The transportation sector in California has begun a shift toward adopting Electric Vehicles (EVs) as a primary source of individual and corporate mobility. The US Government and the State of California are initiating public-sector financed charging station infrastructure to help in this change-over to EVs. Automobile companies and private enterprises are also heavily investing in Battery Electric Vehicle (BEV) infrastructure going forward. The state of California is subject to natural disasters such as Fire, Earthquakes, and periodic flooding. Increasing numbers of BEVs may add new challenges to mass evacuations that are often associated with natural disasters. This work focuses on unique challenges in providing BEV charging infrastructure during evacuations in regions that: are small towns with a considerable rural population, are prone to natural disasters, have a single evacuation route, have underdeveloped EV charging infrastructure, are considerable distance to a major center of EV charging infrastructure and safety from the mass evacuation scenario, have a secondary small charging location also available on the single evacuation route that leads to the major city of safety. To analyze the unique challenges of these particular mass-evacuation scenarios, a simulation was created to estimate the evacuation times of the BEV population given a set charging infrastructure. The model also includes BEV charging infrastructure, and for a single secondary charging station that is along the evacuation route. The objective of the simulation model is to determine the charging needs for a rural evacuation scenario and the ideal distance to an alternate secondary charging station along a single evacuation route in order to minimize total evacuation time. In order to provide a more realistic set of scenarios for the model, two different rural evacuation scenarios were analyzed. Kernville, California, in Kern County that is 52 miles from Bakersfield Auberry, California, in Fresno County that is 36 miles from Fresno The BEV charging infrastructure model inputs are customized for assumed BEV charging infrastructure in the year 2025 based on historical BEV registration numbers according to the Department of Motor Vehicles. The simulation results show that the projected charging infrastructure in the year 2025 would suffice for an evacuation scenario in which 90% of the BEV arrive at the evacuation destination within 10 hours of the evacuation order. However, due to the severity of potential danger in short-notice wildfire evacuations, it would be ideal to further decrease the total evacuation time. The simulation model found that increasing the charging infrastructure by one level 3 charge plug had a much larger impact on minimizing evacuation time than increasing it by two level 2 charge plugs. Therefore, it would be beneficial for the rural towns to invest in level 3 chargers to shorten evacuation times.

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)

The number of electric vehicles on the road is increasing rapidly every year. Due to the decreased sound produced by these vehicles at low speeds, there is significant concern that pedestrians and bicyclists will be at increased risk of vehicle collisions. This is particularly true for those with vision impairment who cannot rely on visual cues to alert them of an approaching vehicle. This thesis explores pedestrian aural detectability of six electric vehicle additive sounds produced by two additive sound systems: a modified version of the factory equipped system and a prototype exciter transducer-based system. All additive sounds and systems were first evaluated for regulatory compliance at stationary, 10 km/h, and 20 km/h conditions and then pedestrian detectability was assessed using 16 blind folded participants and on-road drive by tests. Participant drive by tests were then replicated using 3D sound field recordings played in a high-fidelity immersive reality lab. Results were used to verify the accuracy of lab environment and its potential applicability to future testing. The exciter transducer acoustic warning system was found to created more uniform sound levels on the passenger and drivers' sides of the vehicle than the factory system but produced lower sound levels on the front side of the vehicle. Additive sound modulation rate was not determined to be a key differentiator in pedestrian detectability and low frequency emphasis sounds were found to have the highest level of pedestrian detectability. As expected, vehicle speed played a critical role in participant detection with the 20 km/h speed condition producing higher average detection distances. The immersive reality lab was found to not replicate on-road environment however a perceived linear offset was observed between the two environments. / Master of Science / The number of electric vehicles on the road increases every year due to growing consumer demand for clean and sustainable transportation. Due to the decreased sound produced by these vehicles at low speeds there is significant concern that pedestrians and bicyclists will be at increased risk of vehicle collisions. This is particularly true for those with vision impairment who cannot rely on visual cues to alert them of an approaching vehicle. This thesis explores pedestrians' ability to detect six electric vehicle additive sounds produced by two sound systems: a modified version of the factory equipped system and a prototype system designed to produce uniform sound around the vehicle. All sounds and systems were evaluated see if they met current regulations at stationary, 10 km/h, and 20 km/h conditions. Pedestrians' ability to detect the vehicle was assessed using 16 blind folded participants and on-road tests where participants were asked to press a button when they heard an approaching vehicle. Participant drive by tests were then replicated using recordings taken on the same section of road and played in a lab environment. Results were used to see if the lab environment matched the results seen on the road. The prototype system created more uniform sound levels on the passenger and drivers' sides of the vehicle than the factory system but consistently produced lower sound levels on the front side of the vehicle. Sound modulation rate was not determined to be a key differentiator in pedestrian detectability and low frequency emphasis sounds were found to be the most easily detected by pedestrians. As expected, vehicle speed played a critical role in participant detection with the 20 km/h speed condition producing higher detection distances. The lab environment was found to not replicate on-road environment however similar offsets and sound ordering was observed between the two environments. Further work will be needed assess and correct this disagreement.

Thesis advisor: John J. Piderit / This paper examines state and county-exclusive incentives on battery electric vehicle (BEV) registration in the United States. Using two main methods, a differences-in-differences method and a sigmoidal growth rate equation, I examine the impact of non-federal incentives on the total amount of electric vehicles between 2017 and 2022, as well as estimate the years that each state will reach its net-zero goals for carbon emissions in the transportation sector. I hope to provide a deeper understanding of the effectiveness of incentive policy, based on differing levels of incentive policy between regions, in order to best increase electric vehicle adoption in a cost-effective method. In addition, I hope that my estimates of net-zero projections will serve as a beneficial comparison to track states’ respective progress towards sustainable energy in vehicles. These findings can be used to assist policymakers in determining appropriate BEV adoption policies based on regional consumer demographics and needs, as well as visualize a timeline for the next century of rapid electric vehicle growth. / Thesis (BA) — Boston College, 2024. / Submitted to: Boston College. Morrissey School of Arts and Sciences. / Discipline: Economics. / Discipline: Departmental Honors.

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 1, 2008) Includes bibliographical references (p. 122-130)

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>