Electric cars are projected to become the vehicles of the future. A major barrier for their expansion is range anxiety stemming from the limited range a typical EV can travel. EV batteries' performance and capacity are affected by many factors. In particular, the decrease in ambient temperature below a certain threshold will adversely affect the battery's efficiency. This research develops deterministic and two-stage stochastic program model for charging stations' optimal location to facilitate the routing decisions of delivery services that use EVs while considering the variability inherent in climate and customer demand. To evaluate the proposed formulation and solution approach's performance, Fargo city in North Dakota is selected as a tested. For the first chapter, we formulated this problem as a mixed-integer linear programming model that captures the realistic charging behavior of the DCFC's in association with the ambient temperature and their subsequent impact on the EV charging station location and routing decisions. Two innovative heuristics are proposed to solve this challenging model in a realistic test setting, namely, the two-phase Tabu Search-modified Clarke and Wright algorithm and the Sweep-based Iterative Greedy Adaptive Large Neighborhood algorithm. The results clearly indicate that the EV DCFC charging station location decisions are highly sensitive to the ambient temperature, the charging time, and the initial state-of-charge. The results provide numerous managerial insights for decision-makers to efficiently design and manage the DCFC EV logistic network for cities that suffer from high-temperature fluctuations. For the second chapter, a novel solution approach based on the progressive hedging algorithm is presented to solve the resulting mathematical model and to provide high-quality solutions within reasonable running times for problems with many scenarios. We observe that the location-routing decisions are susceptible to the EV logistic's underlying climate, signifying that decision-makers of the DCFC EV logistic network for cities that suffer from high-temperature fluctuations would not overlook the effect of climate to design and manage the respective logistic network efficiently.

Both the solar photovoltaic (PV) installation and electric vehicles (EVs) deployment are increasing significantly in Sweden. With the large-scale integration of PV and EVs, problems such as the voltage deviations and overloading of components can arise, since the existing distribution grids are not designed to host the large shares of new EV loads and the intermittent PV power feed-in. This thesis investigates a C2V2C (i.e., Community to EV to Community) energy flow concept and evaluates how it can improve the power balance performances in communities with both PV and EV integrated in Sweden. Community refers to a group of buildings (i.e., two or more) connected within the same microgrid. It aims to develop a C2V2C model, which utilizes smart charging of electric vehicles to deliver electricity between different communities, for improving the performances at multiple-community-level. A coordinated control of EV smart charging is developed using the genetic algorithm, and its performance is compared with an existing individual control. Two control strategies are considered: (i) minimizing the peak energy exchanges with the grid and (ii) minimizing the electricity costs. Case studies are conducted considering a residential community and workplace community, as well as one EV commuting between them. The study results show that the advanced control achieves a cost reduction of up to 280 % in a summer week compared to the individual control. In a winter week, a performance improvement of up to 13 % can be achieved using advanced control. The advanced control can also reduce the energy exchange peaks with the power grid of the multiple communities. This study has proven the effectiveness of the C2V2C model in enhancing the local power balance at multiple-community-level. It will enhance the resilience and grid-friendliness of building communities, thus paving way for the large PV and EV penetration in the future.

