Electric vehicles with autonomous driving are the future of transportation, as they are sustainable, efficient, environmentally friendly, and can provide collision-free congestion-free driving. However, the sensing and control technology adds new accessory loads which increase the vehicle energy use. Thus, it is critical to minimize energy use where possible, and optimal speed planning is a promising way to achieve this goal and is thus the topic of study for this thesis. First, a low-computation framework for the onboard calculation of energy-optimal cruising speed of battery electric vehicles is proposed. The framework is used to investigate the critical parameters for energy-optimal cruising speed determination, and it includes major internal and external vehicle losses, uses accurate motor-inverter efficiency maps as look-up tables, and does not require knowledge of the future route. This framework is validated using three electric vehicle models in MATLAB/SIMULINK. Secondly, a novel two-level model predictive control (MPC) speed control algorithm for battery electric autonomous vehicles as a successive convex optimization problem is proposed. The proposed successive convex approach produces a highly accurate optimal speed profile while also being solvable in real-time with the vehicle's onboard computing resources. This algorithm is used to perform a variety of simulated test cases, which show an energy savings potential of about 1% to 20% for different driving conditions, compared to a non-energy-optimal driving profile. Lastly, the research is expanded to consider fuel cell hybrid electric vehicles (FCHEVs), which have the added need for an optimal energy management strategy inv addition to optimal speed planning. Novel successive and integrated convex speed planning and energy management algorithms are proposed to solve the minimum hydrogen consumption problem for autonomous FCHEVs. The simulation results show that the proposed integrated method, which considers fuel cell system efficiency in the optimization objective function for speed planning, leads to 0.19% to 2.37% less hydrogen consumption compared to the successive method on short drive cycles with varying accessory loads. On the same test cycles, the integrated method uses 10.12% to 21.62% less hydrogen than an arbitrary constant-speed profile. / Thesis / Doctor of Philosophy (PhD) / Autonomous vehicles are expected to be the future of transportation, however, the high continuous electrical accessory power needed for control and perception is a challenge. Fortunately, there is an inherent property of speed planning for autonomous vehicles that can help deal with this problem. This thesis focuses on optimal speed planning to minimize energy use, proposing convex methods considering detailed internal and external losses for battery electric vehicles (BEVs), and optimal speed planning integrated with optimal energy management for fuel cell hybrid electric vehicles (FCHEVs). The proposed framework in this thesis is accurate while maintaining a low computational effort, which are the desired criteria for real-time algorithms.

With global warming, an inevitable threat to humanity, significant efforts in all carbon emitting industries are required. Electric vehicles are a suitable alternative to the petroleum dominated automotive industry. However, obstacles like charging infrastructure and limited range still stand in the way of their continued acceptance. This limited driving range can be further reduced in cold weather due to decreased battery efficiency and increased heating load. The heating in most electric vehicles is provided by an electrical positive temperature coefficient resistor. This architecture can lead to reductions in range of over 50 %. A thermal storage system has been devised and presented in this thesis which can partially or fully offset the thermal requirements. This is accomplished by pre-heating a thermal storage tank which then uses sensible energy to provide the heat for the cabin and battery pack. The system has been shown to reduce consumption and improve driving range in low ambient temperature conditions. This system successfully offers a potential solution to the concern of large range fluctuations due to different ambient temperatures. After producing a representative electric vehicle model in AMESim, it was compared to the Nissan Leaf with acceptable errors. The range implications for this baseline electric vehicle are then presented. A coolant based, thermal storage tank is then added to the model and simulated across a variety of temperatures and thermal storage masses. The results show that an 80 kg, 80 °C coolant tank can provide all the heating requirements for a 36 km, hour and 9 minute city drive cycle. Offering a calculated consumption reduction of up to 36 % at -30 °C as compared to the baseline electric vehicle model. Furthermore, a yearly analysis was performed based on this cycle and the results have shown that an optimal 30 kg thermal storage tank can decrease the yearly average consumption by up to 20 Wh/km or 12 %. / Thesis / Master of Applied Science (MASc)

This paper is designed to assist communities with the successful introduction, integration and support of electric vehicles. It provides an introductory look at electric vehicle technology and its application within the community, and should act as a springboard for further investigation and plan development for interested communities. This paper outlines the basic history of electric vehicle technology, the recent forces which have prompted increased research and development, and the leading causes for this evolution in personal transportation. The core of the document describes the steps a community will need to take in order to successfully develop a local electric vehicle program. Initial steps include developing community goals, understanding the community and its transportation environment, and creating an organizational structure to successfully develop a local Electric Vehicle Action Plan. The organizational structure provided is divided into three basic elements; Policy, Infrastructure, and Public Awareness. The analysis of each element includes the identification and discussion of the critical issues, a description of the key participants who should be involved, and recommended methodology for initiating and supporting local electric vehicle commercialization. Furthermore, each element includes a short analysis of three separate market niches that are ripe for early electric vehicle introduction. These applications can be used by the community to develop a local electric vehicle demonstration program and establish a foundation on which to build an electric vehicle community. This paper, if used to its potential, can help communities develop a program which will successfully introduce and integrate electric vehicles into the local transportation mix and bring America a little closer to a sustainable transportation system. / Department of Urban Planning

