Particle Filter Research Articles

MECHANICAL PROPERTIES OF POROUS SILICON CARBIDE CERAMICS: A REVIEW

Porous silicon carbide (SiC)-based ceramics exhibit exceptional structural and functional properties, such as excellent mechanical, chemical, and thermal stability, and controlled electrical resistivity. Owing to their superior properties, porous SiC ceramics are suitable for various industrial applications, including heatable filters, heating elements, thermoelectric energy converters, fusion reactors, thermal insulators, water purifiers, molten metal and hot gas filters, diesel particulate filters, membrane supports, and catalyst supports. A deeper understanding of the mechanical properties of porous SiC ceramics, coupled with the development of new strategies for tuning these properties, will enable the realization of numerous new applications. In this review, important factors known to determine the mechanical strength of porous SiC ceramics, such as microstructures (necking area) and pore characteristics (porosity, pore size), have been analyzed. With increasing porosity and pore size of porous SiC ceramics, the flexural strength tends to decrease. The flexural strength increases with decreasing pore size at a constant porosity, whereas the flexural strength decreases with increasing porosity at a constant pore size. In addition, the flexural strength of porous SiC ceramics is primarily influenced by the developed necking area between SiC grains, which can be obtained through the doping of soluble atoms into the SiC lattice. Based on these critical factors affecting the mechanical properties, a novel strategy for tuning the flexural strength of porous SiC ceramics is proposed.

Hunt-inspired Transformer for visual object tracking

This paper presents a hunt-inspired Transformer for visual object tracking, dubbed as HuntFormer. The HuntFormer focuses on robust target detection and identification, simulating natural hunting processes. Specifically, the HuntFormer comprises two essential module designs including a predictor for detection and a verifier for identification. The predictor emulates the detection stage by designing a motion trajectory guided particle filter, which identifies potential target locations by predicting the motion state within a particle filtering framework. The predictor utilizes spatio-temporal correlation scores between dynamic target templates and the search region to guide the learning process to generate a set of reliable particles. This enables the base tracker to narrow its search range to focus on the target, and swiftly re-detect the target in case of model drift. Once the target is re-detected, the verifier assesses the detection result as a reliable tracked item. The verifier initially maintains a dynamic memory that stores reliable target templates and their corresponding locations in the motion trajectory. It then models the uncertainty of appearance information within this memory probabilistically. The output uncertainty score determines whether the memory gets updated or not. Ultimately, the predictor and the verifier collaborate, ensuring a robust tracking outcome. Extensive evaluations on six challenging benchmark datasets demonstrate HuntFormer’s favorable performance against various state-of-the-art trackers. Notably, in the VOT-LT2022 tracking challenge, the HuntFormer won the third place with an F-score of 0.598, closely competing for the second place with an F-score of 0.600.

Seasonal oxidative dynamics and isotopic fractionation of NOx and SO2 in Ostrava, Czech Republic: Insights from stable isotope analysis

The city of Ostrava, NE Czech Republic, is known for its industrial pollution. It has well-characterized emission sources and stable air movement patterns. These features are conveniently used to generalize on atmospheric NOx and SO2 oxidation processes. In 2021, we conducted a series of sampling campaigns to assess the isotopic fractionation conducive to NO3− and SO42− aerosols in PM10. These sampling campaigns were timed to capture varying atmospheric conditions, including climatic inversion periods, providing also insights into urban emission dynamics over the course of the year. Gaseous NOx and SO2 were collected on passive filters, while their oxidized forms, NO3− and SO42, were captured on PM10 particle filters, enabling us to analyze the transformation dynamics of these pollutants. Isotopic analyses distinguished the sources of NOx emissions—coal combustion (δ15N = −3‰) and vehicular emissions (δ15N = −7 ‰)—and allowed quantifying isotope fractionation during their conversion to NO3− (εNOx-NO3- = 11.5 ± 1.15 ‰). This fractionation, however, was influenced by seasonal variations, and appears to be notably affected by NH4NO3 decomposition during warmer months. The correlation of the NO3− to NOx ratio with PM10 and atmospheric moisture highlighted the interplay between particulate matter and humidity in relevant atmospheric transformations. Similarly, for SO2, primarily emitted from coal combustion (δ34S = −2‰), we identified distinct fractionation patterns during oxidation to SO42− encompassing both kinetic (εSO2-H2O = −1.3 ± 0.5‰) and equilibrium (εSO2- SO42- = 2.24 ± 0.67‰) effects. The SO42−/SO2 ratio was correlated with PM10 but showed no dependence on humidity. Significantly, atmospheric inversion conditions accelerated oxidation, modifying the fractionation patterns for both NOx (εNOx-NO3- = 7‰) and SO2 (εSO2- SO42-= −0.9 ± 0.3‰). Our methodology elucidates pivotal mechanisms in atmospheric pollution transformation, underlined by the tracing of NOx and SO2 to aerosol conversions via δ15N and δ34S isotopic analyses. The isotope fractionation underscores equilibrium processes in oxidation reactions, while the effect of PM10 and humidity reveals the complexity of these atmospheric oxidative transformations. The role of wet deposition in removing SO2 highlights an essential pathway in the atmospheric sulfur cycle.

