Study on Optimization Matching Algorithm for Automotive Powertrain

2014 ◽  
Vol 635-637 ◽  
pp. 1890-1894
Author(s):  
Feng Ping Cao ◽  
Li Fa Zhou ◽  
Yong Di Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Fuel Economy ◽  
Dynamic Performance ◽  
Evaluation Index ◽  
Reduce Fuel Consumption ◽  
Matching Algorithm ◽  
Vehicle Simulation ◽  
Before And After ◽  
Automotive Powertrain

In order to reduce fuel consumption and ensure dynamic performance of the car, an automotive powertrain optimization algorithm was presented in the paper. Firstly, the evaluation index of automobile dynamic performance and fuel economy were introduced. Then, the objective function was built, and the transmission and main reducer transmission ratios were designed as variables, and parameters of the vehicle transmission system were optimized by using the genetic algorithm. Finally, a vehicle simulation model by SimulationX software was established, and the power and economy performance before and after optimization were compared and analyzed.

Optimization of Automotive Powertrain Based on Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 2426-2430
Author(s):  
Feng Ping Cao
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Fuel Economy ◽  
Simulation Experiment ◽  
Target Function ◽  
Power Performance ◽  
Transmission Parameters ◽  
Transmission Design ◽  
Dynamic Target ◽  
Automotive Powertrain

In order to improve the performance of the cars, an optimization algorithm of transmission parameters that take into account automotive dynamic and fuel economy based on genetic algorithm was presented in the paper. Firstly, the origin underway accelerate time was chose as the dynamic target function, and the 100 kilometers fuel consumption of 15-cycle driving was used to evaluate fuel economy, and the two objectives functions were putted into a comprehensive objective function. Then, the constraints were established using the requirement of automobile power performance and transmission design, and the genetic algorithm was adopted to do the optimization. At last, a simulation experiment was design to confirm the validity and effectiveness of the proposed method.

Global Optimal Design and Dynamic Validation of an Independent Double Wishbone Air Suspension Using Genetic Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 374-378
Author(s):  
Jing Zhao ◽  
Pak Kin Wong ◽  
Tao Xu ◽  
Rui Deng ◽  
Cai Yang Wei ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Parametric Design ◽  
Dynamic Performance ◽  
Vehicle Performance ◽  
Air Suspension ◽  
Before And After ◽  
Wheel Alignment ◽  
Global Optimal ◽  
Suspension Structure

In view of the drawbacks of the traditional optimal methods in the suspension structure optimization, this paper elaborates a genetic algorithm (GA) based global optimal design so as to improve the vehicle performance. Firstly, an independent double wishbone air suspension (IDWAS) is constructed. After defining the linkage relation of the guide mechanism of the IDWAS, the model is verified followed with the parametric design. Furthermore, in consideration of the prescribed targets of the vehicle kinematics, the wheel alignment parameters (WAPs) are selected as the objectives of the optimal design of the vehicle kinematics. Apart from the kinematic analysis of the IDWAS, dynamic analysis before and after optimization as well as the traditional independent double wishbone suspension (TIDWS) are also conducted. Numerical results show that the changes of the WAPs are within a certain range and the guide mechanism follows the prescribed constraints. Simulation results show that the IDWAS is superior to the TIDWS, while the optimized IDWAS has a slight improvement as compared to the original IDWAS in dynamic performance of the suspension.

Engine Parameters Optimization Design for Mine Dump Truck Based on the Genetic Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 2143-2146
Author(s):  
Lv Chang
Keyword(s):  
Genetic Algorithm ◽  
Fuel Economy ◽  
Optimization Design ◽  
Dynamic Performance ◽  
Simulation Software ◽  
Parameters Optimization ◽  
Function Optimization ◽  
Dump Truck ◽  
Dump Trucks ◽  
Mine Dump

The interface is constructed between the mine dump trucks performance simulation software and the genetic algorithm, aiming at the very complex relationship between the mine dump truck engine and its dynamic performance and fuel economy. The optimization of the objective function, optimization variables and constraints are designed. Genetic algorithm is used to design type 3303B mine dump trucks. The optimization of the design results are compared and analyzed on before and after and the results show that the fuel economy of the optimization of genetic algorithm is better than the original car, fuel consumption reduced 0.51 every km.

Optimization of Hybrid Electric Bus Control Strategy with Hybrid Optimization Algorithm

2013 ◽  
Vol 341-342 ◽  
pp. 924-930
Author(s):  
Jian Ping Gao ◽  
Zhen Nan Liu ◽  
Zhi Jun Guo ◽  
Yue Hui Wei
Keyword(s):  
Optimization Algorithm ◽  
Fuel Economy ◽  
Control Strategy ◽  
Dynamic Performance ◽  
Hybrid Optimization ◽  
Hybrid Electric Bus ◽  
Hybrid Optimization Algorithm ◽  
Electric Bus ◽  
Hybrid Electric ◽  
Strategy Parameters

control strategy is one of the most decisive techniques in Hybrid Electric Bus (HEB) and directly influences the dynamic performance and fuel economy. For achieving the best fuel economy and keeping the battery for a long time, First, power analytic control strategy was built; then, the hybrid optimization algorithm (HOA) based on Multi-island genetic Algorithm (MIGA) and NLPQL was built by ISIGHT software. HOA is adopted in control strategy parameters of HEB optimization. The results show that the best result can be obtained in few iterative times by HOA, the calculation time was reduce by 12 hours, the fuel economy was improved by 12% and find the rules between control strategy parameters and fuel economy the balance of the battery state of charge (SOC).

