Real-time Energy Management of PHEVs with Supercapacitor Using Nonlinear Model Predictive Control

Abstract

The earth is facing the global warming phenomenon these days, and one of its main contributors is greenhouse gas emission. As transportation produces an enormous portion of greenhouse gasses and also due to the increasing price of oil, automotive companies are now motivated more than ever to manufacture more hybrid vehicles compared to conventional vehicles. Although hybrid vehicles decrease the fuel consumption and greenhouse gas emissions, the cost of ownership and short lifespan of batteries have always been a drawback for them. Comparing the merits and demerits of batteries and Supercapacitors (SC) convinced researchers to use a combination of both to utilize vehicles with, as none of them could replace the other completely. An energy management strategy is crucial for maximizing benefits from utilizing vehicles with a hybrid energy storage system. This study includes modelling an SC module and developing Nonlinear Model Predictive Control (NMPC) strategies for the Toyota Prius Plug-in Hybrid Electric Vehicle (PHEV) with SC. Enhancements in vehicles processing units have absorbed attentions into more complex energy management strategies like MPC. Model-in-the-Loop (MIL) simulations and Hardware-in-the-Loop (HIL) tests investigate the performance of the proposed strategies. HIL tests results suggest the prediction horizon lengths for that the proposed controllers can be real-time implementable. Moreover, the MIL simulations results investigate the performances of fuel consumption and lifespan of the battery. Repeating the MIL simulations for different scenarios guarantees the performance enhancement regardless of driver's behaviour. Using Nonlinear Model Predictive Controller (NMPC) as Energy Management Strategy (EMS) in this study shows improvements in fuel consumption and lifespan of the battery up to 7.4% and 62%, respectively. While hybridizing Energy Storage System (ESS) with Supercapacitor (SC) can achieve up to 47% reduction in the battery load.