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Showing 2 results for Pakdel Bonab

Gh.h Payeganeh, M. Esfahanian, S. Pakdel Bonab,
Volume 4, Issue 2 (6-2014)
Abstract

In the present paper, the idea of braking energy regeneration and reusing that energy during acceleration for a refuse truck is comprehended. According to their driving cycle, the refuse trucks have a good potential for braking energy regeneration. On the other hand, hydraulic hybrid is a powertrain with high power density which is appropriate for energy regeneration. In the primary stage of this issue, the hydraulic hybrid propulsion system is designed with intention of regenerating the maximum possible kinetic energy during the refuse truck braking mode. At this stage, a non-fuzzy rule-based control strategy is applied to manage the energy flow in the hybrid powertrain. After that, the powertrain of the Axor 1828 truck and the elements of the hydraulic powertrain are modeled in MATLAB/Simulink. The modeling is performed considering the efficiencies of the powertrain elements. In the last part of the paper, a fuzzy control strategy is designed and modeled to improve the fuel consumption of the truck with hybrid powertrain. In order to see the usefulness of the designed hybrid powertrain, several simulations are organized on the vehicle model in Simulink. The driving cycle for refuse truck in Tehran is used for performing the simulations. The results state indicated that using the hydraulic hybrid powertrain decreased the fuel consumption of the refuse truck by 7 percent. In addition, this amount of reduction was improved by implementing the fuzzy control strategy. The decrease in fuel consumption was due to the regenerating of the braking energy up to 50 percent.
Mr. Sohrab Pakdel Bonab, Dr. Afshin Kazerooni, Dr. Gholamhassan Payganeh, Dr. Mohsen Esfahanian,
Volume 10, Issue 1 (3-2020)
Abstract

Driving cycle is used to assess fuel consumption, pollutant emissions and performance of the vehicle. The aim of this paper is to extract the driving cycle for refuse collection truck and estimate its braking energy. For this purpose, after selecting the target truck and geographic area, the equipment needed to measure the required variables were prepared and mounted on the truck. Then, the actual data were collected from the performance of the target Truck while performing its mission. Since the amount of braking energy depends on the speed, truck mass and road grade, the speed of the vehicle is measured simultaneously with the truck mass and road grade. The collected data are then processed and subdivided into micro-trips. The micro-trips are clustered according to the number of state spaces using the K-Means algorithm. Next, the representative micro trips are selected from within the clusters and the final driving cycle is generated. The representative driving cycle shows that the truck speed is zero at 47% of the working time. Finally, the amount of braking power and accumulative braking energy in the driving cycle is calculated.

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