expression of the posterior PDF $P(R)$. This result enables fast and unbiased $R$ measurement, with minimal statistical errors. Furthermore, it relies on no underlying model other than the proportionality relation between the two data sets. Even more generally, it applies to the cases where the proportionality relation holds for the underlying physics/statistics instead of the two data sets directly. It also applies to the case of multiple ratios ($R\rightarrow {\bf R}=(R_1,R_2,\cdots)$). We take the lensing ratio as an example to demonstrate our method. We take lenses as DESI imaging survey galaxies, and sources as DECaLS cosmic shear and \emph{Planck} CMB lensing. We restrict the analysis to the ratio between CMB lensing and cosmic shear. The resulting $P(R$), for multiple lens-shear pairs, are all nearly Gaussian. The S/N of measured $R$ ranges from $5.3$ to $8.4$. We perform several tests to verify the robustness of the above result

Synthesis of a Low-Cost Thiophene-Indoloquinoxaline Polymer Donor and Its Application to Polymer Solar Cells

A simple wide-bandgap conjugated polymer based on indoloquinoxaline unit (PIQ) has been newly designed and synthesized via cheap and commercially available starting materials. The basic physicochemical properties of the PIQ have been investigated. PIQ possesses a broad and strong absorption band in the wavelength range of 400~660 nm with a bandgap of 1.80 eV and lower-lying highest occupied molecular orbital energy level of −5.58 eV. Polymer solar cells based on PIQ and popular acceptor Y6 blend display a preliminarily optimized power conversion efficiency of 6.4%. The results demonstrate indoloquinoxaline is a promising building unit for designing polymer donor materials for polymer solar cells

Understanding the Teaching Styles by an Attention based Multi-task Cross-media Dimensional Modeling

Teaching style plays an influential role in helping students to achieve academic success. In this paper, we explore a new problem of effectively understanding teachers' teaching styles. Specifically, we study 1) how to quantitatively characterize various teachers' teaching styles for various teachers and 2) how to model the subtle relationship between cross-media teaching related data (speech, facial expressions and body motions, content et al.) and teaching styles. Using the adjectives selected from more than 10,000 feedback questionnaires provided by an educational enterprise, a novel concept called Teaching Style Semantic Space (TSSS) is developed based on the pleasure-arousal dimensional theory to describe teaching styles quantitatively and comprehensively. Then a multi-task deep learning based model, Attention-based Multi-path Multi-task Deep Neural Network (AMMDNN), is proposed to accurately and robustly capture the internal correlations between cross-media features and TSSS. Based on the benchmark dataset, we further develop a comprehensive data set including 4,541 full-annotated cross-modality teaching classes. Our experimental results demonstrate that the proposed AMMDNN outperforms (+0.0842 in terms of the concordance correlation coefficient (CCC) on average) baseline methods. To further demonstrate the advantages of the proposed TSSS and our model, several interesting case studies are carried out, such as teaching styles comparison among different teachers and courses, and leveraging the proposed method for teaching quality analysis.Comment: ACM Muitimedia 201