Abstract
The control strategy is a major unit in hybrid electric vehicles (HEVs). In order to provide suitable control parameters for reducing fuel consumptions and engine emissions while maintaining vehicle performance requirements, the genetic algorithm (GA) with small population size is applied to search for feasible control parameters in parallel HEVs. The electric assist control strategy (EACS) is used as the fundamental control strategy of parallel HEVs. The dynamic performance requirements stipulated in the Partnership for a New Generation of Vehicles (PNGV) is considered to maintain the vehicle performance. The known ADvanced VehIcle SimulatOR (ADVISOR) is used to simulate a specific parallel HEV with urban dynamometer driving schedule (UDDS). Five population sets with size 5, 10, 15, 20, and 25 are used in the GA. The experimental results show that the GA with population size of 25 is the best for selecting feasible control parameters in parallel HEVs.
Highlights
The development of environmental friendly vehicles with low to no fuel consumption is increasingly important in the automotive industry
In order to overcome this drawback, we propose a genetic algorithm (GA) with small population size when searching for feasible parameters in parallel hybrid electric vehicles (HEVs) control strategy
The population size of 25 is the best among the five small population sizes that have been investigated for searching feasible parameters of the control strategy for reducing the fuel consumption (FC) and engine emissions
Summary
The development of environmental friendly vehicles with low to no fuel consumption is increasingly important in the automotive industry. Hybrid electric vehicles (HEVs) have received the most attentions. HEVs produce few carbon emissions, and for this reason, they are widely accepted by environmentalists. It is expected that HEVs will become the model of future vehicles due to global energy crises and global warming. In a typical HEV, there are two energy converters—internal combustion engine (ICE) and electric motor (EM), which complicate energy flows as the conventional vehicles have only ICE. An appropriately designed HEV control strategy that incorporates both the ICE and EM is essential. Several control strategies have been proposed and they have all been proven to improve fuel consumptions and lower engine emissions [1,2,3,4,5,6]
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