<p>It is widely recognized that a requisite aspect of addressing climate goals is to develop a more sustainable transportation sector. One initiative towards this is the federal administration’s stated goal that 50% of all new vehicle sales will be electric by the year 2030. However, it is a common consensus that this will not occur without significant changes in electric vehicle (EV) adoption trends. In order to meet this goal and significantly diminish transportation greenhouse gas emissions, it is critical to better understand EV adoption at scale. To do this, we must understand at the system level what the progression of adoption will look like and what factors influence that adoption.</p> <p>This problem requires a more granular analysis than has been previously performed. We analyze adoption at the ZIP code level in four US states (CA, CO, NY, WA) with historical data dating to 2011. To understand the progression of adoption, we consider two adoption models (the logistic model and the Bass model) to forecast future EV levels in ZIP codes. We find that the logistic is better for the data that is currently publicly available.</p> <p>We additionally find that EV forecasts must be decomposed into both battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) forecasts. There is sufficient evidence that the adoption processes for these two types of EVs differ.</p> <p>Critically, we extend this analysis to consider the factors influencing adoption. Utilizing the adoption forecasts, we perform spatial regression analyses on the parameters that define the forecast shapes. We examine how multiple sociodemographic, land use, and charging measures correlate with the rate of EV adoption and the lateral shift of early EV adoption.</p> <p>Crucially, we find that multiple measures of charging infrastructure availability correspond with increased adoption; of these, a variation on the distance to fast-charging stations is the most consistent metric across final models. We additionally find that land use type is indeed relevant to adoption. Finally, we are able to corroborate at a granular spatial level numerous sociodemographic variables from the literature.</p> <p>Ultimately, this research can provide valuable insights into adoption trends at a local level and what factors may be best leveraged to promote adoption.</p>

<p dir="ltr">Vehicle electrification has been proposed as one of the most important technologies for the future of sustainable energy and climate change mitigation. These electric vehicles (EVs) are predominantly powered by lithium-ion batteries (LIBs) which contain critical materials — lithium (Li), cobalt (Co), nickel (Ni), manganese (Mn), and graphite — that are in short supply. Maximizing resource efficiency through material recovery is crucial for a circular economy and the long-term financial, environmental, and social sustainability of the EV industry.</p><p><br></p><p dir="ltr">Heavily influenced by technology, business, and policy, the EV ecosystem must balance the interests of multiple stakeholders. There is a system-of-systems dependency between the circular business model employed; the process, scale, and impact of operations; and the overall economy of the operating environment. However, these linkages are highly dependent on the technological process for material recovery. Given that proof-of-concept research methodologies in the academy are typically low-complexity technologies (low-tech) and at a low technological readiness level (TRL), economies of scale, environmental impacts, and policy implications are not readily deduced.</p><p><br></p><p dir="ltr">Two practical low-tech and low TRL methods for cathode material recovery and cell reattachment for extended battery usage were developed as proofs-of-concept. One theoretical approach for cell removal using heat application was also explored. Given that artisanal mining plays a significant role in the upstream battery material supply chain and is often carried out on a small scale with common tools, safe manual disassembly processes through low-tech, low TRL methods for environmentally friendly battery material recovery could be influential in the downstream management of end-of-life (EOL) EVs.</p><p><br></p><p dir="ltr">Another recommendation is to treat lithium-ion batteries and current recycling methods as transitory technologies, thus encouraging investments in low-tech methods as part of effective business practices today. Vertical integration and supply chain partnerships by companies to recover legacy batteries could be more beneficial in the short term than investing large amounts of capital in new recycling facilities of whose features they are unsure. Higher-complexity and TRL methods can be developed as part of new growth engines for future businesses.</p><p dir="ltr">Finally, the major policy observation is the recognition that state level involvement in setting up appealing environments for private companies is a major contributor to attracting investments for local economic growth, thus necessitating the need for stronger multistakeholder engagement and collaboration in workforce development and environmental safety. Without adequate workforce development and retention programs, companies will struggle to meet and keep the labor requirements necessary to take advantage of tax credits, which could hinder their desire to set up shop in certain states.</p>

This paper reviews the Open Charge Point Protocol (OCPP) and implements a Charging Station Management System (CSMS) targeting heavy-duty trucks. The new technique proposed in this paper is designed to maximize Electric vehicle (EV) owner benefits by charging at a low cost, and also the electric utility benefits (operating the system within the acceptable limits) by proper choice of electricity tariff structure. EV owners can be motivated to charge at off-peak hours which have low electricity prices and stop charging at peak hours which have high electricity prices. / Detta dokument granskar Open Charge Point Protocol (OCPP) och implementerar en Charging Station Management System (CSMS) inriktat på tunga lastbilar. Den nya tekniken som föreslås i detta dokument är utformad för att maximera fördelarna för ägare av elfordon (EV) genom att ladda till en låg kostnad, och även fördelarna med elnätet (drift av systemet inom acceptabla gränser) genom korrekt val av elprisstruktur. Elbilsägare kan motiveras att ladda under lågtrafik som har låga elpriser och sluta ladda under rusningstid som har höga elpriser.

