Optimal and predictive power management for hybrid vehicles. Application for predefined itinerary plug-in city bus

Inhaltsverzeichnis (Table of Contents)

• Chapter 1. Introduction
• 1.1 Motivation
• 1.1.1 Depletion of oil reserves and increase of price of fuels
• 1.1.2 Air pollution issues
• 1.1.3 Global warming issues
• 1.1.4 Depletion of ozone layer
• 1.1.5 Public health issues
• 1.2 Background
• 1.2.1 What is a hybrid vehicle
• 1.2.2 Hybrid vehicles and fuel saving
• 1.2.3 Hybrid vehicle topologies
• 1.2.3.1 Parallel hybrid
• 1.2.3.2 Series hybrid
• 1.2.3.3 Mixed Hybrid
• 1.2.3.4 Advantages and disadvantages of different topologies
• 1.2.4 Hybrid vehicles existing in the market
• 1.2.4.1 Toyota
• 1.2.4.2 Honda
• 1.2.4.3 Other hybrid vehicles
• 1.3 Simulation of hybrid vehicles
• 1.3.1 Review on existing hybrid vehicle simulator
• 1.4 Definition of power management in hybrid vehicles
• 1.4.1 Power management in HEVs and P-HEVs
• 1.4.2 Literature review about power management in hybrid vehicles
• 1.5 Contributions of the Dissertation
• 1.6 Organization of the Dissertation
• Chapter 2. Hybrid vehicle modeling and simulation
• Chapter 3. Power management in HEVs
• Chapter 4. HEV fuel economy optimization over battery lifetime
• Chapter 5. Power management for P-HEV: Application for a predefined itinerary plug-in city bus
• Chapter 6. Experimental validation

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This dissertation aims to model, simulate, and optimize power management strategies for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (P-HEVs), with a focus on improving fuel economy and extending battery life. A key aspect involves leveraging information from predefined vehicle itineraries to enhance predictive control.

• Hybrid vehicle powertrain modeling and simulation.
• Development and comparison of power management strategies for HEVs (rule-based and optimal).
• Optimization of HEV fuel economy considering battery lifetime.
• Development of predictive power management strategies for P-HEVs utilizing itinerary information.
• Experimental validation of power management strategies using a real-time test bench.

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1. Introduction: This chapter introduces the motivation behind the research, highlighting the environmental and economic benefits of hybrid and plug-in hybrid vehicles. It provides background information on hybrid vehicle technologies, various topologies, and existing vehicles in the market. A review of existing hybrid vehicle simulation tools and a comprehensive literature review of power management strategies are also presented. The chapter concludes by outlining the dissertation's contributions and organization.

Chapter 2. Hybrid vehicle Modeling and simulation: This chapter details the development of a comprehensive forward-looking model for a parallel hybrid electric powertrain within the MATLAB/Simulink environment. The model integrates various subsystems (engine, motor/generator, battery, clutch, vehicle dynamics, and driver model) and incorporates a graphical user interface for ease of use and analysis. Different standard drive cycles are also defined and integrated into the simulation.

Chapter 3. Power management in HEVs: This chapter focuses on power management strategies for HEVs. It begins with a rule-based strategy based on engineering intuition, comparing its performance to a conventional vehicle. Subsequently, an optimal power management strategy is developed using dynamic programming, providing a benchmark for comparison. The optimal strategy is then used to refine the rule-based strategy, resulting in improved fuel economy. This chapter extensively discusses the results and findings of both strategies, including optimal torque splits and transmission shift sequences.

Chapter 4. HEV fuel economy optimization over battery lifetime: This chapter investigates the optimization of HEV fuel economy over the entire battery lifetime. It examines battery aging mechanisms and proposes a method to balance fuel economy per cycle with the overall battery life. An empirical model is used to predict battery life based on depth of discharge and discharge rate. Simulation results are presented to demonstrate the impact of different State of Charge (SOC) minimum thresholds on both fuel economy and battery longevity, leading to the identification of an optimal SOCmin value.

