Identification Method of Energy Storage Operating Conditions based on Random Forest

BackgroundVariable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories.ResultsSimulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand.ConclusionWe propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analyzing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research.

This chapter illustrates use of a popular supervised machine-learning methods viz., random forest. It provides an application of Boruta algorithm for feature selection and then uses random forest algorithm for predictive modeling of housing data involving 79 explanatory features. The chapter aims to develop a predictive model for the sale price of the house based on appropriate features. Predictive modeling in the presence of a large number of exploratory variables requires the use of methods that support feature selection. The random forest algorithm is a popular machine-learning method that automatically calculates variable importance measure as a by-product and has been successfully used by various researchers. The Boruta package available in R software is used for feature selection. It uses a wrapper algorithm and can work with any classification methodology that yields variable importance measure as an output and by default uses random forest.

Insights on the applications of metal oxide nanosheets in energy storage systems

Organic electrode materials (OEMs) are increasingly replacing conventional inorganic counterparts in metal ion batteries (MIBs) due to their cost-effectiveness and environmental compatibility. Porphyrin-based materials, particularly metalloporphyrins (M-Porph), have garnered significant attention for electrochemical energy storage systems (EESS) owing to their bipolar electrochemical reactivity, making them suitable as both cathodic and anodic materials. However, the correlation between their structure and performance needs further exploration. This computational study examines the redox properties, thermodynamics, and theoretical performance of Zinc(II)-Porphyrin (Zn-Porph) with furan and thiophene substituents. The redox potential (E°) of Zn-Porph changes by 0.17 V with thiophene substituents instead of furan. Thiophene stabilizes the LUMO by 0.105 eV, indicating enhanced electron affinity and faster electron-accepting processes, while the HOMO shows a 0.085 eV stabilization. Thermodynamic calculations reveal that the reduction process intermediates for Zinc(II)-thiophene-Porph are less stable (ΔΔG° = 0.12 eV) than those for furan, suggesting a more accelerated electrochemical process. Additionally, density of states (DOS) analysis of Zn-T18 shows a non-zero DOS at the Fermi energy, indicating available electronic states for occupancy and highlighting its conductive properties. Upon complexation with PF6¯, the Fermi level shifts, reflecting electronic state redistribution and stabilization. The oxidized form, Zn-T16, retains a non-zero DOS at the Fermi energy despite significant Fermi level shifts, ensuring continued electronic conductivity. These findings underscore the robustness and versatility of Zn-porphyrin cathodes in EESS, demonstrating their potential to meet the demands for efficient, cost-effective, and environmentally friendly energy storage solutions.

Electrochemical energy storage (EES) systems demand electrode materials with high power density, energy density, and long cycle life. Metal-organic frameworks (MOFs) are promising electrode materials, while new MOFs with high conductivity, high stability, and abundant redox-reactive sites are demanded to meet the growing needs of EES. Density Functional Theory (DFT) could calculate these properties of MOFs and provide atomic-level insights into the mechanisms, based on which machine learning (ML) can screen MOFs for EES efficiently. In this review, we first review the exploration of mechanisms based on DFT calculations. We focus on the conductivity, stability, and reactivity of MOFs in EES systems. Then, we review the steps to apply ML in screening MOFs. Establishing datasets of MOFs, extracting features from MOF structure, and applying ML in screening MOFs are discussed. Finally, the review proposes the future avenue of DFT and ML to make up the gaps in the knowledge of MOFs.

