The Optimization of Control Parameters for Hybrid Electric Vehicles based on Genetic Algorithm State Key Laboratory of Automotive Simulation

Format:

Conference/Event

Author/Creator:

Jun, Jun, author.

Contributor:

Han, Biao

Wang, Pengyu

Wang, Qingnian

Conference Name:

SAE 2014 World Congress & Exhibition (2014-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2014

Summary:

AbstractThe traditional vehicle design methods of hybrid electric vehicles are based on the rule-based control strategy, which often adopt the trial and error methods and the model-based numerical optimization methods. But these methods require a large number of repeated tests and a longer-term development cycle. In this paper, approximately the global optimization algorithm was used in control parameters designing through rational design of the penalty weights of objective function. But the optimized parameters apply only to vehicles that operating in the special drive cycle to get better fuel economy. Therefore, a drive cycle recognition algorithm was proposed to identify types of drive cycles in real-time, then an off-line genetic algorithm was adopted to acquire the optimization of control parameters under the various drive cycles, through drive cycle recognition results to choose the best control parameters. The simulation results demonstrate that adaptive energy strategy can improves the fuel-economy of hybrid electric vehicle and guarantees the vehicle power performance, driving performance

Notes:

Vendor supplied data

Publisher Number:

2014-01-1894

Access Restriction:

Restricted for use by site license