Showing 11 results for Genetic Algorithm
A. Khalkhali, S. Samareh Mousavi,
Volume 2, Issue 3 (7-2012)
Abstract
In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimization of the automotive energy absorbing components. In this paper, axial impact crushing behavior of the aluminum foam-filled thin-walled tubes are studied by the finite element method using commercial software ABAQUS. Comparison of the present simulation results with the results of the experiments reported in the previous works indicated the validity of the numerical analyses. A meta-model based on the feed-forward artificial neural networks are then obtained for modeling of both the absorbed energy (E) and the peak crushing force (Fmax) with respect to design variables using those data obtained from the finite element modeling. Using such obtained neural network models, a modified multi-objective GA is used for the Pareto-based optimization of the aluminum foam-filled thinwalled tubes considering three conflicting objectives such as energy absorption, weight of structure, and peak crushing force.
M.h. Shojaeefard, R. Talebitooti, S. Yarmohammadisatri, M. Torabi,
Volume 3, Issue 1 (3-2013)
Abstract
In this paper, a method based on binary-coded genetic algorithm is proposed to explore an optimization method, for obtaining an optimal elliptical tank. This optimization method enhances the rollover threshold of a tank vehicle, especially under partial filling conditions. Minimizing the overturning moment imposed on the vehicle due to c.g. height of the liquid load, lateral acceleration and cargo load shift are properly applied. In the process, the width and height of tanker are assumed as constant parameters. Additionally, considering the constant cross-sectional area, an optimum elliptical tanker of each filling condition is presented to provide more roll stability. Moreover, the magnitudes of lateral and vertical translation of the cargo within the proposed optimal cross section under a constant lateral acceleration field are compared with those of conventional elliptical tank to demonstrate the performance potentials of the optimal shapes. Comparing the vehicle rollover threshold of proposed optimal tank with that of currently used elliptical and circular tank reveals that the optimal tank is improved approximately 18% higher than conventional one.
M. Bostanian, S. M. Barakati, B. Najjari, D. Mohebi Kalhori,
Volume 3, Issue 3 (9-2013)
Abstract
Hybrid Electric Vehicles (HEVs) are driven by two energy convertors, i.e., an Internal Combustion (IC) engine and an electric machine. To make powertrain of HEV as efficient as possible, proper management of the energy elements is essential. This task is completed by HEV controller, which splits power between the IC engine and Electric Motor (EM). In this paper, a Genetic-Fuzzy control strategy is employed to control the powertrain. Genetic-Fuzzy algorithm is a method in which parameters of a Fuzzy Logic Controller (FLC) are tuned by Genetic algorithm. The main target of control is to minimize two competing objectives, consisting of energy cost and emissions, simultaneously. In addition, a new method to consider variations of Battery State of Charge (SOC) in the optimization algorithm is proposed. The controller performances are verified over Urban Dinamometer Driving Cycle (UDDS) and New Europian Driving Cycle (NEDC). The results demonstrate the effectiveness of the proposed method in reducing energy cost and emissions without sacrificing vehicle performance.
A. Hemati, M. Tajdari, A.r. Khoogar,
Volume 3, Issue 4 (12-2013)
Abstract
This paper presents a reduce roll vibration of the full vehicle model with passive suspension systems using vibration absorber to change the dynamic system matrix stat’s eigenvalue. Since using the controller system has been splurged and required to energy consuming, in this research the vehicle body roll vibration has been reduced and supplied vehicle stability using a vibration absorber for the passive suspension system. In this paper a new manner is introduced to reduce body roll angle and body's roll acceleration. The transverse instability in the independent suspension is a main problem, roll angle decreased transverse stability, that it has been reduced using vibration absorber. The optimal value of vibration absorber’s mass, spring and damping coefficient has been determined by using genetic algorithms (GA) to achieve developed roll angle behavior. The main purpose of this article is to reduce vehicle body roll angle that has been acquired using vibration absorber, this manner is better than other ways for roll reduction of vehicle body because it has done without any energy consuming.
