Showing 4 results for Mollajafari
Morteza Mollajafari, Farzad Kouhyar,
Volume 12, Issue 1 (3-2022)
Abstract
Recently, number of Hybrid Electric Vehicles (HEV) is on the rise due to concerns over environmental issues. By combining fuel and electricity as two sources of power, this type of vehicle is capable of bettering fuel economy and lowering emission. In this work, fuel and electrical energy consumption of a parallel hybrid electric vehicle are investigated through TEH-CAR urban drive cycle. For this purpose, a forward looking model is developed in AVL CRUISE M. To ensure adequacy of the model and take engine gas path components’ dynamic interaction into account, a crank based model with individual cylinders is utilized. Furthermore, a throttle filter is presented to slow down engine’s response and also, allow the electric motor to have the larger share of delivering power in transients. Finally, genetic algorithm is used to find optimal values for throttle filter parameter and electric motor load ratio, in order to have minimal overall fuel and electrical energy consumption. The optimization results show 1.2% of fuel and 20.2% of total energy consumption reduction in comparison with conventional torque assist.
Dr Morteza Mollajafari, Mr Alireza Rajabi Ranjbar, Mr Shayegan Shahed Haghighi,
Volume 12, Issue 3 (9-2022)
Abstract
The development and adoption of electric vehicles (EVs) appears to be an excellent way to mitigate environmental problems such as climate change and global warming exacerbated by the transportation sector. However, it faces numerous challenges, such as optimal locations for EV charging stations and underdeveloped EVCS infrastructure, among the major obstacles. The present study is based on the location planning of charging stations in real cases of central and densely populated districts of Tehran, the capital of Iran. In order to achieve this goal, this paper attempts to validate the results of a previous study in another country. Secondly, by employing preceding principals in accordance with relevant information collected from the car park and petrol stations in the regions of study, a five-integer linear program is proposed based on a weighted set coverage model considering EV users' convenience, daily life conditions, and investment costs, and finally optimally solved by genetic algorithm under various distribution conditions; normal, uniform, Poisson and exponential, to specify the location and number of EV charging stations in such a way that EV drivers can have access to chargers, within an acceptable driving range.
Morteza Mollajafari, Javad Marzbanrad, Pooriya Sanaei,
Volume 12, Issue 4 (12-2022)
Abstract
The braking system has always been considered one of the most significant vehicle subsystems since it plays a key role in safety issues. To design such a complex system, modeling can be a helpful tool for designers to save time and costs. In this paper, the hydraulic braking system of a B-Class vehicle was modeled by simulating the relationship between brake components such as pedals, boosters, main cylinders, and wheel cylinders, with the vehicle dynamics by using the existing models of the tire and their dynamic relationships. The performed modeling was compared with the results of a concerning vehicle's direct movement. The results of this comparison showed that our modeling is very close to the experimental data. The braking distance parameter was selected to examine the effects of each braking component on the vehicle dynamics. The results of investigating the effect of different parameters of the braking system on the dynamic behavior of the vehicle indicated that the main cylinder diameter, the diameter of the front and rear wheels’ brake cylinders, the effective diameter of the front disk, and the diameter of the rear drum are the most effective design parameters in vehicle's braking system and optimal results are obtained by applying changes to these parameters.
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.