NSGA-II multi-objectives optimization algorithm for energy management control of hybrid electric vehicle

2018 ◽  
Vol 233 (4) ◽  
pp. 1023-1034 ◽  
Author(s):  
Tao Deng ◽  
Chunsong Lin ◽  
Junlin Luo ◽  
Bingqu Chen
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Management Control ◽  
Evaluation Index ◽  
Nsga Ii ◽  
Hybrid Electric ◽  
The Impact

The currently existing energy management control optimization for hybrid electric vehicle (HEV) mainly focuses on fuel economy. Apart from this, there has been some consideration of the impact of emissions, but almost no attention has been paid to drivability performance. Therefore, from the point of view of multi-objectives optimization, the influences of fuel economy, emission and drivability performance on the energy management are comprehensively considered for a parallel HEV. The energy management control parameters and driveline parameters are selected to be optimized parameters. Then, the NSGA-II (Fast Non-dominated Sorting Genetic Algorithm-II) algorithm is proposed to solve the multi-objectives optimization problem. Furthermore, the multi-objectives optimization method for HEV energy management control is established and comparatively simulated with the parallel electric assist control strategy. The results show that the evaluation index of drivability decreases by 27.12% from the maximum and the average enhancement effect of optimization falls by 20.84%. The evaluation index of fuel economy declines by 22.30% from the maximum and the average index drops by 20.26%. The comprehensive index of emission performance descends by 11.33% from the maximum. The proposed multi-objectives optimization algorithm has good convergence and distribution, and obtains more Pareto optimal solution sets, which can provide more selectivity in building HEV energy management control strategies.

scholarly journals A Component-Sizing Methodology for a Hybrid Electric Vehicle Using an Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3147
Author(s):  
Kiyoung Kim ◽  
Namdoo Kim ◽  
Jongryeol Jeong ◽  
Sunghwan Min ◽  
Horim Yang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicle ◽  
Optimization Algorithm ◽  
Hybrid Electric Vehicle ◽  
New Technologies ◽  
New Technology ◽  
Dynamic Performance ◽  
Minimum Principle ◽  
Component Sizing ◽  
Hybrid Electric

Many leading companies in the automotive industry have been putting tremendous effort into developing new powertrains and technologies to make their products more energy efficient. Evaluating the fuel economy benefit of a new technology in specific powertrain systems is straightforward; and, in an early concept phase, obtaining a projection of energy efficiency benefits from new technologies is extremely useful. However, when carmakers consider new technology or powertrain configurations, they must deal with a trade-off problem involving factors such as energy efficiency and performance, because of the complexities of sizing a vehicle’s powertrain components, which directly affect its energy efficiency and dynamic performance. As powertrains of modern vehicles become more complicated, even more effort is required to design the size of each component. This study presents a component-sizing process based on the forward-looking vehicle simulator “Autonomie” and the optimization algorithm “POUNDERS”; the supervisory control strategy based on Pontryagin’s Minimum Principle (PMP) assures sufficient computational system efficiency. We tested the process by applying it to a single power-split hybrid electric vehicle to determine optimal values of gear ratios and each component size, where we defined the optimization problem as minimizing energy consumption when the vehicle’s dynamic performance is given as a performance constraint. The suggested sizing process will be helpful in determining optimal component sizes for vehicle powertrain to maximize fuel efficiency while dynamic performance is satisfied. Indeed, this process does not require the engineer’s intuition or rules based on heuristics required in the rule-based process.

scholarly journals Vision-Based Structural FE Model Updating Using Genetic Algorithm

2021 ◽  
Vol 11 (4) ◽  
pp. 1622
Author(s):  
Gun Park ◽  
Ki-Nam Hong ◽  
Hyungchul Yoon
Keyword(s):  
Genetic Algorithm ◽  
Model Updating ◽  
Structural Parameters ◽  
Fe Model ◽  
Structural Members ◽  
Stiffness Reduction ◽  
Before And After ◽  
Scale Test ◽  
Fe Model Updating ◽  
Story Building

Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating.

scholarly journals Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Simplex Algorithm ◽  
Local Optimum ◽  
Hybrid Optimization Algorithm ◽  
Multimodal Function ◽  
Remote Laboratories ◽  
Remote Lab ◽  
Timetable Problem

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

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