Sudden Change Of State Research Articles

State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm

The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRLS) algorithm and the strong tracking H-infinity filtering (STF-HIF) algorithm. To address the issue of fixed forgetting factors in recursive least squares (RLS) that struggle to maintain both fast convergence and stability in battery parameter identification, we introduce dynamic forgetting factors. This approach adjusts the forgetting factor based on the residuals between the model’s estimated and actual values. To improve the H-infinity filtering (HIF) algorithm’s poor performance in tracking sudden state changes, we propose a combined STF-HIF algorithm, integrating HIF with strong tracking filtering (STF). Simulation experiments indicate that, compared to the HIF algorithm, the STF-HIF algorithm achieves a maximum absolute SOC estimation error (MaxAE) of 0.69%, 0.72%, and 1.22%, with mean absolute errors (MAE) of 0.27%, 0.25%, and 0.38%, and root mean square errors (RMSE) of 0.33%, 0.30%, and 0.46% under dynamic stress testing (DST), federal urban driving schedules (FUDS), and Beijing dynamic stress testing (BJDST) conditions, respectively.

Multi-innovation and strong tracking based H[formula omitted] filter for state of charge estimation of lithium-ion batteries

Accurate state of charge (SOC) is a crucial indicator of the battery management system, which ensures the safety performance of lithium-ion batteries (LiBs). However, traditional SOC estimation methods such as extended Kalman filter and H∞ filter (HIF) do not perform satisfactorily when dealing with sudden state changes, which pose a safety hazard to the normal operation of LiBs. This paper proposes two effective strategies to endow the HIF with the strong tracking capability to react to state mutations. Initially, the single innovation in the HIF is extended to the multi-innovation so that the estimation accuracy is ameliorated by incorporating the historical data. Additionally, a suboptimal fading factor is introduced into the HIF to adjust the gain matrix online by forcing the innovation sequence to keep orthogonal. The fading factor fully utilizes the rich information in the innovation sequence, enabling the filter to track changing states well. Finally, the discharge experiments and dynamic stress tests are conducted to verify that the proposed strategy can ameliorate the precision and robustness without bringing extra computational burden.

Impact of the non-Gaussian measurement noise on the performance of state-of-the-art state estimators for distribution systems

This paper aims to investigate the impact of non-Gaussian measurement noise on state estimation (SE) results in distribution systems. To this end, the measurement noise is assumed to be distributed according to Gaussian or one of the following non-Gaussian probability distribution functions: Uniform, Laplace, Weibull and Gaussian mixture of two Gaussian components. The influence is investigated on three different state-of-the-art SE methods: weighted least squares (WLS) based static SE method, and two Kalman filter based forecasting-aided SE methods, namely extended Kalman filter (EKF) and unscented Kalman filter (UKF). Analyses are conducted on modified IEEE 37-bus system under different operating conditions, including quasi-steady state, sudden state changes and bad data. Performance of the methods in the presence of non-Gaussian measurement noise is compared against their performance when measurement noise is Gaussian distributed. The main conclusions were drawn, summarizing the impacts non-Gaussian measurement noise has on SE and proposing the solutions for overcoming some of the negative impacts.

Sensorless control of a PMSM based on an RBF neural network-optimized ADRC and SGHCKF-STF algorithm

For the problem of the rotor position estimation and control accuracy of permanent magnet synchronous motor (PMSM), this paper proposes a PMSM sensorless based on radial basis function (RBF) neural network optimized Automatic disturbance rejection control (RBF-ADRC) and strong tracking filter (STF) improved square root generalized fifth-order cubature Kalman filter (SGHCKF-STF). The Automatic disturbance rejection control (ADRC) has strong robustness, but there are many parameters and difficult to adjust. Now we use RBF neural network to adjust the parameters in ADRC online so as to improve the robustness and anti-disturbance ability. In order to improve the estimation accuracy of rotor position and speed, the orthogonal triangle (QR) decomposition and STF are introduced on the basis of the generalized fifth-order cubature Kalman filter (GHCKF) to design the SGHCKF-STF algorithm that not only ensure the non-positive nature of the covariance matrix but also improve the ability to cope with sudden changes in state during the filtering process. Experimental results show that the combination of RBF-ADRC and SGHCKF-STF improve the sensorless control effect of the PMSM to some extent.

