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.

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Recent Submissions

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    The reinvention of an energy hub’s planning by considering tidal turbine, bi-facial PV panels, and INVELOX: A unique outlook on the combination performance of novel RERs
    (IEEE, 2024-11-26) Shaterabadi, Mohammad; Mehrjerdi, Hasan; Karimi, Houshang; Iqbal, Atif; Jirdehi, Mehdi Ahmadi
    New approaches must be taken due to the rapid growth of societies globally and the urgent need for more energy, climate change, and global warming. One of these approaches is supporting and expanding new and efficient renewable energy resources (RERs). Therefore, this paper is dedicated to the energy planning and management of the proposed structure in which novel renewable-based technologies such as INVELOX wind turbines, Bi-facial photovoltaic (PV) panels, and tidal turbines (TT), in addition to the demand response programs (DRP) especially time of use (TOU), are investigated. The purpose is to simultaneously reducing the presented system’s overall cost and environmental pollution. The problem is modeled as mixed-integer linear programming (MILP) in GAMS software and solved using a CPLEX solver. The final results show that novel technologies recognized as efficient can significantly reduce contamination, expenses, and greenhouse gas (GHG) emissions while supplying our rapidly growing demand.
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    The energy management and planning of the proposed Water-Energy nexus concept by considering tidal turbine and INVELOX as novel options
    (IEEE, 2024-11-08) Shaterabadi, Mohammad; Mehrjerdi, Hasan; Dehghanian, Payman; Karimi, Houshang
    The energy and water crisis due to the rapidly growing population and developing societies caused countries and governments to think about novel technologies and ways to respond to this issue. Therefore, this paper is about energy planning and management of the proposed concept for supplying water and energy simultaneously. This approach helps to overcome and supply the dramatic energy need of desalination and distribution systems. This article aims to supply the demands and minimize the total cost at the same time. Various renewable and non-renewable energy resources such as tidal turbines, INVELOX turbines, CHP, micro-turbines, and other elements are utilized in the proposed planning. Real weather conditions are considered to compute the output power of wind and tidal turbines. The presented water-energy nexus structure is modeled as a MINLP concept and solved in GAMS software using various solvers to illustrate the reliability of the optimization answers. The final results show a high improvement in the total cost reduction.
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    The impact of tie line capacity on the energy planning of the multi-lateral grid by considering Bi-facial PV
    (IEEE, 2026-02-03) Shaterabadi, Mohammad; Mehrjerdi, Hasan; Karimi, Houshang
    The global energy landscape faces the pressing challenges of resource scarcity and environmental degradation due to fossil fuel dependency. This study investigates the integration of Bi-facial Photovoltaic (BPV) panels within a multi-lateral grid framework, emphasizing their impact on cost, profit, and pollution. By employing a multi-objective optimization approach, the problem is modeled as Mixed Integer Linear Programming (MILP) and solved using GAMS with advanced solvers like CPLEX and Baron. Four distinct scenarios are analyzed to evaluate the proposed energy strategy. The findings highlight the substantial benefits of BPV integration, demonstrating optimized values: a total cost of $19.751, a profit of $270.628, and a pollution level of 1003.268 kg. Furthermore, increasing tie line capacity significantly enhances economic outcomes without compromising environmental standards, yielding a recalibrated profit of $44.269 for building 1, a reduced cost of $265.671 for building 2, and a maintained pollution level. These results underscore the critical role of grid infrastructure optimization in balancing economic efficiency and sustainability. This research provides a novel perspective on renewable energy planning, showcasing BPV panels as a cost-effective, environmentally advantageous solution. It also highlights the strategic importance of enhancing transmission infrastructure to improve economic returns while maintaining ecological responsibility. The insights presented offer a robust framework for academics, policymakers, and industry stakeholders, advancing sustainable energy system optimization and long-term energy resilience.
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    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.
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    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.