Optimization & validation of Intelligent Energy Management System for pseudo dynamic predictive regulation of plug-in hybrid electric vehicle as donor clients

Abstract

In developing countries, policies for discarding the existing Internal Combustion (IC) Engine vehicles for faster adoption of Electric Vehicles’ not only creates burden on the existing power grid but also is impractical. The conversion of Conventional IC Engine based Online Taxis or public transport vehicles into Plug-in Hybrid Electric Vehicles donor clients, to participate in Vehicle to Grid & Vehicle to Vehicle power transfer model, is the solution. These vehicles would not only have emissions within compliance standards but would also reduces the load on the power grid meanwhile making an income through power transfer. The Intelligent Energy Management System (IEMS) developed makes use of a Non Dominated Sorting Genetic Algorithm (NSGA-II) based Pseudo dynamic predictive regulation approach on the powertrain to optimize the emissions, fuel cost and traction battery SoC. If the vehicle intends to participate in power transfer, the IEMS would predetermine the amount of SoC that would be used for an upcoming journey using Global Positioning System(GPS) data interconnected with a server unit which enables the IEMS to optimize the operating conditions of the vehicle. The modelled IEMS performance is tested for a given driving cycle with varying traffic levels on a virtual simulation environment using the IPG CarMaker software. A prototype with a 150 cc, 7.5 kW IC engine integrated to a 3 kW BLDC traction motor is developed and the response to the predictive model is evaluated and found to provide 27.66%, 13.73% and 7.72% equivalent energy to micro grid for low, medium and high criticality conditions for the user.