All electric machines have two mechanical operations, motoring and braking. The nature of braking can be regenerative, where the kinetic energy of the rotor is converted into electricity and sent back to the power source or non-regenerative, where the source supplies electric power to provide braking. This thesis investigates several critical issues related to regenerative braking in both DC and AC electric machines, including the determination of boundaries in the torque-speed plane defining the regenerative braking capability region and the evaluation of operating points within that capability region that result in maximum regenerative braking recharge current. Electric machines are used in the powertrains of electric and hybrid-electric vehicles to provide motoring or braking torque in response to the driver's request and power management logic. Since such vehicles carry a limited amount of electrical energy on-board their energy storage systems (such as a battery pack), it is important to conserve as much electrical energy as possible in order to increase the range of travel. Therefore, the concept of regenerative braking is of importance for such vehicles since operating in this mode during a braking event sends power back to the energy storage system thereby replenishing its energy level. Since the electric machine assists the mechanical friction braking system of the vehicle, it results in reduced wear on components within the mechanical friction brake system. As both mechanical friction braking and electric machine braking are used to provide the requested vehicle braking torque, braking strategies which relate to splitting of the braking command between the two braking mechanisms are discussed. The reduction in energy consumption of a test vehicle along different driving schedules while using different braking strategies is also studied.

Thesis (M.E. Mechanical Engineering)--University of Waikato, 2008. / Title from PDF cover (viewed October 2, 2008) Includes bibliographical references (p. 131-136)

<p dir="ltr">Engine downsizing with the assistance of turbomachinery and/or energy storage system has been realized to be one of the most promising and cost-effective solutions in pursuit of cleaner and more efficient engine products. Fundamental challenges however, exist in terms of control and energy management of advanced powertrain featuring turbocharged engines due to their complex dynamics, inherent coupling nature, and strict emission regulations concerning environmental preservation. For the purpose of addressing those challenges, this dissertation develops an integrated framework for modeling, robust coordinated control, and power management of advanced powertrains featuring turbocharged engines.</p><p dir="ltr">This dissertation first studies an advanced turbocharged lean-burn SI natural gas engine manufactured by Caterpillar, and develops an intuitive physics-based, control-oriented model. The obtained control-oriented model is validated against a high-fidelity truth-reference model and serves as the basis on which a robust coordinated control system is developed. The dissertation then proposes a comprehensive procedure for synthesizing a robust coordinated control system applying optimization-based H_infinity control theory. Specifically, this framework outlines a methodology of modeling uncertainties to account for system robustness, and providing valuable insights into the tuning of general coordinated control system design. For performance testing, the synthesized robust coordinated control system is implemented on the high-fidelity truth-reference model. A parallel closed-loop simulation strategy is adopted so that direct comparison between the robust coordinated control system and benchmark production control system (composed of multiple fine-tuned PID controllers) developed by Caterpillar can be carried out. Simulation results manage to demonstrate the merit of utilizing the robust coordinated control system, with better performances observed in terms of steady-state tracking, transient response, and disturbance attenuation.</p><p dir="ltr">The second part of this dissertation focuses on the development of a proposed novel hybrid electric wheel loader which features a downsized engine assisted by turbocharger and an energy storage system. Research efforts documented in this dissertation involve system configuration, controller design (both component-level and supervisory-level), simulation development (both software-in-the-loop and hardware-in-the-loop) and simulated validation for the proposed novel wheel loader. Inspired by the successful simulation results, John Deere assembled a real demo vehicle with the proposed powertrain and conducted some in-field testing, from which encouraging experimental results are observed.</p>

