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Beginning with problems of global energy resource shortage and environmental pollution that the automobile industry is facing, this chapter depicts the urgency of vehicle research to save energy and reduce emissions. In order to explain the rationale behind application of vehicle power management, the energy conversion chain for vehicle energy consumption is drawn to readers. After that, the objectives of this book are listed and described. In the following section, current research issues in vehicle power management are delineated and compared as well. The last section of this chapter establishes the organization of this book.
KeywordsFuel Consumption Artificial Neural Network Model Fuel Economy Power Management Hybrid Electric Vehicle
The objects under consideration in this book are automobile or motor cars, i.e., wheeled motor vehicles for transporting passengers or goods, which also carry their own engines or electric motors. Nowadays, most vehicles running on the road are propelled by spark-ignition (SI) or compression-ignition (CI) internal combustion engines (ICEs) that use gasoline or diesel as fuels.
Limited oil reserves, increased demand and costs for oil-based fuels, as well as air pollution and greenhouse emissions, are challenging the automobile industry. With fuel and emission reduction as the main objectives, alternative power systems for hybrid electric vehicles (HEV), electric vehicles (EV) and fuel cell vehicles are under development and production. Nevertheless, energy losses in vehicle operation and replacement of aged power sources, and pollutant emissions (in HEV) also exist in these new technologies. Regardless of the type of vehicles, it is essential to improve energy efficiency, reduce emission and extend lifetime of power sources without sacrificing vehicle performance, safety, and reliability.
First of all, to help readers comprehend the urgency of vehicle research for energy saving and emission reduction, we will start with the introduction to the above global problems which the automobile manufacturers are facing today.
1.1 Energy and Environmental Challenges
ICE powered vehicles rely on gasoline and diesel combustion during operation. Pollutions are generated during the combustion process inside the ICE. In addition, unburned fuel evaporates which forms the basis for another type of pollution-volatile organic compounds (VOC).
Cold Start—during cold weather, the catalyst which is used to control tailpipe emissions will not work until they have been warmed up to a certain temperature. Hence, starting and driving a vehicle in the first few minutes result in higher emissions.
Running Exhaust Emissions—emissions are formed during normal operation of the vehicle-driving and idling.
Running Losses—During vehicle running, the gasoline is vaporized in the hot engine and exhaust system.
Hot Soak—The engine remains hot for a while after the vehicle is turned off, and gasoline evaporation continues when the car is parked while cooling down.
Diurnal Emissions—Even when the vehicle is parked for long periods of time, gasoline evaporation occurs due to the high ambient temperature.
Refueling—While the tank is being filled, gasoline vapors escape from the vehicle’s fuel tank and the refueling tubes.
In the United States, vehicles contribute 25 and 33% of the total VOC and NO x respectively which combine to form ground-level ozone. Additionally, the combined direct and indirect contribution of vehicles amounted to 49 and 55% of national PM10 and PM2.5 (both belong to particulate matter) emissions, respectively . Unfortunately, ozone and particulate matter are identified as contributors towards worsening the health of people with asthma and other related public health impacts, e.g. increases in medication use, doctor and emergency room (ER) visits, and hospital admissions. Moreover, the possible contribution of vehicle pollution to the development of asthma, frequent respiratory infections and potential long-term effects of retarded lung growth and reduced lung function in children (which can lead to chronic lung disease later in life) may even have greater long-term public health significance .
Besides, vehicles play a disgraceful role in global climate change. Burning of fossil fuels contributes to the increase of carbon dioxide (CO2) in the atmosphere. This will result in increased thickness and density of the atmosphere due to the action of carbon dioxide and other greenhouse gases (i.e., water vapor, ozone and methane) in the atmosphere. The thicker and denser atmosphere will trap heat inside the atmosphere to form the basis of the greenhouse effect [14, 15, 16]. It may increase the global air temperature and introduce global climate change due to disturbance to the eco system. The Intergovernmental Panel on Climate Change (IPCC) concluded in 2007  by stating that: “Most of the observed increase in globally averaged temperatures since the mid-twentieth century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations.” The consequences including the widespread melting of snow and ice and rising global average sea level will bring disasters to the earth particularly to the maritime countries. Other consequences include increase flood and drought and extreme weathers in certain parts of the world which can cause disasters to many areas.
