Showing 9 results for Morteza
Dr Mohammad H. Shojaeefard, Dr Mollajafari Morteza, Mr Seyed Hamid R. Mousavitabar,
Volume 14, Issue 1 (3-2024)
Abstract
Fleet routing is one of the basic solutions to meet the good demand of customers in which decisions are made based on the limitations of product supply warehouses, time limits for sending orders, variety of products and the capacity of fleet vehicles. Although valuable efforts have been made so far in modeling and solving the fleet routing problem, there is still a need for new solutions to further make the model more realistic. In most research, the goal is to reach the shortest distance to supply the desired products. Time window restrictions are also applied with the aim of reducing product delivery time. In this paper, issues such as customers' need for multiple products, limited warehouses in terms of the type and number of products that can be offered, and also the uncertainty about handling a customer's request or the possibility of canceling a customer order are considered. We used the random model method to deal with the uncertainty of customer demand. A fuzzy clustering method was also proposed for customer grouping. The final model is an integer linear optimization model that is solved with the powerful tools of Mosek and Yalmip. Based on the simulation results, it was identified to what extent possible and accidental changes in customer behavior could affect shipping costs. It was also determined based on these results that the effective parameters in product distribution, such as vehicle speed, can be effective in the face of uncertainty in customer demand.
Mr. Pooriya Sanaie, Dr. Morteza Mollajafari,
Volume 15, Issue 1 (3-2025)
Abstract
Electric Power Steering (EPS) systems are increasingly being integrated into modern vehicles, offering enhanced fuel efficiency and improved maneuverability. However, these systems are often subject to noise and disturbances, which can significantly impact steering precision and driver comfort. Addressing these challenges requires the implementation of robust control strategies capable of mitigating noise and disturbances in EPS systems. This paper explores advanced methods for achieving robust control in Electric Power Steering systems by reducing noise interference and countering external disturbances. Key techniques involve adaptive control algorithms and robust filtering mechanisms that maintain system stability and performance even under variable operating conditions. Experimental results demonstrate that these robust control approaches effectively minimize noise levels and disturbance impacts, leading to smoother steering response and greater reliability. This study underscores the critical role of robust control in enhancing the functionality and safety of Electric Power Steering systems while highlighting the intricate dynamics between noise, disturbances, and control system robustness in automotive applications.
Prof Morteza Montazeri, Mr Mohammad Amin Zakizadeh, Mr Afshin Mostashiri,
Volume 15, Issue 4 (12-2025)
Abstract
The rising demand for sustainable transportation has intensified research on Fuel Cell Hybrid Electric Vehicles (FCHEVs). Integrating fuel cells with lithium-ion batteries provides a pathway to enhance energy efficiency and driving performance, but ensuring the durability of both components under real operating conditions remains a critical challenge. This work proposes an integrated framework to improve FCHEV performance and lifetime through combined modeling, degradation analysis, and optimized energy management. Dynamic vehicle simulations were conducted using the ADVISOR platform under both the Urban Dynamometer Driving Schedule (UDDS) and a real-world cycle based on Tehran traffic data. Degradation models were implemented to capture platinum dissolution in the Proton Exchange Membrane Fuel Cell (PEMFC) and capacity loss in the lithium-ion battery, incorporating the effects of state of charge, temperature, and current rate. An energy management strategy was developed using a Fuzzy Logic Controller (FLC) for fuel cell–battery power distribution, which was further refined with a Genetic Algorithm (GA). The optimization objectives included reducing hydrogen consumption and extending component lifetimes. The GA-optimized FLC extended PEMFC lifetime by 50.6% Tehran and 12.9% UDDS and reduced battery capacity fade by 10% and 4.9%, respectively. While direct hydrogen consumption increased in Tehran due to more aggressive regenerative-energy routing to the battery, the Equivalent Fuel Consumption (EFC) decreased from 971.32 → 937.21 g/100 km (Tehran) and 794.41 → 782.24 g/100 km (UDDS), reflecting a net efficiency gain once SOC restoration is accounted for.
Behzad Heidarpour, Abbas Rahi, Morteza Shahravi,
Volume 15, Issue 4 (12-2025)
Abstract
This study investigates the dynamic response of a lithium‑ion battery pack subjected to environmental vibrations. Considering the widespread use of such packs in electric vehicles and energy storage systems, and the adverse effects of vibrations on their performance and safety, both numerical and experimental approaches are employed. In the numerical simulation phase, a detailed three-dimensional model of the battery pack, including all components and joints, is developed in Abaqus, and a full modal analysis is performed to extract the natural frequencies and mode shapes of the system. In the experimental phase, modal testing is conducted using an impact hammer and an accelerometer on a physical battery-pack sample under free‑free boundary conditions to validate the simulation results. A systematic comparison between the two approaches demonstrates a good agreement, with the maximum deviation in the primary natural frequencies being less than 10%. This level of consistency confirms the accuracy and reliability of the proposed model. The developed model can serve as an effective tool during the early design stages for mechanical optimization, dynamic behavior prediction, and mitigation of vibration‑induced failures in battery packs. The results of this study mark an important step toward improving the reliability and safety of battery packs in operational environments.