Study on the Multi-Point Boundary Value Problem for Second-Order Nonlinear Impulsive Integro-Differential Equation

In the field of biological control, there are a large number of systems that gradually evolve over a certain period of time. However, due to some natural or human intervention behavior, the system state will be subjected to some relatively short time interference, so that the system state changes in an instant. This sudden change of state makes the system not simply described by continuous or discrete dynamical systems, but by means of impulse dynamical systems. In this paper, the multi-point boundary value problem of a class of second-order nonlinear impulsive integral differential equations is studied on the basis of impulsive differential equation theory. The main results of this kind of equation are obtained by using the fixed point theorem of strict set contraction operator. Under certain assumptions, the existence of the solution of the equation is proved by constructing the operator on the special cone. Finally, combined with the practical application, the theory was applied to the stability prediction of biological ecosystem, and the correctness of the conclusion was verified.

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

Implementing the concept of a “smart furnace transformer” should stipulate its information support throughout its life cycle. This requires improving techniques for estimating the transformer’s health and forecasting its remaining useful life (RUL). A brief review of the problem being solved has shown that the known RUL estimation techniques include processing the results of measuring the facility state parameters using various mathematical methods. Data processing techniques (deep learning, SOLA, etc.) are used, but there is no information on their application in online monitoring systems. Herewith, fast (shock) changes in the resource caused by the failures and subsequent recoveries of the facility’s health have not been considered. This reduces the RUL forecasting accuracy for the repairable equipment, including transformers. It is especially relevant to consider the impact of sudden state changes when it comes to furnace transformers due to a cumulative wear effect determined by their frequent connections to the grid (up to 100 times a day). The proposed approach is based on calculating the RUL by analytical dependencies, considering the failures and recoveries of the facility state. For the first time, an engineering RUL forecasting technique has been developed, based on the online diagnostic monitoring data results provided in the form of time series. The equipment’s relative failure tolerance index, calculated with analytical dependencies, has first been used in RUL forecasting. As a generalized indicator, a relative failure tolerance index considering the facility’s state change dynamics has been proposed. The application of the RUL forecasting technique based on the results of dissolved gas analysis of a ladle furnace unit’s transformer is demonstrated. The changes in the transformer state during the operation period from 2014 to 2022 have been studied. The RUL was calculated in the intensive aging interval; the winding dismantling results were demonstrated, which confirmed developing destructive processes in the insulation. The key practical result of the study is reducing accidents and increasing the service life of the arc and ladle furnace transformers. The techno-economic effect aims to ensure process continuity and increase the metallurgical enterprise’s output (we cannot quantify this effect since it depends on the performance of a particular enterprise). It is recommended to use the technique to forecast the RUL of repairable facilities equipped with online monitoring systems.

Robust forecasting-aided state estimation of power system based on extended Kalman filter with adaptive kernel risk-sensitive loss

State estimation (SE) plays a pivotal role in the development of modern power system. Accurate forecasting-aided state estimation (FASE) can track the sudden changes of power system state and maintain the safe operation of modern power system. However, the performance of existing FASE methods is affected by anomalies in real power system, such as sudden state changes and bad data. To address this problem, a robust algorithm based on extended Kalman filter (EKF) with the kernel risk-sensitive loss (KRSL) (called KRSL-EKF) is proposed for FASE. The KRSL-EKF, taking KRSL as the cost function of the original EKF algorithm, can overcome the limitations of the EKF to perform higher estimation accuracy under non-Gaussian noise cases. In addition, an adaptive method is further introduced into the proposed KRSL-EKF algorithm to adjust the covariance matrices of process noise and measurement noise, and we denote it as AKRSL-EKF. The novel AKRSL-EKF algorithm can effectively adapt the noise to system state variations and achieves better estimation accuracy. The effectiveness of the proposed algorithms for FASE is verified on IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems. The results show that the estimation accuracy of the proposed algorithms is 30% higher than other traditional algorithms, with high estimation accuracy.

An adaptive method for tuning process noise covariance matrix in EKF-based three-phase distribution system state estimation

This paper proposes a new adaptive method for online tuning of process noise covariance matrix in the Extended Kalman Filter based three-phase distribution system state estimator. Specifically, a new form of exponential function is proposed for tuning the process noise covariance matrix, adapting it to the level of state changes. A new indicator derived from normalized innovations of the available real-time and virtual measurements is developed for tracking the level of state changes. The proposed method relies on measurements of the existing distribution networks, and therefore can be easily implemented in the advanced distribution management system. The method efficiently adapts process noise to system state variations and can deal with both quasi steady-state and unexpected sudden state changes caused by changes in the system topology, generation or demand. Comparison results with other state-of-the-art adaptive methods on the modified IEEE 37-bus and IEEE 123-bus distribution systems show that the proposed method achieves better accuracy under quasi steady-state condition while being more robust to unexpected sudden state changes.