B. Mashhadi, H. Mousavi, M. Montazeri,
Volume 5, Issue 1 (3-2015)
Abstract
This paper introduces a technique that relates the coefficients of the Magic Formula tire model to the physical properties of the tire. For this purpose, the tire model is developed by ABAQUS commercial software. The output of this model for the lateral tire force is validated by available tire information and then used to identify the tire force properties. The Magic Formula coefficients are obtained from the validated model by using nonlinear least square curve fitting and Genetic Algorithm techniques. The loading and physical properties of the tire such as the internal pressure, vertical load and tire rim diameter are changed and tire lateral forces for each case are obtained. These values are then used to fit to the magic formula tire model and the coefficients for each case are derived. Results show the existing relationships between the Magic Formula coefficients and the loading and the physical properties of the tire. In order to investigate the effectiveness of the method, different parameter values are selected and the lateral force for each case are obtained by using the estimated coefficients as well as with the simulation and the results of the two methods are shown to be very close. This proves the effectiveness and the accuracy of the proposed method.
M. Heidari,
Volume 6, Issue 3 (9-2016)
Abstract
Excavators are heavy construction equipment consisting of a boom, dipper (or stick), bucket and cab on a rotating platform known as the "house". In this paper the hydraulic shovel excavator is analyzed through the D-H method. The shovel working device with the bucket capacity of 36m3 is optimized. The determination of the objective function, variables and constraints are described in detail. The position of optimized shovel is achieved. Also Bucket trajectory and envelope drawing are designed. These are carried on the analysis and comparison. Optimum design is proved rationality.
Dr Hossein Chehardoli, Dr Ali Ghasemi, Mr Mohammad Daneshyian,
Volume 10, Issue 4 (12-2020)
Abstract
A new safe optimal consensus procedure is presented to guarantee the asymptotic and string stability as well as crash avoidance of large-scale non-identical traffic flow. Since time delay is an inherent characteristic of physical actuators and sensors, measurement delay and lags are involved in the upper level control structure. A third-order linear model is employed to define the 1-D motion of each automated vehicle (AV) and the constant time headway plan is employed to regulate the inter-AV distance. It is assumed that the network structure is decentralized look ahead (DLA) and each AV has access to relative position and velocity regarding with the front AV. A linear control law is introduced for each AV and by performing the stability analysis in frequency domain, the necessary conditions guaranteeing string stability and crash avoidance for large-scale traffic flow are derived. Afterwards, to calculate the optimal control parameters guaranteeing the best performance, an objective function combining all mentioned conditions as well as maximum overshoot, settling time and stability margin is introduced. The genetic algorithm (GA) technique is employed to optimize the presented objective function and obtain the optimal control parameters. Various numerical results are proposed to demonstrate the efficiency of this method.
Seyyed Hamed Tabatabaei, Saeed Moradi Haghighi, Amirhossein Kiani, Kasra Ghasemian,
Volume 11, Issue 2 (6-2021)
Abstract
In this paper, an optimized insulator for sound packaging of the vehicle dash panel is proposed. The combination of the micro perforated panel and porous layers has been selected to insulate the dash panel of a vehicle. The main advantages of the mentioned combination are light weight and various tunable parameters in comparison with other insulators. These provide significant flexibility to achieve an optimal performance for the noise attenuation of the vehicle cabin. Therefore, the parameters of the selected sound package have been optimized in order to achieve suitable sound absorption in a selected frequency range. Furthermore, the Genetic Algorithm (GA) is used to optimize the parameters. It can achieve more reliable and more accurate outcomes compared to the conventional method. Full vehicle SEA (Statistical Energy Analysis) simulations are used to evaluate the optimized sound package. The results indicate that the optimized concept has maximum sound absorption capability. Consequently, the proposed sound package improves the vehicle's engine noise reduction by 5 dB in comparison with un-optimized sample in mid and high frequency ranges.
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.
Dr Hossein Chehardoli,
Volume 13, Issue 3 (9-2023)
Abstract
In this article, the optimal robust H2 / H∞ control of self-driving car platoons (SDCPs) under external disturbance is investigated. By considering the engine dynamics and the effects of external disturbance, a linear dynamical model is presented to define the motion of each self-driving car (SDC). Each following SDC is in direct communication with the leader. By utilizing the relative position of following SDCs and the leader, the error dynamics of each SDC is calculated. The particle swarm optimization (PSO) method is utilized to find the optimal control gains. To this aim, a cost function which is a linear combination of H2 and H∞ norms of the transfer function between disturbance and target variables is constructed. By employing the PSO method, the cost function will be minimized and therefore, the robustness of the controller against external disturbance is guaranteed. It will be proved that under the presented robust control method, the negative effects of disturbance on system performance will significantly reduce. Therefore, the SDCP is internally stable and subsequently, each SDC tracks the motion of the leader. In order to validate the proposed method, simulation examples will be presented and analyzed.