Implementation of unknown parameter estimation procedure for hybrid and discrete non‐linear systems

AbstractThe application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non‐linear filter problems, when prediction equations are continuous‐time and the update equations are discrete‐time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete‐time non‐linear filter problems, when prediction equations and update equations are discrete‐time. In order to assess the performance of the filters, the authors consider the non‐linear dynamics for a re‐entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re‐entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re‐entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins‐Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter.

Comparison of 2 methods for sterilization of filtering facepiece respirators worn for extended periods during the COVID-19 pandemic: An experimental laboratory study

During the COVID-19 pandemic, there was a shortage of filtering facepiece respirators (FFR), leading to prolonged use and reuse of FFRs. FFRs were collected in 3 hospitals after extended use (up to 15 or 30days). We assessed the physical characteristics and filtration levels of worn FFRs, before sterilization. Respirators that achieved at least 94% filtration of aerosol particles, nasal clip still attached, had no tears, had preserved elastic bands, and had no dirt were randomized to receive or not receive cleaning before being submitted to hydrogen peroxide plasma gas sterilization. A total of 1,055 FFRs were collected. Over 85% of them exhibited secured nose clips, preserved strap elasticity, and no tears. However, more than 78% of samples contained dirt, leaving only 101 (19.6%) eligible to undergo sterilization. After sterilization, none of the FFRs in either group achieved minimum filtration, although 72% without cleaning and 80% with cleaning had filtration between 90.0% and 93.9%. A large proportion of FFRs were ineligible for sterilization due to factors unrelated to health care (eg, dirt from makeup). Prolonged reuse of FFRs significantly reduced aerosol filtration efficiency. Eligible FFRs did not maintain 94% filtration after sterilization with or without cleaning.

Electricity-driven rapid regeneration of ceramic paper-based soot filters with conductive potassium-supported antimony-doped tin oxide catalyst

To address short-term emission of massive particulate matter (PM) from diesel engines, rapid regeneration of catalyzed diesel particulate filters (CDPF) based on soot combustion is a considerable challenge. Herein, we employed an aluminosilicate fiber-weaved ceramic paper (CP) as a filter matrix, on which highly active potassium-supported antimony-doped tin oxides (K/ATO) were coated to fabricate a CDPF. The K/ATO/CP monolith preserves the 3D-network structure of CP while incorporating the mesopores of K/ATO, providing potential for trapping soot particles effectively. Furthermore, electricity pulses are designed to pass through the conductive K/ATO coating on the monolith for CDPF regeneration, achieving nearly complete soot conversion within less than half a minute using 1400 J of energy input. Both the reaction rate and the energy efficiency surpass previously reported those of other methods for soot combustion. Mechanism studies revealed that the electricity pulse can stimulate the release of tremendous lattice oxygen from the catalyst and in turn trigger rapid soot combustion, and boost surface sulfate decomposition to alleviate SO2 poisoning. This work presents an electrification strategy to realize rapid regeneration of CDPF for effective PM elimination utilizing onboard electricity system for hybrid powers.