Prospects for detecting early warning signals in discrete event sequence data: Application to epidemiological incidence data.

Early warning signals (EWS) identify systems approaching a critical transition, where the system undergoes a sudden change in state. For example, monitoring changes in variance or autocorrelation offers a computationally inexpensive method which can be used in real-time to assess when an infectious disease transitions to elimination. EWS have a promising potential to not only be used to monitor infectious diseases, but also to inform control policies to aid disease elimination. Previously, potential EWS have been identified for prevalence data, however the prevalence of a disease is often not known directly. In this work we identify EWS for incidence data, the standard data type collected by the Centers for Disease Control and Prevention (CDC) or World Health Organization (WHO). We show, through several examples, that EWS calculated on simulated incidence time series data exhibit vastly different behaviours to those previously studied on prevalence data. In particular, the variance displays a decreasing trend on the approach to disease elimination, contrary to that expected from critical slowing down theory; this could lead to unreliable indicators of elimination when calculated on real-world data. We derive analytical predictions which can be generalised for many epidemiological systems, and we support our theory with simulated studies of disease incidence. Additionally, we explore EWS calculated on the rate of incidence over time, a property which can be extracted directly from incidence data. We find that although incidence might not exhibit typical critical slowing down properties before a critical transition, the rate of incidence does, presenting a promising new data type for the application of statistical indicators.

RBPPFF for robust TAN

Terrain-aided navigation (TAN) is a technique for position estimation based on altimeter and digital elevation model measurements. To perform TAN, non-linear filters such as a particle filter (PF) or a point mass filter (PMF) are mainly used owing to the non-linearity of measurements and systems. In particular, owing to the degradation of the global positioning system or sensors and the non-linearity of maps and measurements, an initial state error or sudden state change may occur. The authors propose a Rao-Blackwellised particle-point mass fusion filter (RBPPFF) that performs robustly in the presence of such initial state errors or sudden state changes with low computational complexity in real time. The RBPPFF combines the advantages of a PF and PMF, and it employs Rao-Blackwellisation to efficiently increase the number of states. To develop the RBPPFF, the formulae of the RBPF and Rao-Blackwellised point mass fusion filter (RBPMF) are derived using the same conventions, and a method is presented for an effective fusion of the PF and the PMF. Finally, the performance of the proposed algorithm is demonstrated through Monte Carlo simulation across various environments with filters such as PF, PMF, RBPF, and RBPMF.

RETRACTED ARTICLE: On seismic wave propagation through subsurface media

The paper presents a unified elastic-viscoelastic model which can well represent seismic wave propagation through subsurface media. We consider its transient effects, that is, the short-lived wave propagation through certain media caused by a sudden change of state for a very short time. We obtain two kinds of asymptotic expansions with different differential orders in space-time domains for the impact viscoelastic waves by Buchen-Mainardi algorithm method.

Loess is More: The Spatial and Ecological History of Erosion on China’s Northwest Frontier

AbstractThis is a paper about the speed and intensity with which new and intensive human land use in a semi-arid environment can bring about large-scale environmental change. In particular, this paper pinpoints how and why it was that the Yellow River shifted from a long-term condition of relative stability to a later state of frequent floods and course changes in the eleventh century. It is possible to trace the environmental history of this dramatic and sudden change of state with precision and confidence. Historical sources that record the dates and characteristics of flood events downstream align well with those that note the locations and dates of human activity upstream. More important, each aligns well not only with one another, but also with information from environmental science: sediment cores that preserve soil and pollen evidence for the timing and processes of systematic change.

Long-Term Dynamic in Nutrients, Chlorophyll a, and Water Quality Parameters in a Coastal Lagoon During a Process of Eutrophication for Decades, a Sudden Break and a Relatively Rapid Recovery

