Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulated annealing genetic algorithm

Authors:
Sun Hui
Affiliations:
Jiangsu Xuzhou Construction Machinery Research Institute, Jiangsu, China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

Citing 2
Cited 1

Some Guidelines for Genetic Algorithms with Penalty Functions

Proceedings of the 3rd International Conference on Genetic Algorithms
Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system

Computers and Operations Research

Energy-saving analysis in the hydraulic system of loader working device

International Journal of Computer Applications in Technology

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Abstract

Along with the shortage of energy and the increasingly serious pollution of environment in cities, automobile industries all over the world are exploring and developing energy saving and clean automobile. Hydraulic hybrid vehicle has better potential in medium-size and large-size passenger vehicles than its electric counterparts. The key components' sizes have remarkable influence on the vehicle performance and fuel economy, and an optimization process is needed to find the best design parameters for maximum fuel economy while satisfying the vehicle performance constraints. Multi-Objective optimization method based on adaptive simulated annealing genetic algorithm (ASAGA) is proposed to optimize the key components in HHV. In the objective function of the optimization, all the weighting factors can be set with different values according to different requirements. The optimal results show that the proposed method effectively distinguishes the key components' optimal parameters' position of HHV, enhances the performance and fuel consumption.