1.2 Energy Conversion Chain for Vehicle Energy Consumption
There exist energy losses in every junction of the energy conversion chain. Although the energy conversion efficiency and pollutant emissions in the first step have a great impact on the entire energy saving and environmental protection, large power plants, refineries, or other process engineering systems are beyond the scope of this book. The vehicle power management concept arises for the second energy conversion step (i.e., on-board energy carriers to vehicle mechanical energy) aimed at improving fuel economy, reducing pollutant emissions and maintaining power sources working properly, while the performance and safety are not impacted at all. For the ultimate step, energy conversion is natural and uncontrollable in a fixed driving profile, except that vehicle functional topology changes ahead of vehicle operation. In summary, the second energy conversion step, where vehicle power management strategies are embedded, acts as the main emphasis of this book.
1.3 Fuel Efficiency
Hence one can convert MPG to L/km by dividing 2.35 by the MPG numbers. Similarly one can covert L/km to MPG by dividing 2.35 by the L/km numbers. For example, to convert 33 MPG to L/km, one divides 2.35 by 33 to get 0.0712 L/km. To convert 7.5 L/100 km to MPG, one divides 235 by 7.5 to get 31.3 MPG.
Since different road conditions and driving patterns require different amount of fuel for a given distance, fuel economy or fuel consumption has to be evaluated upon different driving scenarios, also known as driving cycle tests, as further explained in Chap. 2.
1.4 Main Objectives of This Book
Although vehicle power management is familiar to related researchers and designers, this book, addressed to readers at various levels, describes and analyzes the basic concepts with respect to different vehicle configurations. The factors influencing the fuel economy and emissions are also discussed.
Today’s advanced vehicles contain a significant number of components which consume a substantial amount of power. For vehicle designers, it’s impossible to deal with the optimization of the entire system using heuristic methods. The model-based method has been proved to be the most efficient way for the initial-phase of vehicle design  (e.g. determination of vehicle structure and verification of control strategies in simulation). Consequently, this book also introduces the modeling of various devices and components in a vehicle which are involved in the vehicle power management system.
Although reasonable amount of literature exists in the area of vehicle power management , there always exists a feeling that the research results are dispersive and not systematic. To the authors’ best knowledge, there do not exist comprehensive references that systematically define, analyze, and summarize this topic, which can be meaningfully applied to vehicle applications. This book is intended to bridge this gap. Specific algorithms and strategies including analytical approaches, optimal control, intelligent system approaches, wavelet technology, and optimizations, are theoretically derived and analyzed in this book for realistic applications towards vehicle power management. Optimal control, in particular the dynamic programming (DP) and the intelligent schemes (e.g. fuzzy logic control, neural networks, etc.), are existing popular power management methodologies for the purpose so far, while the wavelet technology is introduced for the first time to vehicle power management.
Electrification of the automobile is the current focus to shift fossil fuel based transportation to alternative energy based transportation. The fuel cell and battery are commonly regarded as two major alternative power sources for vehicle propulsion [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. Equipped with these alternative power sources, various types of vehicles with low or zero emissions, pure electric vehicles (EV), plug-in hybrid electric vehicles (PHEV), hybrid electric vehicles (HEV), and fuel cell vehicles (FCV), have become research and development emphasis lately, which are within the scopes of this book. Despite of low or zero emissions, the problems of equivalent fuel economy improvement and power source lifetime extension in these alternative fuel vehicles or alternative drive train vehicles are still under consideration. So vehicle power management is beneficial and suitable for the above advanced vehicles.
In addition, the hardware-in-the-loop for vehicle power management research to emulate the realistic conditions is introduced with existing and potential real system designers in mind. Useful tools and devices, as well as some experimental results, can be found in this book to enlighten designers when establishing their own experimental platforms for vehicle power management research.
1.5 Issues in Research on Vehicle Power Management
Automotive industry is focusing on developing affordable vehicles with increased electrical/electronic components to satisfy consumers’ needs on safety and comfort, while improving fuel economy and reducing emissions to comply with environmental regulations. With this in perspective, vehicle power management strategy that is employed to control power flow of power sources in vehicles was proposed during the last two decades to meet the above challenges .
Hybrid electric vehicles (HEV) are one of the leading technologies aimed towards sustainable mobility, and vehicle power management that is suitable for all types of vehicles has been more intensified by this emerging technology .
