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Abstract
The paper presents the application of grey wolf algorithm for multidimensional engine optimization of converted parallel operated diesel plug-in hybrid electric vehicle to optimize specific fuel consumption (FC) and emissions. All emissions hydrocarbon (HC), carbon monoxide (CO), nitrogen oxide (NOx) and particulate matter (PM) are considered as optimization parameters. Offline engine maps of FC, HC, CO, NOx and PM are generated for 70 hp engine by data obtained from Oak Ridge National Laboratory for study. MATLAB program is used for simulation. A grey wolf coding is developed and tested extensively for various values of speed and torque. The optimization results obtained are verified by available engine maps. The optimization performance and its environmental impact are discussed in detail. It is observed that grey wolf optimizer (GWO) gives the global minimum value with slight deviation, although least computation time and simplicity makes this algorithm a potential candidate for real-time implementation.
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Schedule a callMore From: Transportation Research Part D: Transport and Environment
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