Nonlinear Model Inversion-Based Output Tracking Control for Battery Fast Charging

We propose a novel nonlinear control approach for fast charging of lithium-ion batteries, where health- and safety-related variables, or their time derivatives, are expressed in an input-polynomial form. By converting a constrained optimal control problem into an output tracking problem with multiple tracking references, the required control input, i.e., the charging current, is obtained by computing a series of candidate currents associated with different tracking references. Consequently, an optimization-free nonlinear model inversion-based control algorithm is derived for charging the batteries. We demonstrate the efficacy of our method using a spatially discretized high-fidelity pseudo-two-dimensional (P2D) model with thermal dynamics. Conventional methods require computationally demanding optimization to solve the corresponding fast charging problem for such a high-order system, leading to practical difficulties in achieving low-cost implementation. Results from comparative studies show that the proposed controller can achieve performance very close to nonlinear and linearized model predictive control but with much lower computational costs and minimal parameter tuning efforts

Control-Oriented Modeling of All-Solid-State Batteries Using Physics-Based Equivalent Circuits

Considered as one of the ultimate energy storage technologies for electrified transportation, the emerging all-solid-state batteries (ASSBs) have attracted immense attention due to their superior thermal stability, increased power and energy densities, and prolonged cycle life. To achieve the expected high performance, practical applications of ASSBs require accurate and computationally efficient models for the design and implementation of many onboard management algorithms, so that the ASSB safety, health, and cycling performance can be optimized under a wide range of operating conditions. A control-oriented modeling framework is thus established in this work by systematically simplifying a rigorous partial differential equation (PDE) based model of the ASSBs developed from underlying electrochemical principles. Specifically, partial fraction expansion and moment matching are used to obtain ordinary differential equation based reduced-order models (ROMs). By expressing the models in a canonical circuit form, excellent properties for control design such as structural simplicity and full observability are revealed. Compared to the original PDE model, the developed ROMs have demonstrated high fidelity at significantly improved computational efficiency. Extensive comparisons have also been conducted to verify its superiority to the prevailing models due to the consideration of concentration-dependent diffusion and migration. Such ROMs can thus be used for advanced control design in future intelligent management systems of ASSBs

Expression in A. thaliana and cellular localization reveal involvement of BjNRAMP1 in cadmium uptake

Although Brassica juncea has demonstrated potential as a hyperaccumulator crop, it was not entirely clear how cadmium (Cd) accumulates in plants. Here, we found that BjNRAMP1 (Natural Resistance-Associated Macrophage Protein 1) plays a crucial role in the accumulation of Cd and manganese (Mn) through its expression in yeast and Arabidopsis thaliana. The high concentration of Cd exposure could induce the expression of BjNRAMP1. The ectopic expression of BjNRAMP1 in yeast led to higher accumulation of Cd and Mn compared to the vector control. BjNARAMP1 was localized to the plasma membrane and expressed in the vascular system of roots, leaves, and flowers. The overexpression of BjNRAMP1 in A. thaliana resulted in an increased accumulation of Cd in both roots and shoots, which inhibited the normal growth of transgenic lines. Moreover, Mn uptake in roots was activated by the increase in Cd stress. Together, our results indicated that BjNRAMP1 significantly contributes to the uptake of Mn and Cd in B. juncea

A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation

Data assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as additive Gaussian noise, specified by their covariance matrices Q and R, respectively. These error covariances, and specifically their respective amplitudes, determine the weights given to the background (i.e., the model forecasts) and to the observations in the solution of data assimilation algorithms (i.e., the analysis). Consequently, Q and R matrices significantly impact the accuracy of the analysis. This review aims to present and to discuss, with a unified framework, different methods to jointly estimate the Q and R matrices using ensemble-based data assimilation techniques. Most of the methodologies developed to date use the innovations, defined as differences between the observations and the projection of the forecasts onto the observation space. These methodologies are based on two main statistical criteria: (i) the method of moments, in which the theoretical and empirical moments of the innovations are assumed to be equal, and (ii) methods that use the likelihood of the observations, themselves contained in the innovations. The reviewed methods assume that innovations are Gaussian random variables, although extension to other distributions is possible for likelihood-based methods. The methods also show some differences in terms of levels of complexity and applicability to high-dimensional systems. The conclusion of the review discusses the key challenges to further develop estimation methods for Q and R. These challenges include taking into account time-varying error covariances, using limited observational coverage, estimating additional deterministic error terms, or accounting for correlated noise