Chapter 5. Power management for P-HEV: Application for a predefined itinerary plug-in city bus: This chapter addresses power management for P-HEVs, particularly focusing on a plug-in city bus with a predefined itinerary. It begins by analyzing existing charge depletion-charge sustaining (CD-CS) strategies. A new strategy based on instantaneous optimization using the Lagrange formalism is proposed, aiming for optimal electricity usage throughout the day. This strategy leverages the knowledge of the predefined itinerary and uses an optimized Lagrange multiplier to achieve better fuel economy compared to CD-CS. A further improvement is explored by incorporating a predictive total regenerative braking strategy that aims to recover 100% of braking energy at predetermined stops.

Schlüsselwörter (Keywords)

Hybrid vehicle, power management, optimization, Lagrange formalism, predefined itinerary vehicle, total regenerative braking, dSPACE, battery life, fuel economy, dynamic programming, plug-in hybrid electric vehicle (P-HEV), hybrid electric vehicle (HEV).

Häufig gestellte Fragen

What is the main topic of this language preview?

This language preview focuses on hybrid and plug-in hybrid electric vehicles (HEVs and P-HEVs), specifically addressing their modeling, simulation, and power management strategies.

What are the main themes explored in the dissertation outlined in the preview?

The key themes include hybrid vehicle powertrain modeling and simulation, development and comparison of power management strategies for HEVs, optimization of HEV fuel economy considering battery lifetime, development of predictive power management strategies for P-HEVs utilizing itinerary information, and experimental validation of power management strategies.

What are the goals of the dissertation?

The dissertation aims to model, simulate, and optimize power management strategies for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (P-HEVs), with a focus on improving fuel economy and extending battery life. It also focuses on leveraging information from predefined vehicle itineraries to enhance predictive control.

What topics are covered in Chapter 1 (Introduction)?

Chapter 1 introduces the motivation behind the research, highlighting the environmental and economic benefits of hybrid and plug-in hybrid vehicles. It provides background information on hybrid vehicle technologies, various topologies, and existing vehicles in the market. A review of existing hybrid vehicle simulation tools and a comprehensive literature review of power management strategies are also presented. The chapter concludes by outlining the dissertation's contributions and organization.

What does Chapter 2 (Hybrid vehicle modeling and simulation) cover?

Chapter 2 details the development of a comprehensive forward-looking model for a parallel hybrid electric powertrain within the MATLAB/Simulink environment. The model integrates various subsystems (engine, motor/generator, battery, clutch, vehicle dynamics, and driver model) and incorporates a graphical user interface. Different standard drive cycles are also defined and integrated into the simulation.

What is the focus of Chapter 3 (Power management in HEVs)?

Chapter 3 focuses on power management strategies for HEVs. It begins with a rule-based strategy, comparing its performance to a conventional vehicle. Subsequently, an optimal power management strategy is developed using dynamic programming, providing a benchmark for comparison. The optimal strategy is then used to refine the rule-based strategy, resulting in improved fuel economy.

What problem is addressed in Chapter 4 (HEV fuel economy optimization over battery lifetime)?

Chapter 4 investigates the optimization of HEV fuel economy over the entire battery lifetime. It examines battery aging mechanisms and proposes a method to balance fuel economy per cycle with the overall battery life. An empirical model is used to predict battery life based on depth of discharge and discharge rate. Simulation results demonstrate the impact of different State of Charge (SOC) minimum thresholds on both fuel economy and battery longevity, leading to the identification of an optimal SOCmin value.

What is the main topic of Chapter 5 (Power management for P-HEV: Application for a predefined itinerary plug-in city bus)?

Chapter 5 addresses power management for P-HEVs, particularly focusing on a plug-in city bus with a predefined itinerary. A new strategy based on instantaneous optimization using the Lagrange formalism is proposed, aiming for optimal electricity usage throughout the day. This strategy leverages the knowledge of the predefined itinerary and uses an optimized Lagrange multiplier to achieve better fuel economy compared to CD-CS. A further improvement is explored by incorporating a predictive total regenerative braking strategy.

What are some of the key words associated with this research?

Key words include: Hybrid vehicle, power management, optimization, Lagrange formalism, predefined itinerary vehicle, total regenerative braking, dSPACE, battery life, fuel economy, dynamic programming, plug-in hybrid electric vehicle (P-HEV), hybrid electric vehicle (HEV).