The growth of the market for electric vehicles (EVs) is increasing each year with record numbers of newly registered electric vehicles in 2022. Electric vehicles are considered the key component of a sustainable transportation sector. The knowledge of influential factors on purchase decisions for new EVs is high. The research attention within the field has been huge over the last decade. As the number of EVs in the transport sector increases, the number of used EVs is increasing. However, basically no research about influential factors for purchase decisions of used EVs can be found. As the prices of new EVs are high, the market for such vehicles is constrained to certain consumer groups. To succeed with the transformation of the transport sector, more consumer groups need to have access to EVs, which can be accomplished with a well-established market for used EVs. The knowledge about financial factors for used EVs need to increase. The purpose of this study is to understand if well-known financial factors influencing purchase decisions for new EVs have similar effects on purchase decisions for used EVs. As in previous studies, this study applies a quantitative research approach where an online survey has been designed with liker-scale questions. A total of 90 respondents in varying age groups answered the survey based on five categories of financial factors: financial incentives, purchasing price, operational costs, maintenance costs, and residual value. The data has been analyzed using Spearman correlation, principal component analysis (PCA), and ordered logistic regression. The Spearman correlation found positive significance between the financial factors and the purchase intention of new and used EVs. The factors identified in the PCA are in line with the expected factors as the predefined research model. The ordered logistic regression could however support significant relation with purchase intensions of new EVs the aspect of financial incentives and residual value. For used EVs the regression was not successful. This study shows that the influence of financial incentives on the purchase intention of EVs, are similar to both new EVs as for used EVs. This result is however based on a small data sample that is much smaller than previous studies. For future research it is important to retrieve a larger data sample to improve reliability of the result. The regression analysis did not support the analytical outcome of the Spearman correlation and it needs to be further analyzed why a significant regression model could not be defined in relation to used EVs.

The future success of chargeable vehicles will, among other factors, depend on their charging costs and their ability to charge with minimal disturbances to the national, local and household electrical grid. To be able to minimize costs and schedule charging sessions, there has to be knowledge of how the charging power varies with time. This is called charging profile. A number of charging profiles for a Volvo V60 plug‑in hybrid electric vehicle have been recorded. For charging currents above 10 A they prove to be more complex than are assumed in most current research papers.   The charging profiles are used together with historical electricity prices to calculate charging costs for 2013 and 2014. Charging is assumed to take place during the night, between 18:00 and 07:00, with the battery being totally depleted at 18:00. By using a timer to have the charging start at 01:00, instead of immediately at 18:00, annual charging costs are reduced by approximately 7 to 8%. By using dynamic programming to optimize the charging sessions, annual charging costs are reduced by approximately 10 to 11%. An interesting issue regarding dynamic programming was identified, namely when using a limited set of predetermined discrete control signals, interpolation returns unrealizable cost-to-go values. This occurs specifically for instances crossing the zero cost-to-go area boundary.   It is concluded that the mentioned savings are realizable, via implementing timers or optimization algorithms into consumer charging stations. Finally, by using these decentralized charging planning tools and seen from a power usage perspective, at least 30% of the Swedish vehicle fleet could be chargeable and powered by the electrical grid.

To make a compelling case for government incentives as a stimulus for alternative fuel vehicle adoption, this thesis assesses the preliminary impacts associated with the elimination of Georgia’s income tax credits for low-emission and zero-emission vehicle purchases. The thesis identifies policy factors that appear to impact alternative fuel vehicle (AFV) adoption in the United States, with a focus on government incentives. Specific policy factors are discussed in the context of state and federal laws. For Georgia, motor vehicle registrations were collected to track AFV adoption rates before and after the change in law. Electric and hybrid vehicle registrations in Georgia have plummeted since the income tax credits were eliminated on June 30, 2015. Income tax credit data were collected to chart the significant increase in zero-emission and low-emission vehicle purchases and leases since electric vehicles started flooding the market. The primary outcome of this research is a set of distinct, measurable policy factors that influence AFV adoption in the United States. The factors identified include: 1) reward amount to income ratio, 2) ease of policy comprehension, 3) consumer awareness, 4) fuel/vehicle coverage of incentives, 5) incentive user groups, 6) forms of incentives (grants, income tax credits, etc.), 7) number of incentives available, and 8) dollar values of incentives. The conclusion presents factors for use in choice model estimation. These factors should be useful by policymakers who are trying to understand the true value of government incentives for alternative fuel vehicles.

Energy and transportation are topics of great importance to global sustainable development.  Tesla Motor Corporation is an electric vehicle company with the objective to “accelerate the world’s transition to sustainable energy” (Musk, 2016).  This thesis, a media discourse analysis, examines media texts concerning Tesla Motors to provide a better understanding of the company’s hitherto success in penetrating the automotive market.  Qualitative analyses of text were utilized to first define the discourse, then to describe how it has contributed to Tesla’s success.  A combination of word frequency analysis, textual analysis for positive modality, and analysis for principles of branding was utilized as method.  A sample set of 15 texts were analyzed to define the macro discourse, and one interview of Elon Musk analyzed closely to explicate how the textual content contributes to the company’s success. The results of a word frequency analysis suggest that Elon Musk’s personal narrative represents the discourse surrounding Tesla Motors and that it contributes to the company’s success via being imbued with authoritybuilding, trust building, and branding content.