الفهرس 
Title : Energy management system for fuel cell battery vehicles using multi-objective online optimization
SummaryOnly 14 pages are availabe for public view
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Abstract
There is a global trend to replace Internal Combustion vehicles with cleaner vehicles to reduce CO2 emitted from vehicles and also due to the reduction in oil reserves. Hybrid Electric Vehicles, Battery Electric Vehicles, and Fuel Cell Vehicles got a lot of interest recently in the literature. An overview is provided of different types of electric vehicles and different topologies of fuel cell vehicles. In this thesis, Fuel Cell Vehicles are addressed.
The Energy Management system (EMS) is playing an important role in determining Fuel consumption and the lifetime of the powertrain system while fulfilling the load demand which is the first priority for the Energy management system. This is achieved by controlling the power split between the different energy sources. So, Energy Management System is considered the brain of the vehicle. So, different types of Energy Management Systems used in Fuel cell vehicles are compared. The advantages and disadvantages of each EMS are presented.
In many works of literature, there are attempts to present Multi-objective functions that minimize fuel consumption taking into consideration the powertrain system lifetime. But all attempts are proposing an offline Energy management system that can fit predetermined driving cycles such as trains, metro, and also for buses. The few attempts, to present online multi-objective EMS, were done after having many experimental results.
This thesis proposes a novel Multi-objective online optimization Energy Management system that can minimize fuel consumption and extend the lifetime of the powertrain system besides regulating the battery’s State of charge (SOC) without any knowledge of the driving cycle. Tuning parameters are used to achieve the needed objective of balancing between them. This EMS was tested using the Simulink Model of Honda FCX Clarity Vehicle. The simulation runs with a realistic driving condition using New European Driving Cycle (NEDC).
A comparison is presented between different scenarios of the proposed EMS and also the Original EMS of the vehicle. Equivalent hydrogen consumption is also evaluated for all Scenarios. Dynamic Programming (DP) is used as a reference for Comparison. This Comparison showed that the proposed EMS can achieve a sub-optimal solution without any prior knowledge of the Driving cycle. The simulation results show the effectiveness of the proposed EMS and its flexibility to achieve any objective. It can minimize fuel consumption, regulate the State of charge of the battery and minimize the stresses over the fuel cell or the battery depending on the tuning parameters of the proposed EMS.