Electrochemical Energy Storage (EES) systems have various applications ranging from large‐scale generation and transmission‐related systems, to distribution networks, customer/end‐user sites or electrical mobility and transportation. [1] Still higher performance and more environmentally acceptable component materials are continuously required. This can only be overcome by major advances in materials with nanomaterials playing a central role. Lithium‐Ion batteries, by far the most common form of EES system, share three common features: two electrodes and an ionically conducting electrolyte which provides means of transporting ions to and from the component electrodes. The electrodes comprise a current collector and an active material. Improvement in the design of one or more of these features may lead to EES systems with improved performance, delivering higher energy densities and characteristics such as increased capacity, charge/discharge rates and cycle lifetime. Along this line the key‐issue addressed by the present work relates to the assessment of the batteries performances when nanostructured electrode materials are considered for both the anode and the cathode. Grown directly on the current collector (copper for anode and respectively aluminum for cathode) substrate, thin walled carbon nanotubes in the form of very dense vertically aligned (VACNT) carpets are considered as ideal support matrix (current collector) for the insertion active materials due to their efficient electron transport pathways and stable mechanical support. [3] The present work focuses in presenting preliminary 2D analysis based on advanced electron microscopy techniques (TEM, HR‐TEM, STEM) coupled with the spectroscopy ones (EELS and EDX), dedicated to the characterization of both anode and cathode decorated carbon nanotubes based electrodes. The different analyses allowed the validation of the CNTs carpets fabrication, which becomes nanostructured current collector upon their coupling (decoration) with Si nanoparticles for anodes and metallic lithiated nanoparticles (LiMO 2 ) for cathode. The electrochemical experiences performed on the nanostructured system confirmed the devices performance. Regarding the anode fabrication, the synthesis of CNTs directly on the copper substrate is visible from figure 1a) that using a dHF‐CVD technique one can obtain a homogenous and very dense carpet of VACNT having a 60µm length. The HR‐TEM analysis allowed identifying a double walled structure for the nano‐tube with a diameter size of 3‐4nm presenting surface defects which act as anchorage sites for the eletrodeposition of the nanoparticles. Concerning the nano‐tubes decoration with silicon nanoparticles the process has been carried following an innovative approach using ionic liquid with SiCl 4 as precursor for Si. These hybrid nanostructures are expected to enhance the electrode capacity since the VACNT acts as both mechanical support and electrical conductor while silicon nanoparticles as high capacity insertion anode material. Figure 2b) illustrates silicon decorated CNTs. The HR‐TEM analyses on several areas on the current collector allowed evidencing that the deposition is obtained homogenously on all nanotubes with nanoparticles sizes ranging from 5 to 30nm. Regarding the cathode fabrication, similar TEM analyses have been performed to validate the synthesis of CNTs carpets on aluminum foil substrate as well as their subsequent decoration with LiMO 2 nanoparticles.

Ultrahigh-rate and high-density lithium-ion capacitors through hybriding nitrogen-enriched hierarchical porous carbon cathode with prelithiated microcrystalline graphite anode

The random forest(RF) algorithm is a very efficient and excellent ensemble classification algorithm. In this paper, we improve the random forest algorithm and propose an algorithm called ‘Bayesian Weighted Random Forest’(B-RF), focus on the problem that inaccurate decision tree classification caused by the same voting weights in the traditional random forest model. The main idea underlying the proposed model is to replace the supermajority voting of random forests into weighted voting, fully consider the difference of classification ability of each decision tree, using the Bayesian formula to dynamically update the weight value for each tree, so that the strong classifier has higher voting power and effectively improves the overall performance of classification. Through the verification of UCI database, the results show that the classification accuracy of the proposed weighted random forest model is higher. This illustrate the outperformance of the proposed model in comparison with the general random forest algorithm.

This paper presents an algorithm for integrating and managing electrochemical energy storage systems (ESSs) on unit commitment (UC) problem. As some elements required for integrating electrochemical ESS, such as the power converter, have non-linear characteristics, its corresponding linear modeling could be difficult to be developed and included on the UC problem, which could lead to unfeasible solutions or unexpected results. In order to incorporate full models of ESS and its interface with the power system, in this paper an algorithm to incorporate electrochemical ESS management on the UC problem is presented. An insular power system of 10-units is analyzed, and conclusions are duly drawn.

This paper comparatively examines three different electrochemical energy storage systems (ESSs), i.e., a Li-ion battery pack, a supercapacitor pack, and a dual buffer, for a hybrid bus powertrain operated in Gothenburg, Sweden. Existing studies focus on comparing these ESSs, in terms of either general attributes (e.g., energy density and power density) or their implications to the fuel economy of hybrid vehicles with a heuristic/nonoptimal ESS size and power management strategy. This paper adds four original contributions to the related literature. First, the three ESSs are compared in a framework of simultaneous optimal ESS sizing and energy management, where the ESSs can serve the powertrain in the most cost-effective manner. Second, convex optimization is used to implement the framework, which allows the hybrid powertrain designers/integrators to rapidly and optimally perform integrated ESS selection, sizing, and power management. Third, both hybrid electric vehicle (HEV) and plug-in HEV (PHEV) scenarios for the powertrain are considered, in order to systematically examine how different the ESS requirements are for HEV and PHEV applications. Finally, a sensitivity analysis is carried out to evaluate how price variations of the onboard energy carriers affect the results and conclusions.