Estimation of the Hurst Parameter in Spot Volatility

This paper contributes in three stages in a logic of the cognitive process: we firstly propose a new estimation of Hurst exponent by changing frequency method which is purely mathematical. Then we want to check if the new Hurst is efficient, so we prove the advantages of this new Hurst in asymptotic variance in the perspective compared with other two Hurst estimator. However, a purely mathematical game is not enough, a good estimation should be proven by reality, so we apply the new Hurst estimator into truncated and non-truncated spot volatility which fills the gap of previous literatures using 5-min price data (Source: Wind Financial Terminal) of 10 Chinese A-share industry indices from 1 January 2005 until 31 December 2020

Estimation of the Hurst Parameter in Spot Volatility

This paper contributes in three stages in a logic of the cognitive process: we firstly propose a new estimation of Hurst exponent by changing frequency method which is purely mathematical. Then we want to check if the new Hurst is efficient, so we prove the advantages of this new Hurst in asymptotic variance in the perspective compared with other two Hurst estimator. However, a purely mathematical game is not enough, a good estimation should be proven by reality, so we apply the new Hurst estimator into truncated and non-truncated spot volatility which fills the gap of previous literatures using 5-min price data (Source: Wind Financial Terminal) of 10 Chinese A-share industry indices from 1 January 2005 until 31 December 2020

Design and implementation of simulation training software for naval security monitoring system

[Objectives] To improve the security monitoring system operation ability of a crew,[Methods] an approach involving embedding simulation training modules in naval security monitoring systems is adopted so as to design a closed-loop onboard training system. The problems of resource sharing and security isolation are solved by using such key techniques as the interconnection of simulation training modules and actual equipment,cooperative training and training evaluation.[Results] Security monitoring system embedded simulation training is implemented,providing the crew with a real and effective environment for operation and training.[Conclusions] With its effectiveness validated by tests,this software can provide references for developing other onboard training systems

A PDE Model Simplification Framework for All-Solid-State Batteries

All-solid-state batteries (ASSBs) have attracted immense attention due to their superior thermal stability, improved power and energy densities, and prolonged cycle life. Their practical applications require accurate and computationally efficient models for the design and implementation of many onboard management algorithms, so that the safety, health, and cycling performance of ASSBs can be optimized under a wide range of operating conditions. A control-oriented modeling framework is thus established in this work by systematically simplifying a partial differential equation (PDE) based model of the ASSBs developed from underlying electrochemical principles. Compared to the original PDE model, the reduced-order models obtained with the proposed framework demonstrates high fidelity at significantly improved computational efficiency

A Group of Highly Secretory miRNAs Correlates with Lymph Node Metastasis and Poor Prognosis in Oral Squamous Cell Carcinoma

MicroRNAs (miRNAs) in oral squamous cell carcinoma (OSCC)-derived small extracellular vesicles (sEVs) play a pivotal role in modulating intercellular communications between tumor cells and other cells in the microenvironment, thereby influencing tumor progression and the efficacy of therapeutic interventions. However, a comprehensive inventory of these secretory miRNAs in sEVs and their biological and clinical implications remains elusive. This study aims to profile the miRNA content of OSCC cell line sEVs and computationally elucidate their biological and clinical relevance. We conducted miRNA sequencing to compare the miRNA profiles of OSCC cells and their corresponding sEVs. Our motif enrichment analysis identified specific sorting motifs that are implicated in either cellular retention or preferential sEV secretion. Target cell analysis suggested that the sEV miRNAs potentially interact with various immune cell types, including natural killer cells and dendritic cells. Additionally, we explored the clinical relevance of these miRNAs by correlating their expression levels with TNM stages and patient survival outcomes. Intriguingly, our findings revealed that a distinct sEV miRNA signature is associated with lymph node metastasis and poorer survival in patients in TCGA-HNSC dataset. Collectively, this research furthers our understanding of the miRNA sorting mechanisms in OSCC and underscores their clinical implications