YorkSpace

YorkSpace is York University's Institutional Repository. It supports York University's Senate Policy on Open Access by providing York community members with a place to preserve their research online in an institutional context.

Photo by NASA on Unsplash

Communities in YorkSpace

Select a community to browse its collections.

Now showing 1 - 63 of 63

Recent Submissions

  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    An Adaptive Kalman-Guided Soft Sensor Using Feedforward Neural Networks for SOC Estimation in Lithium-Ion Batteries
    (IEEE, 2025) Mahdi Yousef, Mostafa; Shaterabadi, Mohammad; Karimi, Houshang
    Accurate State-of-Charge (SOC) estimation is crucial for ensuring the battery’s safe operation and prolonged lifespan, but it remains a challenge due to sensor noise and system nonlinearity. This paper proposes a novel covariance-adaptive hybrid approach that interprets neural network outputs as virtual measurements and dynamically integrates them with the a priori state predictions from a Discrete Kalman filter to yield refined a posteriori estimates. The introduced adaptive scheme uses exogenous inputs derived from spectral analysis to provide past information to the Feedforward Neural Network (FNN) to perform soft sensing and generate network-aided measurements. The novel processing occurs after this step by adaptive integration with Kalman filter results. The developed hybrid architecture is tested on real experimental battery datasets for LG 18650 HG2, and the results prove its superior performance against existing state-of-the-art algorithms. Experimental results averaged over twenty Monte Carlo trials demonstrated nearly 28% and 39% improvement in the mean absolute error and root mean square error, respectively, compared to the baseline results.
  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    Enhancing Memory-Limited Feedforward Neural Networks for State of Charge Estimation through Temporal Feature Engineering
    (IEEE, 2025-08-25) Mahdi Yousef, Mostafa; Shaterabadi, Mohammad; Karimi, Houshang
    State-of-charge (SOC) estimation is a key function of Battery Management Systems (BMS) in electric vehicles and battery energy storage systems. However, SOC is not directly measurable, making accurate estimation inherently challenging. Data-driven approaches offer a practical solution by leveraging measurable inputs such as voltage, current, and temperature. Feedforward Neural Networks (FNNs) are attractive due to their low computational complexity, but they lack inherent temporal memory, unlike recurrent architectures. This paper investigates three established casual smoothing techniques-moving average, Butterworth filtering, and exponential moving average-as temporal memory proxies for enhancing FNN-based SOC estimation. Their effectiveness is supported by frequency-domain analysis using the Fast Fourier Transform (FFT), which reveals that key signal dynamics occur at ultra-low frequencies (less than 0.1 mHz), justifying the use of smoothing as memory-preserving transformations. The main contribution of this work is a unified, frequency-informed evaluation framework that systematically benchmarks these techniques under consistent conditions and across varying temperatures. All models are trained and evaluated on LG 18650HG2 Lithium-ion battery data, with 20 repeated runs per model to ensure statistical robustness.
  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    Advanced Energy Management and Planning Strategies for the IEEE 33-Bus System: Integration of EV Penetration, INVELOX Turbines, and Bifacial PV Panels
    (IEEE, 2026-04-09) Shaterabadi, Mohammad; Mehrjerdi, Hasan; Karimi, Houshang
    The rapid urbanization and growing population in cities present significant challenges in energy management. Smart cities aim to address these challenges by leveraging advanced technologies to optimize energy production, distribution, and consumption. This paper explores advanced energy management and planning strategies for the IEEE 33-Bus system through the synergistic integration of Electric Vehicle (EV) penetration, INVELOX turbines (IWT), and bifacial photovoltaic (BPV) panels. These innovative technologies offer substantial potential for enhancing power distribution systems’ efficiency, reliability, and sustainability. The integration of these elements is examined to understand their combined impact on energy management, system stability, and overall grid performance. The findings provide a comprehensive understanding of how these technologies can be harnessed to optimize energy management in modern power systems. Multi-objective functions, including total cost and emission, are considered, which should be minimized simultaneously. The study models a mixed integer quadratic linear programming (MIQCP) in GAMS software and solves it using CPLEX. Furthermore, the weighted sum approach is used to solve this problem. The final results show the efficiency and accuracy of responses and the operator’s planning in addressing the distribution network (DN) issues.
  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    Ionization of the water molecule by electron and positron impact
    (IOP Publishing, 2010-01-29) Tóth, István; Campeanu, Radu I.; Chiş, V.; Nagy, Ladislau
    Theoretical cross-sections for the ionization of the water molecule by electron and positron impact are presented. The calculations were performed in the framework of the simpler CPE and two distorted-wave models, ES and TS, by employing Gaussian wavefunctions for the description of the target. We found good agreement with the experiment, especially for higher impact energies.
  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    Archiving Hong Kong in Canada: Digital Preservation, Community Collaboration, and the Future of Diasporic Collections
    (The Reading Room: A Journal of Special Collections, 2026) Leong, Jack Hang-tat
    This paper examines the evolution and significance of Hong Kong-related and Chinese Canadian collections in Canada, addressing the persistent challenge of fragmented resources and the urgent need for sustainable curation. By surveying major archives and initiatives, the study identifies trends in digital preservation and collaborative approaches among curators, researchers, and communities. These efforts not only safeguard cultural heritage but also advance library and information science scholarship by informing archival theory, policy development, and community engagement frameworks. The research highlights the role of digital platforms and partnerships in preserving born-digital and digitized materials for interdisciplinary inquiry and inclusive knowledge production.