Coastal lagoons are considered among the marine habitats with the highest biological productivity, and support a great variety of human activities and pressures that make them especially vulnerable to trophic imbalances. While dystrophic crises are common in many lagoons, others like the Mar Menor show homeostatic mechanisms, high resilience, and clear waters. This paper analyses the water column descriptors dynamic during the last 22 years in this coastal lagoon, in the context of a eutrophication process produced by an increase in nutrient inputs, mainly derived from agriculture. Despite water column nitrate concentration increased by one order of magnitude, the lagoon maintained homeostatic regulation for two decades, keeping the water transparency and relatively low levels of nutrients and chlorophyll a (Prebreak phase), followed by a sudden change of state in 2016 with an abrupt increase in average nutrients and chlorophyll a concentration and loss of water transparency (Break phase), and a relatively rapid recovery after the reduction of nutrient discharges (Recovery phase). The activation of the regulation mechanisms seems to manifest through an ammonium production in the water column, as a consequence of the activity in the trophic web. The low correlation between chlorophyll a and nutrients concentration, mainly at small spatio-temporal scales, is in disagreement with eutrophication traditional models, and suggests a rapid response of primary producers to nutrient inputs and a zooplankton control in the short-term, which in turn is controlled by the rest of the trophic web components. Homeostatic properties that in the Mar Menor lagoon have provided resistance to eutrophication are based on several mechanisms: channeling its production toward the benthic system (maintaining high biomasses of primary producers, filter feeders, and detritivores), a top-down control of the pelagic trophic web exerted by ichthyoplankton and jellyfish, and exporting surplus production outside the system. Resilience of the system would be based on the high turnover in the species composition related to the restricted connectivity to the sea, the spatio-temporal variability of the environmental conditions, and the multiplicity of spatial–temporal scales involved in lagoon processes. TRIX index was sensitive to the trophic and water quality changes. However, in our study, its current score does not allow to anticipate or alert the eutrophication risk and the trophic breakpoint of the system.

On the networked architecture of genotype spaces and its critical effects on molecular evolution.

Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, sharing deep qualitative and quantitative similarities with slowly driven physical systems: nonlinear responses analogous to critical transitions, sudden state changes or hysteresis, among others. Furthermore, the phenotypic plasticity inherent to genotypes transforms classical fitness landscapes into multiscapes where adaptation in response to an environmental change may be very fast. The quantitative nature of adaptive molecular processes is deeply dependent on a network-of-networks multilayered structure of the map from genotype to function that we begin to unveil.

Analysis on Strong Tracking Filtering for Linear Dynamic Systems

Strong tracking filtering (STF) is a popular adaptive estimation method to effectively deal with state estimation for linear and nonlinear dynamic systems with inaccurate models or sudden change of state. The key of the STF is to use a time-variant fading factor, which can be evaluated based on the current measurement innovation in real time, to forcefully correct one step state prediction error covariance. The strong tracking filtering technology has been extensively applied in many practical systems, but the theoretical analysis is highly lacking. In an effort to better understand STF, a novel analysis framework is developed for the strong tracking filtering and some new problems are discussed for the first time. For this, we propose a new perspective that correcting the state prediction error covariance by using the fading factor can be thought of directly modifying the state model by correcting the covariance of the process noise. Based on this proposed point of view, the conditions for the STF function to be effective are deeply analyzed in a certain linear dynamic system. Meanwhile, issues of false alarm and alarm failure are also briefly discussed for the strong tracking filtering function. Some numerical simulation examples are demonstrated to validate the results.

Head injury became personal for me: A health professional’s perspective

Head injury became personal for me in February 2007, when, during an afternoon of snowboarding at Sunshine Village in Banff National Park (Alberta), I was found crumpled on the slope with the occipital region of my helmet shattered. The accident was unwitnessed. Apparently I was conscious, although clearly confused, and was eventually seen at the Foothills Hospital in Calgary (Alberta). The most striking initial symptom was amnesia; I lost nearly 30 h of memory, starting from the evening before the accident until some 10 h after the injury. My emotions were, in retrospect, markedly abnormal; I remember an eager, academic interest in what my scan looked like, with, paradoxically, no fear or concern about the obvious implications. This emotional incongruity persisted for several weeks. I was quite happy at home watching television with a cat on my chest, while those around me quietly agonized over my prognosis. This despite being unable to stand without the room rocking like a boat in one-metre-high waves, being disorganized to the point where cooking supper was beyond me and being profoundly and dreadfully fatigued. My head pain was moderate, but the neck pain was dramatic enough that I eventually became quite concerned that a cervical spine injury had been missed. Ibuprofen was moderately effective, but it was sleep that truly improved my pain and brought back some measure of concentration and executive capacity. I did not ever experience nausea or emesis. I was fortunate to be assessed two weeks after the injury, and only then at the urging of others, by a colleague who was a neurosurgeon with expertise in minor traumatic brain injury. Any thoughts of returning to work or exercise were quickly squashed, and “complete cognitive and physical rest” was prescribed. At this point my dizziness was improving and my thoughts were more organized, but my planning, judgement and organizational abilities remained tremendously limited. Recovery was not linear. Rather, there were prolonged periods of equilibrium, followed by sudden state changes over a 24 h or 48 h period. I can recall suddenly realizing one afternoon that I might not work again, and with this realization the ‘happy’ period of my concussion was over. It was several weeks before I was able to concentrate on academic work, and four months before I began to cautiously try my hand at patient care again. I was not fully back at work until the middle of August, more than six months after my injury. Even then, I was subject to fatigue and high cognitive loads, or sudden movements could reproduce dizziness and migrainous headaches. Months later, academic presentations, which had previously been a personal strength, remained challenging, even embarrassing events. It was nearly a year before I returned to running. Critically, others were often as blind as I was to the true degree of my disability. Cocktail chatter, it would appear, does not require a lot of cognitive capacity. While my family was aware of my true level of functioning, my friends, neighbours and even medical colleagues were often fooled. Subsequently, I successfully returned to a full academic and patient care workload. A role as an advocate for concussion care and my research interest in decision support for concussion in children are perhaps the only ongoing consequences of my original injury.

