Student Publications

Data-driven analysis of electric vehicle charging impact on power distribution systems

Electric vehicles (EVs) are becoming increasingly essential for reducing greenhouse gas emissions and promoting a more sustainable transportation sector. However, EV adoption also introduces a significant electrical load. This paper analyzes real-world EV charging data alongside household electrical load data to evaluate the impact of EV charging on power distribution systems. The EV charging data were collected from a pilot program in Saskatchewan, Canada, while the household load data were obtained through advanced metering infrastructure (AMI) in the same province. The pilot program categorized participants into three groups: Open Choice, Targeted Charging, and Peak Avoidance. The Targeted Charging and Peak Avoidance groups received incentives to shift their charging behavior and reduce peak demand. Our analysis shows that such incentives are highly effective in reducing peak demand—for example, the Targeted Charging group achieved a 51% reduction. However, simulation results indicate that incentivized charging can overload transformers in low-voltage power distribution systems. The study shows that the evaluated power distribution system can handle 2 EVs per house under normal conditions. However, under extreme conditions (e.g., hot days), the existing power infrastructure cannot handle 1 EV per house, even with incentives. In such cases, an optimized charging strategy is necessary. The study also indicates that the voltage sag is not the bottleneck of the EV penetration. This study provides a foundational reference for utilities to design smart EV charging programs, assess their impacts, and plan for future infrastructure needs.

Gharebaghi, M., Wang, Z., Paranjape, R., Pederson, S., Kozoriz, D., & Fick, J. (2025). Data-driven analysis of electric vehicle charging impact on power distribution systems. In 2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall). IEEE.

https://doi.org/10.1109/VTC2025-Fall65116.2025.11310012

State of charge estimation in Li-ion batteries using a robust nonlinear observer with low-cost sensors

This paper presents a robust Luenberger observer for accurate State of Charge (SOC) estimation in lithium-ion batteries. A Lyapunov-based strategy is used to make the system work appropriately with model uncertainties as well as measurement problems. The proposed observer jointly estimates SOC, terminal voltage, and sensor-induced measurement errors using a second-order battery model. This method is particularly useful for stationary energy storage systems using low-cost sensors. Low-cost sensors typically have a large bias with inaccurate measurements. We conducted the experiment using a 25 A lowcost current sensor. In the experiment, we added bias as an error to the current measurement. The experimental results show that, compared to the conventional Sliding Mode Observer (SMO), the proposed Luenberger observer significantly decreases the measurement error, effectively improving the accuracy of the SOC estimation. In addition, the observer decreases chattering effects and offers robust performance. The proposed observer can be used to reduce the battery management systems using low-cost sensors, without compromising the SOC estimation accuracy.

Wang, Z., Gharebaghi, M., & Rezaei, O. (2025). State of charge estimation in Li-ion batteries using a robust nonlinear observer with low-cost sensors. In 2025 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Middle East). IEEE.

A Two-Stage Framework for Spatio-Temporal Clustering and Forecasting of Electric Vehicle Charging

The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for power systems, particularly in understanding and forecasting charging behavior. This paper presents a two-stage framework to segment and forecast electric-vehicle (EV) charging behavior using spatio-temporal features extracted from one year of 15-minute telematics for 264 EVs in Saskatchewan, Canada. The feature set combines temporal usage (power/energy levels and variability) with spatial mobility (home-charging ratio, location diversity, travel distance and angular dispersion). We compare three complementary clustering families including Fuzzy C-Means (soft membership), Gaussian Mixtures (probabilistic partitions), and Hierarchical clustering (multiscale structure) and using algorithm-appropriate selection criteria (Xie–Beni, BIC, elbow/Calinski–Harabasz) to ensure a fair assessment. The framework identifies five behaviorally meaningful groups ranging from home-dominant commuters to high-mobility, DC fast-charging users. Hierarchical clustering provides the most balanced and interpretable segmentation (lowest Davies–Bouldin; highest Calinski–Harabasz), while GMM attains the highest Silhouette (0.585) with coarser partitions. In the supervised stage, ensemble classifiers forecast user-segment membership from features, with XGBoost achieving 98.1% accuracy. The outputs translate directly into planning inputs: expected residential feeder peaks for home-centric segments, DC fast-charging demand for high-mobility users, and demand-response potential for managed charging. The results indicate that spatio-temporal features coupled with hierarchical segmentation and gradient boosting provide actionable and scalable inputs for siting, tariff design, and grid operations.

