University of Engineering & Technology, Rasul

State of Charge (SOC) estimation plays a crucial role in managing the performance and longevity of electric vehicle (EV) batteries. Various estimation techniques, including various Kalman Filters (KF), have been employed for this purpose. The mathematical process and limits of different KF family algorithms are analysed. In this study, an impact of nonlinear KF parameters on SOC estimation accuracy for EV batteries is analyzed. Specifically, the performance of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) is examined. EKF is most preferred for SOC estimation whereas introducing sigma points in UKF is the basic difference between EKF and UKF. In comparison to EKF and UKF, PF is more flexible since it applies to nonlinear systems with non-Gaussian distributions. Utilising a sequence of noisy measurements, KF is a recursive method that approximates the state of a dynamic system. It requires accurate parameters such as process noise covariance (Q) and measurement noise covariance (R) to work effectively. In this work, the effect of these parameters on state of charge estimation using nonlinear KFs has been analysed under different settings of noise covariance matrices. Tuning these parameters is essential for optimizing the performance of the KF and ensuring accurate and reliable state estimation in real-world applications. To find parameter values that provide the best balance between accuracy, robustness, and computational efficiency, the tuning process involves experimentation, simulation, and validation using real-world data. According to the results observed, the filter parameters such as three diagonal elements of Q are selected as 2e-8, 0.005, 0.003 and R as 2e-6. By evaluating the state estimation accuracy under different driving scenarios, robustness and sensitivity of filtering technique in estimating SOC for EV batteries has been studied. This analysis contributes to the optimization of SOC estimation algorithms, enhancing the reliability and efficiency of EV battery management systems.

Motivated by the need for controllable fast-light platforms in compact photonic systems, this work investigates surface plasmon polariton (SPP)-assisted superluminal group propagation in a four-level cesium atomic medium driven by Laguerre-Gaussian control fields. The aim is to examine how the beam waist and intensity of the structured control fields, together with plasmonic field confinement, modify absorption, dispersion, group index, and group velocity. Using a density-matrix formulation, the probe-field coherence is obtained, the optical susceptibility is calculated, and the group velocity is evaluated from the dispersion slope while the SPP contribution is included through the effective dielectric response. The results show that increasing the Laguerre-Gaussian beam waist broadens the spatial region of anomalous dispersion and negative group index, while SPP confinement strengthens the local atom-field interaction and enhances susceptibility modulation. The study demonstrates a tunable route for controlling localized superluminal group propagation without implying superluminal information transfer, with potential applications in optical switching, photonic signal processing, plasmonic sensing, and quantum optical devices.

The Fresnel light dragging effect has remained a subject of significant interest for over two centuries, describing the modification of light propagation in a moving medium. This effect is strongly influenced by the dispersive properties of the medium and is commonly manifested through the phase shift of transmitted light, which varies with the velocity of the medium. In this work, we investigate the phase shift induced by varying the velocity of a moving microcavity. Our results show that a phase shift of approximately 10 m rad is obtained at a velocity of 2 m/s, which increases to about 40 m rad as the velocity reaches 10 m/s. Additionally, the motion of the microcavity significantly affects the transmission of the probe field, leading to measurable perturbations that depend on the induced phase shift. Specifically, the perturbation increases from 0.004 at 2 m/s to 0.02 at 10 m/s. These findings demonstrate that increasing the velocity of the medium enhances the light dragging effect, resulting in a greater modification of light propagation compared to its behavior in a vacuum. This study provides valuable insight into the control of light-matter interaction in dynamic optical systems and its potential applications in advanced photonic technologies.

Light-matter interaction between a light field and an atomic medium could be used to manipulate the medium’s dispersive properties, leading to subluminal and superluminal regimes. Utilizing the same idea, this work investigates the propagation of structured superluminal light and anomalous phase shifts in a Λ- type three-level atomic medium, where a structured control field governs the atomic system. The absorption and dispersion properties are modulated by the azimuthal quantum number (ℓ) of the control field. The maxima and minima of the absorption spectrum, along with the corresponding normal and anomalous dispersion peaks, are enhanced with a (2ℓ) dependence on the azimuthal quantum number in the medium. The negative group index and anomalous phase shift vary within the ranges −1000 ≤ ng ≤ −900 and −6.5 × 10 5 ≤ ϕ ≤ −5.6 × 10 5 rad, respectively. The corresponding group velocity lies in the range −3.33 × 10 5 ≤ vg ≤ −3.0 × 10 5 m/s within the spatial region −2λ ≤ x, y ≤ 2λ. Furthermore, the anomalous phase shift is significantly enhanced in regions exhibiting superluminal behavior, following the (2ℓ) azimuthal quantum number dependence. The findings of this work may have potential applications in optical storage devices, imaging, coding, and the design of advanced photonic technologies.

Social networks have become an integral part of the society, facilitating connections and collaborations among individuals and entities. Social Internet of Things (SIoT) allows for seamless interactions and data sharing by integrating smart devices and sensors into social networks. Trust within the SIoT ecosystem is crucial for ensuring the reliability and security of interactions among users. This paper explores the dynamics of trust within the Social IoT environment. Our research comprises of four essential components: Trust Composition, Aggregation, Decision, and Propagation. Trust Composition involves integrating three trust factors: social similarity, feedback, and honesty. Trust Aggregation is used to consolidate individual trust ratings, providing aggregated trust measures for decision-making processes. Trust Decision mechanisms allow entities to make informed decisions regarding interactions and collaborations. We propose a new trust propagation technique, which enhances the effectiveness of trust dissemination across the SIoT network. By leveraging social relationships, historical feedback, and contextual information, our trust propagation model ensures efficient and reliable trust dissemination, fostering secure and trustworthy interactions. Experimental results using Sigcomm dataset and Epinion dataset has proven the proposed trust management framework's effectiveness in accurately evaluating trustworthiness and efficiently propagating trust while detecting untrustworthy nodes.