Showing 4 results for Hev
Morteza Montazeri, Masoud Khasheinejad, Dr. Zeinab Pourbafarani,
Volume 9, Issue 2 (6-2019)
Abstract
Hardware implementation of the Plug-in hybrid electric vehicles (PHEVs) control strategy is an important stage of the development of the vehicle electric control unit (ECU). This paper introduces Model-Based Design (MBD) approach for implementation of PHEV energy management. Based on this approach, implementation of the control algorithm on an electronic hardware is performed using automatic code generation. The advantages of the MBD in comparison with the traditional methods are the capability of eliminating the manual coding complexities as well as compiling problems and reducing the test duration. In this study, hardware implementation of a PHEV rule-based control strategy is accomplished using MBD method. Also, in order to increase the accuracy of the results of the implementation, the data packing method is used. In this method, by controlling the primer and end data of the data packet transferred between the electronic board and the computer system, the noisy data is prevented from entering. In addition, to verify the performance of the implemented control strategy, hardware-in-the-loop (HIL) simulation is used with the two frequency rates. The results show the effectiveness of the proposed approach in correct and rapid implantation procedure.
Mr Peyman Bayat, Dr. Hossein Afrakhte,
Volume 9, Issue 3 (9-2019)
Abstract
As an effective means of displacing fossil fuel consumption and reducing greenhouse gas emissions, plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs) have attracted more and more attentions. From the power grid perspective, PHEVs and PEVs equipped with batteries can also be used as energy storage facilities, due to the fact that, these vehicles are parked most of the time. Since, the temperature has a strong influence on the battery life-time and also the inherent characteristics of PHEV/PEV energy storage systems limit their use as appropriate resources for energy tuning, this paper, at first, presents a detailed model for energy storage systems of PEVs considering the cooling system and set temperature, and then, it proposes a reliable energy management method for scheduling of PEVs in the multi-microgrid (MMG) systems for both faulted and normal operations using parametric multi-objective function. The simulation results indicate that, considering proper energy management of energy storage systems of PEVs has significant influence on energy scheduling of MMG systems. For this investigation, all data analysis and simulations were done and implemented in MATLAB/Simulink environment.
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.
Bentolhoda Eivani, Hossein Moeinkhah, Saeed Farahat,
Volume 14, Issue 4 (12-2024)
Abstract
This paper presents an efficient dynamic programming method in order to examine the problem of optimal power management of hybrid electric vehicle (HEV) powertrains and compares its performance with a rule-based method. Since dynamic programming is a trajectory based optimization algorithm and provides a globally optimal solution, it can be used as a benchmark for assessment of other control strategies. However, a major limitation of this method is its extreme computational load which is known as the curse of dimensionality. The computation time and the memory requirements increase exponentially with the increase of states and inputs. In this paper, a novel approach is used to decrease the total computation load and shows how this improvement can provide more accurate results.