Shahrokhi, S., Wang, Z., Paranjape, R., Kozoriz, D., Fick, J., & Pederson, S. (2025). A Two-Stage Framework for Spatio-Temporal Clustering and Forecasting of Electric Vehicle Charging. IEEE Access, 13, 182345–182364.

An analytical framework to quantify municipal solid waste disposal service inequality using three simple socioeconomic indicators

The ability of residents in an industrialized nation to afford waste management services is linked to equitable distribution of resources across diverse populations. Landfilling is the ultimate endpoint of all waste management systems, and landfill disposal fees have a significant impact on the financial sustainability and accessibility of waste management systems. This study introduces three indicators: Disposable Income Affordability (DIA), Household Burden Index (HBI), and Labor Cost Index (LCI) to assess the economic burden of disposal fees on individuals, households, and labor in four selected U.S. states. A Borda Count-based method was then used to compute a composite affordability score. Considerable disparities were observed from 2016 to 2023. Florida had the highest mean DIA (961 US tons/person), indicating strong individual affordability. Conversely, Maine recorded the highest mean HBI and LCI values of 1.25 × 10−3 and 6.70 × 10−4 ton−1, respectively, suggesting a higher household financial strain and possible underinvestment in labor relative to tipping revenue. Lower-income states are experiencing up to 40 % higher household affordability burdens. California ranked first with 12 points, reflecting favorable economic and policy conditions. Maine ranked lowest with 3 points, highlighting ongoing affordability challenges. The results underscore the importance of multidimensional affordability metrics that encompass economic, social, and labor factors. The proposed framework offers a replicable, policy-relevant tool for evaluating the distributional effects of tipping fees. The results support a waste management system that is more equitable and transparent across diverse regional and economic contexts.

Darko-Budu, P. A., Nayan, A. H., Jamil, M. S., Richter, A., Ng, K. T. W. (2025) "An analytical framework to quantify municipal solid waste disposal service inequality using three simple socioeconomic indicators". Environmental and Sustainability Indicators, 28, 101037.

Evaluating climatic vulnerability of Saskatchewan’s road network through inverse distance weighting (IDW) and equal weighting approaches

This study assesses the climatic vulnerability of Saskatchewan’s road network by examining how six climate-related factors—minimum and maximum temperature, rainfall, wind, snow, and flooding—affect infrastructure sustainability. Using Inverse Distance Weighting (IDW) interpolation and remote sensing, a composite risk index was developed with equal weighting for each factor. The resulting map classified road segments into five risk categories, revealing that approximately 3,800 km (14%) fall within high or very high-risk zones, mainly in southern and central regions. The spatial analysis highlights region-specific exposure and the influence of uniformly weighted variables on identifying vulnerable infrastructure. This framework offers a simplified, transparent approach to climate risk assessment, supporting spatial decision-making for infrastructure planning, asset management, and climate adaptation.

Islam, M., & Kabir, G. (2026). Evaluating climatic vulnerability of Saskatchewan’s road network through inverse distance weighting (IDW) and equal weighting approaches. In Ž. Stević, O. Prentkovskis, M. Kostadinović, & A. Danilevičius (Eds.), New horizons of transport and communications 2025. Springer.

Application of Interactive Threat Matrix Induced System Dynamics Model to Determine Risk Probability and Resilient Policy Measures for CO2 Pipelines

In the sphere of decarbonization, a comprehensive CO2 (Carbon dioxide) pipeline risk analysis framework is crucial for resilient long-term operations. Canadian Standards Association (CSA) updated regulations Z662:23 requires operators and regulatory bodies to develop quantitative risk assessment methodologies with probability and consequence analysis. Thus, this study is aimed at determining risk probability of CO2 pipelines across Canada, while developing a simulation tool for consecutive policy analysis. The process involves integration of threat matrix from real gas pipeline incident dataset, long-short term memory (LSTM) model and system dynamics (SD) simulation. Baseline simulation represents a risk probability value of 5.89 with a synthetic integrity of 55.1 % by 2055. Sensitivity analysis, calibration, scenario analysis and structural validity have been performed to check the numerical boundary adequacy, accuracy and variability of the built SD model. Among two policies simulated, Policy 2 has been found to be more resilient, as it restrained the risk probability to a value of 2.54 with an increased 77.4 % pipeline integrity. The developed methodology is a simplified risk probability analysis tool for CO₂ pipelines, with extensible features to incorporate further consequences and economic analysis.