Controlled human exposures to diesel exhaust or particle-depleted diesel exhaust with allergen modulates transcriptomic responses in the lung

The evidence associating traffic-related air pollution (TRAP) with allergic asthma is growing, but the underlying mechanisms for this association remain unclear. The airway epithelium is the primary tissue exposed to TRAP, hence understanding its interactions with TRAP and allergen is important. Diesel exhaust (DE), a paradigm of TRAP, consists of particulate matter (PM) and gases. Modern diesel engines often have catalytic diesel particulate filters to reduce PM output, but these may increase gaseous concentrations, and their benefits on human health cannot be assumed. We conducted a randomized, double-blinded, crossover study using our unique in vivo human exposure system to investigate the effects of DE and allergen co-exposure, with or without particle depletion as a proxy for catalytic diesel particulate filters, on the airway epithelial transcriptome. Participants were exposed for 2 h before an allergen inhalation challenge, with each receiving filtered air and saline (FA-S), filtered air and allergen (FA-A), DE and allergen (DE-A), or particle-depleted DE and allergen (PDDE-A), over four different occasions, each separated by a 4-week washout period. Endobronchial brushings were collected 48 h after each exposure, and total RNA was sequenced. Differentially expressed genes (DEGs) were identified using DESeq2, followed by GO enrichment and pathway analysis. FA-A, DE-A, and PDDE-A exposures significantly modulated genes relative to FA-S, with 462 unique DEGs identified. FA-A uniquely modulated the highest number (↑178, ↓155), followed by DE-A (↑44, ↓23), and then PDDE-A exposure (↑15, ↓2); 6 DEGs (↑4, ↓2) were modulated by all three conditions. Exposure to PDDE-A resulted in modulation of 285 DEGs compared to DE-A exposure, further revealing 26 biological process GO terms, including “cellular response to chemokine” and “inflammatory response”. The transcriptional epithelial response to diesel exhaust and allergen co-exposure is enriched in inflammatory mediators, the pattern of which is altered upon particle depletion.

IMUPF-BIN: A new autonomous search method for radioactive sources

To solve the problem of mobile robots autonomously searching for unknown radioactive sources, this paper proposes an automatic source search strategy, abbreviated as IMUPF-BIN: the location of the radioactive source is estimated by improved unscented particle filtering (IMUPF) algorithm, and the search path is predicted and planned by bioinspired neural network (BIN) model. First, the search environment is expressed as an occupied grid map, and the BIN model is combined with the grid occupied map to obtain the environment information in the search process. Then, the unscented particle filter (UPF) which is used for source parameter estimation is improved by introducing the idea of linear optimization to improve the efficiency of particle usage and the diversity of particles. Further, the BIN model is introduced to reduce the influence of obstacles on the movement of the robot, thereby improving the search accuracy and efficiency of radioactive source. After the robot detects the radiation count value, the parameter information of the source is estimated through the IMUPF algorithm. By using the activity value of the BIN model and distributed model predictive control (DMPC) to plan the path, while continuously iterating the search process, the location of the radioactive source is finally determined. The simulation results show that in without obstacles environment, the localization accuracy of the IMUPF algorithm is improved by 85.6% and 52.8% compared to the standard particle filtering (PF) and UPF algorithm respectively, and the search time of the IMUPF-BIN algorithm is reduced by 75.9% compared to the gradient search (GS) algorithm. In obstacles environment, the localization accuracy of the IMUPF algorithm is improved by 80.8% and 35.9% compared to the PF and UPF algorithms respectively. In addition, the GS algorithm fails to search for the radioactive source due to the shield of obstacles, while the IMUPF-BIN algorithm can successfully search for radioactive source in a shorter search time.

Adjoint Synthesis for Trans‐Oceanic Tsunami Waveforms and Simultaneous Inversion of Fault Geometry and Slip Distribution

AbstractTsunamis propagate over long distances and can cause widespread damage even after crossing ocean basins. Prediction of tsunamis in distant areas based on observations near their sources is critical to mitigating damage. In recent years, the accuracy of numerical models of trans‐oceanic tsunami propagation has improved significantly due to the incorporation of effects such as the solid earth response to tsunami loading and wave dispersion. However, these models are computationally expensive and have not been fully utilized for real‐time prediction. Here, we derive the adjoint operator for the linear set of equations describing deep‐ocean tsunami propagation and show how a pre‐computed database of adjoint states can achieve rapid synthesis of tsunami waveforms at target sites from nonpoint arbitrary tsunami sources. The adjoint synthesis method allows for an exhaustive parameter search for tsunami source estimation. A method for simultaneous inversion of fault geometry and slip distribution using adjoint synthesis with Sequential Monte Carlo method was proposed and applied to the 2012 Haida Gwaii earthquake tsunami. The influence of model accuracy and the amount of observed data on the estimation of tsunami sources and waveforms was examined. It was found that with a highly accurate propagation model, using only a limited amount of observed data produced source and waveform estimates very similar to the final models obtained with much larger data sets. The final inferred fault model involved megathrust slip distributed between the Haida Gwaii trench and the Queen Charlotte fault. The proposed method can also quantify the uncertainty of the waveform forecasts.

