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IJSTR >> Volume 8 - Issue 10, October 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

NSGA-II Based Optimization Approach For Intelligent Load Sharing In Plug-In Hybrid Vehicles

[Full Text]



Parag Jose Chacko, Meikandasivam Sachidanandam



PHEV; IEMS; NSGA-II; SoC; DoD; Pareto Front.



Plug –in Electric Vehicles (PHEV) ensures the reduction in emission levels as compared to IC engine based vehicles. The minimization of emission levels and fuel costs would lead to over utilization of the Traction battery in a PHEV. This work focuses on development of an Intelligent Energy Management System (IEMS) which optimizes the emission level and fuel costs considering the criticality of the future journey of the PHEV user. The criticality of the journey for the PHEV user regulates the decision made by the Intelligent Energy Management System. The system developed is a Parallel Hybrid system with a BLDC motor assisting the IC engine in propulsion. To validate the IEMS operation a detailed design and development of a Parallel Hybrid Vehicle considering a 150cc Petrol engine as the main propulsion source and a 3kW BLDC motor as the support propulsion source is done. The design and testing of the response of the vehicle to drive cycle is performed using Matlab/Simulink environment. The impact of the designed model on the 14 –Degrees of Freedom is performed to validate the developed model. The prototype is then developed and tested with the IEMS controller. The decision on load sharing is performed using a Non-Dominated Sorting Genetic Algorithm –II (NSGA-II) approach. The IEMS optimizes the Emission Level and Fuel costs depending on the next journey distance, altitude and the PHEV user criticality and decides the permissible Depth of Discharge (DoD ) level for the traction battery.



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