Switching control laws in the presence of measurement noise

In this paper we study a class of systems governed by piecewise continuous control laws, subject to measurement noise and such that more than one state configuration could be a desired point for the system to be stabilized. Such a problem is common in many practical situations, including, for example, vehicle attitude control, robotic manipulator positioning, deployment of multistable structures, etc. Our aim is to characterize, both for continuous and discrete-time models, control properties and switching policies ensuring that any possible state trajectory of the closed-loop system is robustly asymptotically convergent to one of the desired “target points”. First we will show that in the discrete-time case, conditions guaranteeing the asymptotic stability to a set of points can be derived by exploiting the intrinsic “holding” nature of the discrete-time framework. Such results can be extended to continuous time by means of standard sample-and-hold techniques. However, this kind of solution is revealed to be very conservative and then not realistically employable in many contexts. For such a reason further less restrictive policies will be introduced and investigated; these are mostly based on the concept of “strategy holding” in time (by providing a slower switching policy sampling time) and space (by using hysteresis regions). Those strategies will be characterized both in terms of global stabilizability and in terms of switching flexibility, i.e., the capability to react in a convenient way to sudden state changes. Driven by the above analysis an “opportunistic switching” strategy combining all the advantages of the strategies considered will be proposed and analyzed. Synthesis results are presented, providing a constructive procedure to design the proposed control laws. The paper is concluded with an application example based on the attitude control of a Ducted-Fan Aerial Vehicle, showing the effectiveness of the proposed approach in a practical application.

Dicke quantum phase transition with a superfluid gas in an optical cavity

A phase transition describes the sudden change of state of a physical system, such as melting or freezing. Quantum gases provide the opportunity to establish a direct link between experiments and generic models that capture the underlying physics. The Dicke model describes a collective matter-light interaction and has been predicted to show an intriguing quantum phase transition. Here we realize the Dicke quantum phase transition in an open system formed by a Bose-Einstein condensate coupled to an optical cavity, and observe the emergence of a self-organized supersolid phase. The phase transition is driven by infinitely long-range interactions between the condensed atoms, induced by two-photon processes involving the cavity mode and a pump field. We show that the phase transition is described by the Dicke Hamiltonian, including counter-rotating coupling terms, and that the supersolid phase is associated with a spontaneously broken spatial symmetry. The boundary of the phase transition is mapped out in quantitative agreement with the Dicke model. Our results should facilitate studies of quantum gases with long-range interactions and provide access to novel quantum phases.

What to do about apnea of prematurity?

apnea of prematurity and resultant intermittent hypoxic episodes are universal in preterm infants. These events range from presumably benign “periodic” breathing to clinically significant events that may require mechanical ventilation. Persistence of these events is a major factor in prolonging

Numerical simulation of flow around an orbiting cylinder at different ellipticity values

This paper deals with the numerical simulation of low Reynolds number flow (Re=120–180) past a circular cylinder in orbital motion. The Navier–Stokes equations, pressure Poisson equations and continuity are written in primitive variables in a noninertial system fixed to the orbiting cylinder and solved by the finite difference method. Ellipticity values between 0 and 1.2 (from pure in-line oscillation through a full circle and beyond) were investigated. Sudden changes in state (jumps) are found when time-mean or root-mean-square values of force coefficients or energy transfer are plotted against ellipticity. Pre- and post-jump analysis was carried out by investigating limit cycles, time-histories, phase angles and flow patterns. These investigations revealed that ellipticity can have a large effect on the energy transfer between the incompressible fluid and a circular cylinder forced to follow an orbital path, and that small changes in the amplitude of transverse motion can have a dramatic effect. The phase angle was altered by about 180° at the jumps. Also investigated were the direction of orbit, which affects the state curves belonging to the time-mean values of lift only, and the effect of initial conditions, which alters the location of jumps without changing the state curves.