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

摘要

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