Abstract
Fuel consumption is a serious environmental issue. Hybrid electric vehicles “HEVs” are highly energy efficient and reduce emissions. Parallel Hybrid Electric Vehicles “PHEVs” are currently the most popular architecture type in the HEV market. A PHEV manages energy between its components based on a proposed fuzzy logic control strategy. This strategy has been well researched and proven to be effective. PHEV control strategy has several simultaneous objectives. Reducing fuel consumption is the main goal of a PHEV while maintaining driving performance. Due to the complex nature of PHEVs, optimization algorithms must be used as the applied control strategies do not yield satisfactory global system efficiency. Several constraints were added as boundary conditions in the optimization process to allow optimized sizing of components not only to achieve goals, but also to meet specific vehicle performance requirements. In an acceleration test, the PHEV must reach a certain speed within a given time. In addition, in terms of reducing fuel consumption and emissions, the PHEV must have a zero energy balance, between the start and end of the cycle, at the battery level. PHEV optimization involves several decision variables, including key powertrain components and energy management strategy “EMS” system parameters. These variables greatly affect vehicle performance. A comparative study of optimization algorithms was conducted to determine the characteristics of PHEV to improve fuel economy. The particle swarm optimization “PSO” algorithm and the divided rectangular “DIRECT” algorithm are applied to the studied PHEV to solve optimization problems specific to fuel consumption. Conducted studies prove the effectiveness of the PSO algorithm compared to the DIRECT algorithm in terms of fuel economy improvement. This research has made it possible to determine the optimal size parameters for components that will make our PHEV more efficient and less polluting.
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Schedule a callMore From: Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
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