A hybrid residual correction method with auxiliary particle filter and conditional kernel density estimation for remaining useful life prediction

Predicting the remaining useful life (RUL) is crucial for prognosis and health management and provides an essential foundation for maintenance decisions. This study constructed a hybrid RUL prediction method based on residual correction to enhance prediction accuracy, minimize uncertainty, and mitigate errors caused by local fluctuations in the degeneration state within complex environments. This approach combined an auxiliary particle filter (APF), complementary ensemble empirical mode decomposition (CEEMD), and conditional kernel density estimation (CKDE). First, a Wiener degradation model was established, and the APF was used to acquire the initial state estimate of the equipment and residual difference between actual observations. Second, CEEMD was used to reconstruct the residual sequence and reduce interference. Subsequently, the nonparametric CKDE predicted the residual to correct the filter estimation. The corrected degeneration state value elucidated the local details of the system degradation process, and the point prediction of the RUL at the current time was obtained. The probability density distribution of the remaining life was estimated by substituting the modified degeneration state values into the kernel density estimation (KDE) model. Finally, the method was tested using an aircraft engine dataset. The results demonstrated the efficacy of the proposed method and its superiority over alternative approaches.

Emission Factors of Tyre Wear Particles Emitted by Light Road Vehicles in Real Driving Conditions: A New Challenge for Clean Road Transport to Improve Urban Air Quality

Non-exhaust road transport emissions in cities contribute to poor air quality and have an impact on human health. This paper presents a new study of particles emitted by tyre wear in real driving conditions and gives their emission factors. The most frequently emitted particles were collected in urban, suburban and road areas. They were identified and analysed physically and chemically. Their level of toxicity is well known. An overall analysis of the measured pollutants was carried out to assess their emission factors in real driving situations. The highest emitting pollutants, considered separately, were found to have high emission factors. The values obtained exceed the Euro standard for vehicles but are below those of vehicles not equipped with particle filters. Significant test analysis confirmed that the inertia of chemical pollutants is homogeneous. Emission factors have also been provided for PM10 and PM2.5. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship for particles from tyre wear.

Evaluation of the effect of remanufacturing diesel particulate filters to minimize environmental impacts

Exhaust gas purification is required for the operation of heavy machinery, e.g., construction machinery which mainly uses diesel engines. Precious metals such as the platinum group are used in catalysts for this purpose, which heavily impacts the environment. In this study, the authors evaluated the potential of remanufacturing diesel particulate filters (DPF) to reduce these impacts. Climate change indicators, i.e., global warming potential (GWP), and resource consumption were evaluated.As a result, the environmental impacts of new product manufacturing, particularly resource production and the manufacturing process, were quantitatively estimated to be significant, while the environmental impacts of the remanufacturing process, product delivery, and disposal of the used products were significantly lower. In addition, 47% of the GWP and 50% of the resource consumption were reduced using remanufactured diesel particulate filters compared with using only new diesel particulate filters.

Distributed multi‐station target tracking based on unscented particle filter and Dempster‐Shafer theory

Distributed multi‐station target tracking based on unscented particle filter and Dempster‐Shafer theory

Online state and unknown inputs estimation for nonlinear systems with particle filter based recursive expectation‐maximization algorithm

AbstractThe article presents an innovative approach to simultaneously estimate states and unknown inputs (UIs) in nonlinear systems using a particle filter (PF) based recursive expectation‐maximization (EM) algorithm. This method is distinct from traditional iterative EM algorithms. During the E‐step, it calculates the Q‐function recursively within the maximum likelihood framework, while the PF estimates the system states. The M‐step involves local maximization of the recursive Q‐function to online estimate the UIs. The effectiveness of the PF‐based recursive EM algorithm is demonstrated with a numerical example, and comparisons with the augmented state PF are made to highlight its advantages. Finally, the proposed algorithm is implemented in a real application for the estimation of the continuous fermentation process.

