Space cooling using geothermal single‐effect water/lithium bromide absorption chiller

Gadsden

Energy Harvesting and Storage: Materials, Devices, and Applications XIII

2023

The sliding innovation filter (SIF) is a recently developed estimation technique that has gained widespread use. It is a predictor-corrector filter that utilizes a hyperplane and applies a force to allow estimates to fluctuate about it. SIF belongs to the same family as the smooth variable structure filter and sliding mode observer, and it is stable and robust in the face of uncertainties. This paper discusses the use of SIF for estimating the states of Power Converters, which play a crucial role in Electric Vehicles (EVs) by converting high-voltage DC from the battery to low-voltage AC used by the motor. One of the main challenges in Power Converters is accurately estimating their states, such as input voltage, output voltage, and inductor current, which are critical for optimal control and efficient operation. The SIF has demonstrated promising results in addressing this challenge.

Section: Introductionmentioning

confidence: 99%

Sliding innovation filter to estimate power converters of electric vehicles

Gadsden

Energy Harvesting and Storage: Materials, Devices, and Applications XIII

2023

“…Combining filters from both classes improves estimate efficiency and reduces limitations [115][116][117][118][119][120][121][122][123][124][125]. Moreover, several studies have proposed new filtering techniques for various applications, such as robotic manipulators, pneumatic systems, battery condition monitoring, and photovoltaic/thermal systems, among others [126][127][128][129][130][131][132][133][134][135][136][137][138][139] and Kalman filters are widely used for state estimation in various fields, as seen in recent research studies such as adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation, vehicle running states-fused estimation strategy, and multi-area distributed state estimation in smart grids . * malshabi@sharjah.ac.ae…”

Section: Introductionmentioning

confidence: 99%

Sliding innovation filter for micorgrid application

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

El-Nadi

et al. 2023

Currently, microgrids are frequently used and various control algorithms have been applied to improve their performance in both grid-connected and islanded modes. However, research has shown that incorporating a filtering technique into the controller can lead to even better performance. As a result, a simple controller with a filter can perform just as well as a complex controller that operates alone. This study focuses on the performance of a microgrid using a new filter called the sliding innovation filter, which is known for its robustness and stability. The filter is an excellent option for estimating performance under various conditions, such as load and injected powers. To demonstrate the filter's advantages, modeling uncertainties are introduced into the system while the filter is estimating states. The filter's performance is evaluated using a MATLAB© simulation environment.

“…In this paper, a new variant of the SIF is proposed with the intention of addressing these challenges in a straightforward while simultaneously effective manner [135][136]. The SIF is incorporated into the Luenberger method, which is well-known for its capacity to uncover latent states based on observable data using the measurements that are readily available [137][138][139][140][141][142][143][144][145][146][147][148][149][150][151][152]. The proposed method seeks to achieve a balance between simplicity and effectiveness while maintaining stability and robustness in the filtering process.…”

Section: Introductionmentioning

confidence: 99%

The extended Luenberger sliding innovation filter

Gadsden

Radar Sensor Technology XXVII

2023

The sliding innovation filter is a newly developed filter that was derived in 2020 to be a predictor-corrector filter. The filter uses the measurement as a hyperplane, and then applies a force that makes the estimates fluctuating around it. The filter works on systems with full ranked measurement matrix (all states are measured). However, once the rank becomes partial, the filter depends highly on the pseudo inverse of the measurement matrix. This means that if the measurement matrix does not have a direct link to the hidden states, then these states will not be correctly estimated. When the system is nonlinear, the problem becomes worse as the Jacobean matrix must be calculated for the measurement matrix before the pseudo inverse is applied. To solve this issue, this paper proposes a new formulation of the SIF that is based on the extended Luenberger filter. The proposed method is tested on extracting the damping ration for a third order system.