Advances in Energy Research

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Hybrid artificial bee colony-grey wolf algorithm for multi-objective engine optimization of converted plug-in hybrid electric vehicle

  • Gujarathi, Pritam K. (Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology) ;
  • Shah, Varsha A. (Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology) ;
  • Lokhande, Makarand M. (Department of Electrical Engineering, Visvesvaraya National Institute of Technology)
  • Received : 2019.10.31
  • Accepted : 2020.03.25
  • Published : 2020.03.25
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Abstract

The paper proposes a hybrid approach of artificial bee colony (ABC) and grey wolf optimizer (GWO) algorithm for multi-objective and multidimensional engine optimization of a converted plug-in hybrid electric vehicle. The proposed strategy is used to optimize all emissions along with brake specific fuel consumption (FC) for converted parallel operated diesel plug-in hybrid electric vehicle (PHEV). All emissions particulate matter (PM), nitrogen oxide (NOx), carbon monoxide (CO) and hydrocarbon (HC) are considered as optimization parameters with weighted factors. 70 hp engine data of NOx, PM, HC, CO and FC obtained from Oak Ridge National Laboratory is used for the study. The algorithm is initialized with feasible solutions followed by the employee bee phase of artificial bee colony algorithm to provide exploitation. Onlooker and scout bee phase is replaced by GWO algorithm to provide exploration. MATLAB program is used for simulation. Hybrid ABC-GWO algorithm developed is tested extensively for various values of speeds and torque. The optimization performance and its environmental impact are discussed in detail. The optimization results obtained are verified by real data engine maps. It is also compared with modified ABC and GWO algorithm for checking the effectiveness of proposed algorithm. Hybrid ABC-GWO offers combine benefits of ABC and GWO by reducing computational load and complexity with less computation time providing a balance of exploitation and exploration and passes repeatability towards use for real-time optimization.

Keywords

References

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