Incremental analysis of load handling device deflection considering lubrication degradation for predictive maintenance

In the Industry 4.0 era, unmanned automated factories heavily rely on load handling devices (LHDs), which are indispensable but susceptible to failures. Deflection is a crucial health indicator, with excessive deflection causing issues such as tilting and reduced positioning accuracy. This underscores the necessity for reliable deflection prediction methods in LHDs, a currently under-researched area, essential for informing predictive maintenance strategies. This study introduces an incremental analysis method that addresses the impact of internal cumulative wear and lubrication degradation on deflection increase. It involves iteratively updating a multiparametric model to represent the cumulative wear process and utilizes an empirical model based on the Stribeck curve to describe lubrication degradation. To enhance accuracy, particle filtering (PF) is employed to revise friction coefficients derived from the empirical model. Experimental data from an LHD operating in a load-carrying mission validates the accurate and robust nature of this method, particularly when compared with non-friction coefficient revising counterparts. As a typical application case study for preventive maintenance, this method is utilized to simulate various scenarios of lubrication replenishment and quantify the effect of timing on LHD lifespan enhancement through lubricant replenishment. The results facilitate the optimization of maintenance schedules and offer a new approach for updating lubrication-related parameters in digital twin models of equipment-level products, in line with preventative maintenance strategies.

An information field theory approach to Bayesian state and parameter estimation in dynamical systems

Dynamical system state estimation and parameter calibration problems are ubiquitous across science and engineering. Bayesian approaches to the problem are the gold standard as they allow for the quantification of uncertainties and enable the seamless fusion of different experimental modalities. When the dynamics are discrete and stochastic, one may employ powerful techniques such as Kalman, particle, or variational filters. Practitioners commonly apply these methods to continuous-time, deterministic dynamical systems after discretizing the dynamics and introducing fictitious transition probabilities. However, approaches based on time-discretization suffer from the curse of dimensionality since the number of random variables grows linearly with the number of time-steps. Furthermore, the introduction of fictitious transition probabilities is an unsatisfactory solution because it increases the number of model parameters and may lead to inference bias. To address these drawbacks, the objective of this paper is to develop a scalable Bayesian approach to state and parameter estimation suitable for continuous-time, deterministic dynamical systems. Our methodology builds upon information field theory. Specifically, we parameterize the dynamical system state path using a linear basis. Then, we construct a physics-informed prior probability measure for the coefficients of the linear basis such that functions that satisfy the physics are more likely. This prior allows us to quantify model form errors. We connect the system's response to observations through a probabilistic model of the measurement process. The joint posterior over the system responses and all parameters is given by Bayes' rule. To approximate the intractable posterior, we develop a stochastic variational inference algorithm. In summary, the developed methodology offers a powerful framework for Bayesian estimation in dynamical systems.

Study on the active regeneration characteristics of DPF in diesel-methanol dual-fuel engine under different exhaust oxygen concentrations

This study proposes a method to control exhaust oxygen (O2) concentration by adjusting the intake throttle valve, achieving intake throttling, and reducing intake airflow. The impact of exhaust O2 concentration at the inlet of the diesel oxidation catalyst (DOC) the diesel particulate filter (DPF) performance during active regeneration in diesel engines is investigated. Experiments are conducted under methanol substitution rates (MSR) of 0% and 20% to investigate how different exhaust O2 concentrations affect the performance of the DOC and DPF during active regeneration. A comparative analysis of fuel consumption and cumulative CO2 emissions during DPF active regeneration under different exhaust O2 concentrations is performed. Research results indicate that decreasing exhaust O2 concentration reduces exhaust flow, raising DOC inlet temperature. This enhancement improves catalytic conversion efficiency, accelerating the rise in DPF inlet temperature. The average conversion efficiency of DOC to unburned methanol exceeds 98.07%. After initiating DPF active regeneration, O2 concentration sharply drops, leading to elevated CO2 emissions. As soot combustion advances, the required O2 diminishes, resulting in a gradual rise and eventual stabilization of O2 emissions. Economically and environmentally, irrespective of MSR (20% or 0%), there exists an optimal exhaust O2 concentration that minimizes effective regeneration time during DPF active regeneration, achieving minimal fuel consumption and the lowest CO2 mass emissions. This study offers theoretical support for optimizing energy-saving and emission-reduction processes during DPF active regeneration.