Rafew, S. M., & Kabir, G. (2025). Application of Interactive Threat Matrix Induced System Dynamics Model to Determine Risk Probability and Resilient Policy Measures for CO2 Pipelines. Reliability Engineering & System Safety, 112034.

https://doi.org/10.1016/j.ress.2025.112034

Comprehensive Review of Improvement in Inventory Management Methods through Digitalization: Traditional Practices and Emerging Trends

Efficient inventory management is necessary to enhance supply chain performance. Traditional models such as economic order quantity (EOQ), just-in-time (JIT), material requirements planning (MRP), and reorder point (ROP) fail to satisfy the demands in the current dynamic supply chain. This study tries to perform a comparative review of these methodologies in integration with emerging digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), radio frequency identification (RFID), and blockchain. The research evaluates each method in terms of effectiveness, adaptability, scalability and implementation complexity based on various academic and industry sources. Traditional systems remain cost-effective in a stable context; however, they frequently lack the responsiveness required in technology-driven inventory management. On the other hand, digital tools provide greater transparency and predictive capabilities, but they are more challenging due to performance cost and technical barriers. To address this gap, this paper describes both methods and compares them to identify weaknesses and strengths and offers an insight into a hybrid model that integrates the strengths of both paradigms. This approach may facilitate a slight transition toward digitalization, leading to greater resilience and operational efficiency. The findings tend to inform both practitioners and researchers interested in optimizing inventory strategies.

Rezaeinavaei, S., & Khan, S. (2025, October). Comprehensive Review of Improvement in Inventory Management Methods through Digitalization: Traditional Practices and Emerging Trends. In 2nd IEOM World Congress on Industrial Engineering and Operations Management,

Effect of Virtual Mass and Time Delay on the Stability of Haptic Rendering

Virtual mass simulation is one of the recent topics in the field of haptic devices (HDs), which can alter the apparent mass of the HD. Simulating negative values of virtual mass leads to a decrease in the apparent effective mass, improving transparency but weakening stability. Positive virtual mass rendering increases the apparent mass, reduces transparency, and enhances stability. This paper analyzes the stability of a haptic device while simulating a virtual environment consisting of a mass, spring, and damper in the presence of a constant time delay. The results are closed-form equations that can predict the stability boundary for small and even large values of virtual damping and time delay. These closed-form equations demonstrate that the maximum renderable virtual mass is twice the physical mass of the HD, and the minimum value equals its negative; both occur in the case of zero time delay. Increasing the time delay reduces both the minimum and maximum values of the renderable virtual mass. The study also shows that using virtual mass can improve the maximum value of a renderable virtual spring. The equations show that, in the absence of delay, properly tuning the virtual mass and virtual damping can enlarge the maximum renderable stiffness by up to 5.8 times in theory. In the experiments under time delay, the stiffness increased by a factor of 3.5, compared to the theoretical prediction of 4.1 times. The results further reveal situations where a nonzero minimum stiffness is required for stability. All findings are validated via simulations and experiments on a dedicated test bed.

Mashayekhi, A., Shakeri, M., Khorasani, A., & Verstraten, T. (2025). Effect of Virtual Mass and Time Delay on the Stability of Haptic Rendering. IEEE transactions on haptics.

https://pubmed.ncbi.nlm.nih.gov/41385442/

Policy and practice for enhancing resilience in railway: The promise and pitfalls of IoT adoption

Resilience is crucial for rail transportation in terms of safety, operational efficiency, and transport policy. This study explores the role of resilience in rail transportation, emphasizing the growing integration of Internet of Things (IoT) technologies for asset monitoring, maintenance, and security. Motivated by the potential and risks of IoT adoption, the research aims to evaluate key policies supporting safe and efficient implementation. A hybrid methodology combining qualitative review and a structured survey of railway professionals in a developing country was employed. Using the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), seven decision criteria and three strategic policies were analyzed. The findings identify three critical criteria and one optimal policy strategy for effective IoT integration in the railway. The study highlights both opportunities, like predictive maintenance and real-time monitoring,—and challenges, such as data complexity and safety concerns. The study concludes with policy recommendations to address adoption pitfalls and enhance the resilience of rail systems through structured IoT implementation.

Islam, R., Kabir, G., & Jerry, J. (2026). Policy and practice for enhancing resilience in railway: The promise and pitfalls of IoT adoption. In Ž. Stević, O. Prentkovskis, M. Kostadinović, & A. Danilevičius (Eds.), New horizons of transport and communications 2025 (TransportaCom 2025). Lecture Notes in Intelligent Transportation and Infrastructure. Springer.

https://doi.org/10.1007/978-3-032-14078-4_19