Mathematical models or human expertise are critical to the development of most of the power management approaches prior to real applications. Optimal control deals with the problem of finding a control law for a given system such that a certain optimality criterion is achieved. A control problem includes a cost function that is a function of state and control variables. An optimal control is a set of differential equations describing the paths of the control variables that minimize the cost function [39, 40, 41]. The optimal control strategy has been the most popular vehicle power management approach since the fuel consumption or emissions or other indexes can be considered as a cost function. The optimal control, especially dynamic programming (DP), has been widely applied to a broad range of vehicle models [42, 43, 44]. When using optimal control in vehicle power management, researchers usually assumes that the entire driving cycle is available for analysis and algorithm development. This assumption can only provide off-line solution to the problem. Nevertheless, the off-line results provide a benchmark for performance of control strategies prior to real applications.
Recently, the intelligent system approaches including the artificial neural network (ANN), fuzzy logic, etc. have been introduced into vehicle power management [45, 46, 47]. The ANN, composed of artificial neurons or nodes, is a mathematical model or computational model. With the desire to incorporate the fuel consumption or emissions as design criteria, researchers used ANN models for prediction of vehicle behaviors . The ANN models are trained using data from tests or simulations for different driving cycles.
Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise [45, 49]. In fuzzy logic, the degree of truth of a statement ranges between 0 and 1. In fuzzy-logic-based vehicle power management strategy, fuzzy rules were developed by researchers for the fuzzy controller to effectively determine the power split between various power sources in vehicles. The fuzzy controllers can be built based on some variables such as the driver command, the SOC of the energy storage system (ESS), the alternator speed, etc. The purpose of obtaining fuzzy rules is to optimize the operational efficiency of different power sources in all types of vehicles.
The analytical approach tries to reduce a system to its elementary elements in order to study in detail and understand the types of interaction between them. General laws can be inferred to predict the system properties under various conditions. Laws of the additivity of elementary properties have to be invoked to guarantee the possibility of this prediction . The analytical approach can take on the responsibility of meeting the objective (i.e., fuel consumption minimization) of the plug-in hybrid electric vehicle (PHEV). The simplified or unified analytical power solution to this optimization problem can be derived on basis of a realistic vehicle model comprised of individual power source components. Usually different PHEV operation modes such as the pure electric mode and blended mode need to be involved in discussion.
Besides, wavelets are introduced in this book for vehicle power management system applications. Wavelet transforms can be considered as forms of time–frequency representation for continuous-time signals, and so are suitable for harmonic analysis [50, 51, 52]. High-frequency transients can be identified from the real time power demand of the drive line. With the help of wavelet transform, a proper power demand combination can be achieved for power sources in all types of vehicles. The wavelet-based power management strategy helps improve system efficiency and life expectancy of power sources, usually in the presence of various constraints due to drivability requirements and component characteristics.
Details of the above state-of-the-art technologies for vehicle power management will be described from their fundamentals to specific applications for various types of vehicles in the later chapters of this book.
1.6 Book Organization
Chapter 2 introduces the basic concepts of vehicle power management such that readers can have a clear global idea of what vehicle power management is. Due to the importance of modeling to development of vehicle power management strategies during the initial phase, Chap. 3 establishes mathematical or electrical models for vehicle propulsion systems where the internal combustion engine (ICE), battery, ultracapacitor, fuel cell, etc. may exist with respect to various vehicle types. Multiple vehicle power management strategies are described and analyzed in Chaps. 4, 5, 6, 7, 8, 9. Chapter 4 is devoted to the analytical approaches for the power management of hybrid and plugin hybrid electric vehicles. Chapter 5 introduces wavelets to the vehicle power management system. Chapter 6 introduces dynamic programming and quadratic programming for the power management of hybrid and plugin electric hybrid vehicles. Chapter 7 depicts two intelligent system approaches i.e., the fuzzy logic and neural networks, for vehicle power management. Chapter 8 briefly discusses the battery management in EV, HEV and PHEV. Chapter 9 discusses the component optimization which can also result in performance improvement of HEV and PHEV. In order to verify the validity of power management strategies prior to real applications in vehicles, experimental platforms are necessary. Thus Chap. 10 describes the definition and structure of hardware-in-the-loop (HIP), and introduces relevant experimental devices and methodologies to enlighten the readers. Finally, Chap. 11 gives an outlook on trends in future vehicle power management.
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