Real-time Energy Management of a Battery Electric Vehicle Hybridized with Supercapacitor
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The increased interest in electric vehicles (EVs) in the recent years has intrigued numerous research, on improving efficiency and reducing ownership costs of these vehicles. As the battery in EVs is the sole energy provider, it is exposed to degradation due to high peaks and rapid fluctuations in the power demanded by the driver. Therefore, integrating a supercapacitor (SC) pack into the energy storage system of these vehicles has been proposed as a potential solution; maintaining the battery as the main energy source of the vehicle while using the SC when exposed to high power peaks and power fluctuations. However, just like any other hybrid system, the maximum benefit of this integration can only be exploited when applying a proper energy management controller. Various energy management controllers have been used for these systems through the literature; ranging from simple rule based control strategies to more complex optimal control approaches. In this thesis, nonlinear model predictive control (NMPC) strategies have been designed as energy management controllers for battery-SC hybrid energy storage systems (HESSs) in a Toyota Rav4EV. Although traditionally used in applications dealing with slow dynamics like process control, with the rapid improvement in electric control units (ECUs) in the recent years, NMPCs have received a great deal of attention in areas with systems of faster dynamics, including the automotive sector. However, the question still needs to be addressed whether NMPC can demonstrate performance improvement over other state-of-the-art controllers, while maintaining computational efficiency necessary for automotive real-time applications. This investigation has been conducted through Model-in-the-Loop (MIL) simulating and Hardware-in-the-Loop (HIL) testing on the NMPC energy management strategies designed in this work. The NMPC uses a control-oriented model of the system, some form of the future trip prediction, and an optimization solver to find the optimal power split between the battery and SC at each time step during the trip. The designed NMPC has been compared to other state-of-the-art controllers in the literature. A number of methods for future trip prediction have also been studied through the thesis and the NMPC shows to outperform other controllers even with no prior knowledge of the future trip whatsoever. The results obtained through HIL testing on a dSPACE ECU indicate that upon carefully choosing the prediction and control horizon length, as well as the maximum number of iterations allowed, the execution time for NMPC falls far below the necessary sampling time of 10 milliseconds in vehicle control. The correlation between each of these parameters and turn-around time have been presented; constructing a benchmark for NMPC design.
Cite this version of the work
Parisa Golchoubian (2017). Real-time Energy Management of a Battery Electric Vehicle Hybridized with Supercapacitor. UWSpace. http://hdl.handle.net/10012/11273