Study on the Evolution of Physicochemical Properties of Carbon Black at Different Regeneration Stages of Diesel Particulate Filters Regenerated by Non-Thermal Plasma

As a new type of aftertreatment technology, non-thermal plasma (NTP) can effectively decompose the particulate matter (PM) deposited in diesel particulate filters (DPFs). In this paper, a regeneration test of a DPF loaded with carbon black was carried out using an NTP injection system, and the changes of oxidative activity, elemental content, and occurrence state, microstructure and graphitization degree of carbon black were analyzed to reveal the evolution of the physicochemical properties of carbon black at different regeneration stages of the DPF regenerated by NTP. As the regeneration stage of the DPF advanced, Ti, Tmax, and Te of the carbon black at the bottom of the DPF decreased, which were higher than those at the regeneration interface. After the NTP reaction, the proportion of C element decreased to less than 80%, while the proportion of O element increased to more than 20%; C-O was converted to C=O and the relative content of C=O increased. The average microcrystalline length and average spacing decreased, while the average microcrystalline curvature increased. The ID1/IG (relative peak intensities) of carbon black samples decreased from 3.31 to 3.10, and the R3 (relative peak intensities, R3 = ID3/(IG + ID2 + ID3)) increased from 0.41 to 0.58. The content of carbon clusters had a great influence on the disorder of the microcrystalline structure, so the graphitization degree of carbon black decreased and the oxidation activity increased.

Hybrid structured silk-rPET nanotechnical cloth for advanced air purification

AbstractAir pollution has become a significant global issue due to its detrimental environmental and human health effects. In this study, a novel approach was taken to address these challenges by developing a recycled polyethylene terephthalate (rPET) nano-coated silk technical cloth embedded with green-synthesized silver nanoparticles (AgNPs) using a solution electrospinning technique. The filtration performance of the developed material was assessed through particle filtration efficiency (PFE) tests, while differential pressure (DP) tests were conducted to evaluate pressure drop. SEM, FTIR, tensile, antibacterial, radiative heat barrier performance, and moisture management properties of the developed samples were also performed. Maximum 96.58% of filtration performance was observed with corresponding low differential pressures of 29.1 Pa/cm2; maximum tensile force and elongation% were 157.47 N and 15.32%, respectively of the developed samples. FTIR analysis confirmed the presence of silk, rPET, sodium alginate, and AgNPs in the developed sample. Antibacterial assays demonstrated inhibition against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Moisture management property revealed water penetration resistance and radiative heat barrier testing showed good barrier performance. These results make the promising potential of the developed material as an advanced air filter. Graphical Abstract

Towards robust state estimation for LFP batteries: Model-in-the-loop analysis with hysteresis modelling and perspectives for other chemistries

The accurate estimation of a battery’s state of charge (SOC) is critical in battery management systems for various applications. Lithium Iron Phosphate (LFP) batteries, preferred for their long cycle life, cost efficiency, and enhanced safety, have emerged as favourable choices for stationary storage. Yet, they still face challenges in precise SOC estimation due to the flatness and hysteresis of their open circuit voltage. Addressing this, our study integrates a hysteresis model into a third-order battery model for BMS controlling a stationary storage system in frequency containment reserve (FCR) application. We analysed three advanced SOC estimation techniques — extended Kalman filter (EKF), dual unscented Kalman filter (DUKF), and particle filter (PF) — with the hysteresis model using a model-in-the-loop (MiL) toolchain. Performance testing under a 48-hour FCR load profile showed EKF with a 4% error, DUKF achieving the best result with a 1.1% error, and PF’s performance varying between 2.9% and 4% depending on particle count. Robustness tests against initialization and current sensor errors under an 8hr profile revealed DUKF maintained a 2% error boundary irrespective of the error introduced, highlighting the hysteresis model’s effectiveness. Broadening the scope, the study also explores extending the method to other lithium-ion chemistries.