Learning Rates Of Electric Vehicles

Governments of many countries consider the electrification of individual passenger transport as a suitable strategy to decrease oil dependency and reduce transport-related carbon dioxide (CO2) and air pollutant emissions. However, battery-electric vehicles (BEVs) and plug-in hybrid-electric vehicles (PHEVs) have been more expensive than their conventional counterparts and suffer from relatively short electric driving ranges, which still hampers the market potential of these vehicles. Despite persisting shortfalls, mechanisms such as technological learning and economics of scale promise to improve the techno-economic performance of BEVs and PHEVs in the short- to mid-term. Here, the author seeks to obtain insight into the techno-economic prospects of BEVs and PHEVs by: (i) establishing experience curves and (ii) quantifying user costs and the costs of mitigating carbon dioxide and air pollutant emissions in a time-series analysis. The analysis captures the situation in Germany between 2010 and 2016.

Technological Learning in the Transition to a Low-Carbon Energy System: Conceptual Issues, Empirical Findings, and Use in Energy Modeling quantifies key trends and drivers of energy technologies deployed in the energy transition. It uses the experience curve tool to show how future cost reductions and cumulative deployment of these technologies may shape the future mix of the electricity, heat and transport sectors. The book explores experience curves in detail, including possible pitfalls, and demonstrates how to quantify the ‘quality’ of experience curves. It discusses how this tool is implemented in models and addresses methodological challenges and solutions. For each technology, current market trends, past cost reductions and underlying drivers, available experience curves, and future prospects are considered. Electricity, heat and transport sector models are explored in-depth to show how the future deployment of these technologies—and their associated costs—determine whether ambitious decarbonization climate targets can be reached - and at what costs. The book also addresses lessons and recommendations for policymakers, industry and academics, including key technologies requiring further policy support, and what scientific knowledge gaps remain for future research. Provides a comprehensive overview of trends and drivers for major energy technologies expected to play a role in the energy transition Delivers data on cost trends, helping readers gain insights on how competitive energy technologies may become, and why Reviews the use of learning curves in environmental impacts for lifecycle assessments and energy modeling Features social learning for cost modeling and technology diffusion, including where consumer preferences play a major role

Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.

In this book, theoretical basis and design guidelines for electric vehicles have been emphasized chapter by chapter with valuable contribution of many researchers who work on both technical and regulatory sides of the field. Multidisciplinary research results from electrical engineering, chemical engineering and mechanical engineering were examined and merged together to make this book a guide for industry, academia and policy maker.

Important factor in political decision-making is a public opinion as well. Therefore, it is very important to raise global ecological awareness and wider public education regarding ecology. Goal of this book is to bring closer to the readers new drive technologies that are intended to environment and nature protection. The book presents modern technique achievements and technologies applied in the implementation of electric vehicles. Special attention was paid to energy efficiency of EV's. Also today's trends, mathematical models and computer design elements of future cars are presented.

The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.

Electric Vehicle Integration in a Smart Microgrid Environment The growing demand for energy in today’s world, especially in the Middle East and Southeast Asia, has been met with massive exploitation of fossil fuels, resulting in an increase in environmental pollutants. In order to mitigate the issues arising from conventional internal combustion engine-powered vehicles, there has been a considerable acceleration in the adoption of electric vehicles (EVs). Research has shown that the impact of fossil fuel use in transportation and surging demand in power owing to the growing EV charging infrastructure can potentially be minimalized by smart microgrids. As EVs find wider acceptance with major advancements in high efficiency drivetrain and vehicle design, it has become clear that there is a need for a system-level understanding of energy storage and management in a microgrid environment. Practical issues, such as fleet management, coordinated operation, repurposing of batteries, and environmental impact of recycling and disposal, need to be carefully studied in the context of an ageing grid infrastructure. This book explores such a perspective with contributions from leading experts on planning, analysis, optimization, and management of electrified transportation and the transportation infrastructure. The primary purpose of this book is to capture state-of-the-art development in smart microgrid management with EV integration and their applications. It also aims to identify potential research directions and technologies that will facilitate insight generation in various domains, from smart homes to smart cities, and within industry, business, and consumer applications. We expect the book to serve as a reference for a larger audience, including power system architects, practitioners, developers, new researchers, and graduate-level students, especially for emerging clean energy and transportation electrification sectors in the Middle East and Southeast Asia.

This open access book analyzes the transition toward a low-carbon energy system in Europe under the aspects of flexibility and technological progress. By covering the main energy sectors – including the industry, residential, tertiary and transport sector as well as the heating and electricity sector – the analysis assesses flexibility requirements in a cross-sectoral energy system with high shares of renewable energies. The contributing authors – all European energy experts – apply models and tools from various research fields, including techno-economic learning, fundamental energy system modeling, and environmental and social life cycle as well as health impact assessment, to develop an innovative and comprehensive energy models system (EMS). Moreover, the contributions examine renewable penetrations and their contributions to climate change mitigation, and the impacts of available technologies on the energy system. Given its scope, the book appeals to researchers studying energy systems and markets, professionals and policymakers of the energy industry and readers interested in the transformation to a low-carbon energy system in Europe.