Energy-efficient powertrain components and advanced vehicle control strategies are two effective methods to promote the potential of hybrid electric vehicles (HEVs). Aiming at hybrid system efficiency improvement, this thesis presents a comprehensive review of energy-efficient hybrid powertrain specific engines and proposes three improved energy management strategies (EMSs), from a basic non-adaptive real-time approach to a state-of-the-art learning-based intelligent approach. To evaluate the potential of energy-efficient powertrain components in HEV efficiency improvement, a detailed discussion of hybrid powertrain specific engines is presented. Four technological solutions, i.e., over-expansion cycle, low temperature combustion mode, alternative fuels, and waste heat recovery techniques, are reviewed thoroughly and explicitly. Benefits and challenges of each application are identified, followed by specific recommendations for future work. Opportunities to simplify hybrid-optimized engines based on cost-effective trade-offs are also investigated. To improve the practicality of HEV EMS, a real-time equivalent consumption minimization strategy (ECMS)-based HEV control scheme is proposed by incorporating powertrain inertial dynamics. Compared to the baseline ECMS without such considerations, the proposed control strategy improves the vehicle drivability and provides a more accurate prediction of fuel economy. As an improvement of the baseline ECMS, the proposed dynamic ECMS offers a more convincing and better optimal solution for practical HEV control. To address the online implementation difficulty faced by ECMS due to the equivalence factor (EF) tuning, a predictive adaptive ECMS (A-ECMS) with online EF calculation and instantaneous power distribution is proposed. With a real-time self-updating EF profile, control dependency on drive cycles is reduced, and the requirement for manual tuning is also eliminated. The proposed A-ECMS exhibits great charge sustaining capabilities on all studied drive cycles with only slight increases in fuel consumption compared to the basic non-adaptive ECMS, presenting great improvement in real-time applicability and adaptability. To take advantage of machine learning techniques for HEV EMS improvement, a deep reinforcement learning (DRL)-based intelligent EMS featuring the state-of-the-art asynchronous advantage actor-critic (A3C) algorithm is proposed. After introducing the fundamentals of reinforcement learning, formulation of the A3C-based EMS is explained in detail. The proposed algorithm is trained successfully with reasonable convergence. Training results indicate the great learning ability of the proposed strategy with excellent charge sustenance and good fuel optimality. A generalization test is also conducted to test its adaptability, and results are compared with an A-ECMS. By showing better charge sustaining performance and fuel economy, the proposed A3C-based EMS proves its potential in real-time HEV control. / Thesis / Doctor of Philosophy (PhD)

The growing demand of electrified light duty trucks, including sports utility vehicles (SUV) require high performance motors to surpass form their internal combustion engine counterparts. The Axial Flux Permanent Magnet (AFPM) Motor is expected to be one of the leading technologies to meet the demands of these industries due to its efficenct and high torque and power density. Designing a robust thermal management system for this motor is key to utilizing these performance benefits. To meet these demanding conditions, additive manufacturing is expected to play a critical role in enhancing performance. Additively manufactured hollow coil is a cooling strategy to extract heat directly from the hottest part of the motor, the stator. The following research assesses the viability of the design in a prototype motor. ANSYS CFX is used to characterize the pressure drop and flowrate, and a test setup is used to validate the results. The challenges associated with integrating the solution into a motor is highlighted as well as design issues during design development. Finally, the integration of a parallel hybrid SUV using an AFPM motor is documented and the challenges with integration into a vehicle is explained. / Thesis / Master in Advanced Studies (MAS)