Developing a deeper understanding of dynamic chemical, electronic, and morphological changes at interfaces is key to solving practical issues in electrochemical energy storage systems (EESSs). To unravel this complexity, an assortment of tools with distinct capabilities and spatiotemporal resolutions have been used to creatively visualize interfacial processes as they occur. This review highlights how electrochemical scanning probe techniques (ESPTs) such as electrochemical atomic force microscopy, scanning electrochemical microscopy, scanning ion conductance microscopy, and scanning electrochemical cell microscopy are uniquely positioned to address these challenges in EESSs. We describe the operating principles of ESPTs, focusing on the inspection of interfacial structure and chemical processes involved in Li-ion batteries and beyond. We discuss current examples, performance limitations, and complementary ESPTs. Finally, we discuss prospects for imaging improvements and deep learning for automation. We foresee that ESPTs will play an enabling role in advancing EESSs as we transition to renewable energies. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 16 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The advancing information technology is playing more and more important role in data mining of relational database.1 The transfer and sharing of databases cause the copyright-related security threats. Database watermarking technology can effectively solve the problem with copyright protection and traceability, which has been attracting researchers' attention. In this paper, we proposed a novel, robust and reversible database watermarking technique, named histogram shifting watermarking based on random forest and genetic algorithm (RF-GAHCSW). It greatly improves the watermark capacity by means of histogram width reduction and eliminates the impact of the prediction error attack. Meanwhile, random forest algorithm is used to select important attributes for watermark embedding, and genetic algorithm is employed to find the optimal secret key for the database grouping and determine the position of watermark embedding to improve the watermark capacity and reduce data distortion. The experimental results show that the robustness of RF-GAHCSW is greatly improved, compared with the original HSW, and the distortion has little effect on the usability of database.

There is growing momentum to develop statistical learning (SL) methods as an alternative to conventional genome-wide association studies (GWAS). Methods such as random forests (RF) and gradient boosting machine (GBM) result in variable importance measures that indicate how well each single-nucleotide polymorphism (SNP) predicts the phenotype. For RF, it has been shown that variable importance measures are systematically affected by minor allele frequency (MAF) and linkage disequilibrium (LD). To establish RF and GBM as viable alternatives for analyzing genome-wide data, it is necessary to address this potential bias and show that SL methods do not significantly under-perform conventional GWAS methods. Both LD and MAF have a significant impact on the variable importance measures commonly used in RF and GBM. Dividing SNPs into overlapping subsets with approximate linkage equilibrium and applying SL methods to each subset successfully reduces the impact of LD. A welcome side effect of this approach is a dramatic reduction in parallel computing time, increasing the feasibility of applying SL methods to large datasets. The created subsets also facilitate a potential correction for the effect of MAF using pseudocovariates. Simulations using simulated SNPs embedded in empirical data-assessing varying effect sizes, minor allele frequencies and LD patterns-suggest that the sensitivity to detect effects is often improved by subsetting and does not significantly under-perform the Armitage trend test, even under ideal conditions for the trend test. Code for the LD subsetting algorithm and pseudocovariate correction is available at http://www.nd.edu/~glubke/code.html.

While Li-ion battery cathode-electrolyte interfaces (CEIs) have been extensively investigated in recent decades, accurately identifying the chemical nature and tracking the dynamics of the CEIs during electrochemical cycling still remain a grand challenge. Here we report our findings in the investigation into the dynamic evolution of the interface between a LiNi0.33Co0.33Mn0.33O2 (LNMC) cathode and an ethylene carbonate/dimethyl carbonate (EC/DMC)-based electrolyte using surface-enhanced Raman spectroscopy (SERS) performed on a model cell under typical battery operating conditions. In particular, the strong SERS activity provided by a monolayer of Au nanocubes deposited on a model LNMC electrode (additive-free) enables quasi-quantitative assessment of the CEI evolution during cycling, proving information vital to revealing the dynamics of the species adsorbed on the LNMC surface as a function of cell potential. Furthermore, our theoretical calculation, which is based on the interaction between a model interface-bound molecule and a model LNMC surface, agrees with our experimental observation. The carefully designed operando SERS platform has demonstrated high sensitivity, good surface specificity, and excellent compatibility with extensive electrochemical measurements; it is also applicable to fundamental studies of dynamic interfaces in other electrochemical energy storage and conversion systems.

Reliable fault detection is essential for ensuring the safe and efficient operation of electrochemical energy storage systems, including lithium-ion batteries and transformer. However, the performance of machine learning-based fault diagnosis models is often degraded in practice due to label noise in training data, caused by sensor inaccuracies, ambiguous fault transitions, and imperfect labeling processes. This paper proposes a lightweight and effective kernel-based data rectification framework to improve the robustness of fault detection under noisy label conditions. The method identifies and discards low-density data points that are statistically more likely to be mislabeled, using kernel density estimation and a tunable data discarding strategy. The approach is computationally efficient, classifier-agnostic, and easily applicable to existing fault diagnosis pipelines. We evaluate the proposed method on two datasets: simulated lithium-ion battery voltage data under various fault scenarios, and transformer winding oscillation wave data under multiple winding fault conditions. The results demonstrate that the rectification framework significantly improves classification accuracy across both Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers. Furthermore, the choice of discarding ratio is shown to be critical, with optimal performance achieved when the ratio is tuned close to the underlying noise level. These results highlight the potential of the proposed method to enhance the reliability of fault diagnosis in electrochemical energy storage systems. Future work will explore adaptive strategies to automatically optimize the rectification strength without requiring prior knowledge of the noise rate, and extend the framework to multi-sensor and multi-modal monitoring scenarios.