Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128673
Type: Thesis
Title: Energy Management System Design for Fuel Cell Vehicles
Author: Shen, Di
Issue Date: 2020
School/Discipline: School of Electrical and Electronic Engineering
Abstract: Fuel cell vehicles combine the benefits of fuel cell stacks and energy storage systems to achieve fuel economy and zero emission. Energy management systems are vital to fuel cell vehicles in fuel economy and system durability since it determines the distribution of power from the fuel cell stack and energy storage system. In this thesis, we propose three novel energy management system designs for fuel cell vehicles to improve the vehicle energy system stability, optimality and durability. We first present a non-myopic energy management system for controlling multiple energy flows in fuel cell hybrid vehicles. The control problem is solved by convex programming under a partially observable Markov decision process based framework. We propose an average-reward approximator to estimate a long-term average cost instead of using a model to predict future power demand. Thus, the dependency between the system closed-loop performance and the model accuracy for predicting the future power demand is decoupled in the energy management design for fuel cell vehicles. The energy management scheme consists of a real-time self-learning system, an average-reward filter based on the Markov chain Monte Carlo sampling, and an action selector system through the rollout algorithm with convex programming based policy. The performance evaluation of the energy management strategy is conducted via simulation studies using data obtained from real-world driving experiments and its performance is compared with three benchmark schemes. To increase the applicability of the energy management system to various driving scenarios and multiple drivers, we propose an energy management scheme in fuel cell vehicle systems. The energy management problem is cast in the form of a nonlinear infinite-time optimisation problem. A model-based fuzzy control method is employed to design the control law. By linear matrix inequality approach, sufficient conditions are proposed to design the control strategy such that the energy system is robustly stable with a desired mixed H₂/H∞ performance. The effectiveness and potential of the new design technique developed are demonstrated by different real-world driving scenarios. By using optimal control principle, we further improve the energy management system performance in terms of reducing hydrogen consumption while maintaining the battery state of charge under practical operating constraints and uncertain future power demand. The fuzzy modelling approach is employed to describe the nonlinear power plant and a robust model predictive based control is designed to achieve the desired system performance. Moreover, traffic condition is incorporated into the energy management controller design to further improve the system performance. The effectiveness and advantages of the proposed control scheme are illustrated by a simulator developed based on real-world experimental data. Finally, we investigate the problem of controlling energy flow in fuel cell vehicles by considering system stability, optimality, and durability. The energy management problem is transformed into a nonlinear optimisation problem with multi-objectives to improve fuel economy, maintain battery state of charge, and reduce the incidence of factors affecting the fuel cell performance degradation. A robust model-predictive-based fuzzy control method is employed to design the nonlinear control law. The energy management system is capable of coordinating with a fuel cell stack state of health estimator and an energy storage system scheduler to achieve the optimisation objectives in the presence of uncertainty of the driver’s power demand. The effectiveness of the new design technique developed is demonstrated by conducting studies on control performance over typical urban/highway driving scenarios.
Advisor: Lim, Cheng-Chew
Shi, Peng
Dissertation Note: Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2020
Keywords: Fuel cell
electric vehicles
energy management
partially observable Markov decision process
convex programming
fuzzy control
robust control
model predictive control
Provenance: This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals
Appears in Collections:Research Theses

Files in This Item:
File Description SizeFormat 
Shen2020_PhD.pdf8.35 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.