Search Results (6)

A secondary particle filter (SPF) inversion method for geostationary space object characteristics based on ground photometric data is presented. The method combines the estimation results of the standard particle filter (PF) algorithm and the resampling algorithm of the particle generation process. SPF first generates N particles according to the standard PF process, and performs the standard PF without resampling. Particle weight is an important indicator to determine the closeness of particles to the real state. With the progress of PF, the weight of particles closer to the real state will gradually increase. SPF takes the particle weight value as an important basis to judge the closeness of particles to the real state. By setting a threshold, the particles closest to the real state are screened out and roughened. The SPF method in this paper uses a particle filter twice and it is a new particle filter method. The first particle filter identifies particles near the real state. Before the second particle filter, it is equivalent to the actual state distribution of the system is known, so that the distribution of initial particles can be set more efficiently and effectively, and the number of particles close to the real state of the system can be increased. Experiment results show that the estimation error and the RMSE of the inversion error of SPF are less than PF, which not only shows that the inversion result based on SPF is better than the inversion result based on PF, but also proves the effectiveness of the inversion method based on SPF. Full article

The solid particle number emissions of Diesel vehicles are very low due to the particulate filters as exhaust aftertreatment devices. However, periodically, the trapped particles are oxidized (i.e., active regeneration) in order to keep the backpressure at low levels. The solid particle number emissions during regenerations are only partly covered by the regulations. Many studies have examined the emissions during regenerations, but their contribution to the overall emissions has not been addressed adequately. Furthermore, the number concentration of volatile particles, which is not included in the regulations, can be many of orders of magnitude higher. In this study, the particulate emissions of two light-duty Euro 6 vehicles were measured simultaneously at the tailpipe and the dilution tunnel. The results showed that the weighted (i.e., considering the emissions during regeneration) solid particle number emissions remained well below the applicable limit of 6 × 10¹¹ #/km (solid particles > 23 nm). This was true even when considering solid sub-23 nm particles. However, the weighted volatile particle number emissions were many orders of magnitude higher, reaching up to 3 × 10¹³ #/km. The results also confirmed the equivalency of the solid particle number results between tailpipe and dilution tunnel locations. This was not the case for the volatile particles which were strongly affected by desorption phenomena. The high number of volatiles during regenerations even interfered with the 10 nm solid particle number measurements at the dilution tunnel, even though a catalytic stripper equipped instrument was also used in the dilution tunnel. Full article

Data assimilation has been widely applied in atmospheric and oceanic forecasting systems and particle filters (PFs) have unique advantages in dealing with nonlinear data assimilation. They have been applied to many scientific fields, but their application in geoscientific systems is limited because of their inefficiency in standard settings systems. To address these issues, this paper further refines the statistical observation and localization scheme which used in the classic localized equivalent-weights particle filter with statistical observation (LEWPF-Sobs). The improved method retains the advantages of equivalent-weights particle filter (EWPF) and the localized particle filter (LPF), while further refinements incorporate the effect of time series on the reanalyzed data into the statistical observation calculations, in addition to incorporating the statistical observation proposal density into the localization scheme to further improve the assimilation accuracy under sparse observation conditions. In order to better simulate the geoscientific system, we choose an intermediate atmosphere-ocean-land coupled model (COAL-IC) as the experimental model and divide the experiment into two parts: standard observation and sparse observation, which are analyzed by the spatial distribution results and root mean square error (RMSE) histogram. In order to better analyze the characteristics of the improved method, this method was chosen to be analyzed in comparison with the localized weighted ensemble Kalman filter (LWEnKF), the LPF and classical LEWPF-Sobs. From the experimental results, it can be seen that the improved method is better than the LWEnKF and LPF methods for various observation conditions. The improved method reduces the RMSE by about 7% under standard observation conditions compared to the traditional method, while the advantage of the improved method is even more obvious under sparse observation conditions, where the RMSE is reduced by about 85% compared to the traditional method. In particular, this improved filter not only combine the advantage of the two algorithms, but also overcome the computing resources. Full article

The electric vehicle has become an important development direction of the automobile industry, and the lithium-ion power battery is the main energy source of electric vehicles. The accuracy of state of charge (SOC) estimation directly affects the performance of the vehicle. In this paper, the first order fractional equivalent circuit model of a lithium iron phosphate battery was established. Battery capacity tests with different charging and discharging rates and open circuit voltage tests were carried out under different ambient temperatures. The conversion coefficient of charging and discharging capacity and the simplified open circuit voltage model considering the hysteresis characteristics of the battery were proposed. The parameters of the first order fractional equivalent circuit model were identified by using a particle swarm optimization algorithm with dynamic inertia weight. Finally, the recursive formula of a fractional extended Kalman filter was derived, and the battery SOC was estimated under continuous Dynamic Stress Test (DST) conditions. The results show that the estimation method has high accuracy and strong robustness. Full article

Diesel exhaust has been associated with asthma, but its response to other engine emissions is not clear. The increasing prevalence of vehicles with gasoline direct injection (GDI) engines motivated this study, and the objective was to evaluate pulmonary responses induced by acute exposure to GDI engine exhaust in an allergic asthma murine model. Mice were sensitized with an allergen to induce airway hyperresponsiveness or treated with saline (non-allergic group). Animals were challenged for 2-h to exhaust from a laboratory GDI engine operated at conditions equivalent to a highway cruise. Exhaust was filtered to assess responses induced by the particulate and gas fractions. Short-term exposure to particulate matter from GDI engine exhaust induced upregulation of genes related to polycyclic aromatic hydrocarbon (PAH) metabolism (Cyp1b1) and inflammation (TNFα) in the lungs of non-allergic mice. High molecular weight PAHs dominated the particulate fraction of the exhaust, and this response was therefore likely attributable to the presence of these PAHs. The particle fraction of GDI engine exhaust further contributed to enhanced methacholine responsiveness in the central and peripheral tissues in animals with airway hyperresponsiveness. As GDI engines gain prevalence in the vehicle fleet, understanding the health impacts of their emissions becomes increasingly important. Full article

The state of charge (SOC) is the residual capacity of a battery. The SOC value indicates the mileage endurance, and an accurate SOC value is required to ensure the safe use of the battery to prevent over- and over-discharging. However, unlike size and weight, battery power is not easily determined. As a consequence, we can only estimate the SOC value based on the external characteristics of the battery. In this paper, a cubature particle filter (CPF) based on the cubature Kalman filter (CKF) and the particle filter (PF) is presented for accurate and reliable SOC estimation. The CPF algorithm combines the CKF and PF algorithms to generate a suggested density function for the PF algorithm based on the CKF. The second-order resistor-capacitor (RC) equivalent circuit model was used to approximate the dynamic performance of the battery, and the model parameters were identified by fitting. A dynamic stress test (DST) was used to separately estimate the accuracy and robustness of the CKF and the CPF algorithms. The experimental results show that the CPF algorithm exhibited better accuracy and robustness than the CKF algorithm. Full article