The automotive industry is inevitably experiencing a paradigm shift from fossil fuels to electric powertrain with significant technological breakthroughs in vehicle electrification. Emerging hybrid electric vehicles were one of the first steps towards cleaner and greener vehicles with a higher fuel economy and lower emission levels. The energy management strategy in hybrid electric vehicles determines the power flow pattern and significantly affects vehicle performance. Therefore, in this thesis, a learning-based strategy is proposed to address the energy management problem of a hybrid electric vehicle in various driving conditions. The idea of a deep recurrent neural network-based energy management strategy is proposed, developed, and evaluated. Initially, a hybrid electric vehicle model with a rule-based supervisory controller is constructed for this case study to obtain training data for the deep recurrent neural network and to evaluate the performance of the proposed energy management strategy. Secondly, due to its capabilities to remember historical data, a long short-term memory recurrent neural network is designed and trained to estimate the powertrain control variables from vehicle parameters. Extensive simulations are conducted to improve the model accuracy and ensure its generalization capability. Also, several hyper-parameters and structures are specifically tuned and debugged for this purpose. The novel proposed energy management strategy takes sequential data as input to capture the characteristics of both driver and controller behaviors and improve the estimation/prediction accuracy. The energy management controller is defined as a time-series problem, and a network predictor module is implemented in the system-level controller of the hybrid electric vehicle model. According to the simulation results, the proposed strategy and prediction model demonstrated lower fuel consumption and higher accuracy compared to other learning-based energy management strategies. / Thesis / Master of Applied Science (MASc)

The Hybrid Electric Vehicle (HEV) is a complex electromechanical system with complex interactions among various components. Due to the large number of design variables involved, the design flexibility in the HEV makes performance studies difficult. As the system complexity and sophistication increases, it becomes much more difficult to predict these interactions and design the system accordingly. Also, different variations in the design and manufacture of various components and systems involve a large amount of work and cost to keep updated of all these variations. While the above issues ask for a flexible design environment suitable for vehicle design, most of the existing powertrain design tools are based on experiential models, such as look-up tables, which use idealized assumptions and limited experimental data. The accuracy of the results produced by these tools is not good enough for designing these new generation vehicles. Also, sometimes the designs may lead to components or systems beyond physical limitations. To make the powertrain design more efficient, the models developed must be closely related to the underlying physics of the components. Only such physics-based models can facilitate high fidelity simulations for dynamics at different time scales. This results in the quest for a design tool that manages the vehicle?s development process while maintaining tight integration between the software and physical artifacts. The thesis addresses the above issues and focuses on the modeling of HEV using model integrated computing and employing physics-based resistive companion form modeling method. For this purpose, Generic Modeling Environment (GME), software developed by Institute of Software and Integrated Systems (ISIS), Vanderbilt University is used as the platform for developing the models. A modeling environment for hybrid vehicle design is prepared and a Battery Electric Vehicle (BEV) is developed as an application of the developed environment. Resistive companion form models of various BEV components are prepared and a model interpreter is prepared for integrating the developed component models and simulating the design.

The rise in overall powertrain complexity and the stringent performance requirements of a hybrid electric vehicle (HEV) have elevated the role of its powertrain control strategy to considerable importance. Iterative modeling and simulation form an integral part of the control strategy design process and industry engineers rely on proprietary ?legacy? models to rapidly develop and implement control strategies. However, others must initiate new algorithms and models in order to develop production-capable control systems. This thesis demonstrates the development and validation of a charge-sustaining control algorithm for a through-the-road (TTR) parallel hybrid (diesel-electric) powertrain. Some unique approaches used in powertrain-level control of other commercial and prototype vehicles have been adopted to incrementally develop this control strategy. The real-time performance of the control strategy has been analyzed through on-road and chassis dynamometer tests over several standard drive cycles. Substantial quantitative improvements in the overall HEV performance over the stock configuration, including better acceleration and fuel-economy have been achieved.