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In searching for more efficient vehicles with lower carbon emissions, researchers have invested enormous time and resources in designing new materials, components, systems, and control methods. The result is not only an immense volume of publications and patents but also a true electrification revolution in the transportation sector. Although the advancements are remarkable, much is still to be developed. Energy management systems are often designed to fulfil drive cycles that represent just a fraction of the actual use of the vehicles, disregarding essential factors such as driving conditions that may vary in real life. Furthermore, control algorithms should not ignore one of the most relevant driving aspects, comfort. Driving should be a pleasant activity since we spend much time of our lives performing this task. This research proposes a novel algorithm for managing energy consumption in electrified vehicles, the regen-based equivalent consumption minimization strategy (R-ECMS). Its suitability for solving the power-split problem is evaluated. Experiments emulating labelling schedules are conducted considering an example application. Robustness to different drive cycles and flexibility of the algorithm to various modes of operation are assessed. Furthermore, the method is integrated into an autonomous longitudinal control. The function leverages vehicle dynamics and journey mapping to assure energy efficiency and adequate drivability. Finally, the tests are conducted using human-driven cycles leveraging driving simulation technology. That allows for including driver subjective feelings in the design and assessing the algorithm's performance in realistic driving conditions. / Thesis / Doctor of Philosophy (PhD)

Problem Discussion: In the Swedish context, 74 per cent of transports are within regional borders, meaning that transportation departed in a specific region seldom leaves the region it departed from. This condition makes it possible to transition many trucks in the Swedish transportation fleet to electric. It was found that the increasing interest in transitioning to the electrification of transportation, as well as the choice of technology, reflects the need for transportation actors to make a decision regarding which type of electrification technology to implement into their operations. Purpose: The aim of the study is to explore the connection between the choice of different electrification technologies and the transportation actors' business models for different local and regional heavy truck applications in Sweden. Methodology: A qualitative case study was selected with an abductive approach. The data collection was performed by semi-structured interviews with both transporters, who operate the vehicles, and with their surrounding system actors. The analysis method that was used was thematic analysis. Analysis: From the collected data, some influential aspects were found to have an impact on the choices of technology and/or business model components. These were further explored and analyzed in relation to the respective technology and the transporters' business models to derive connecting factors as findings. Findings and Conclusion: In total, five connecting factors were derived. These were the baseline/investment cost of complements, flexibility, load capacity and range, new ownership models, and closeness of partners. The flexibility factor offered an additional interpretation of the theoretical perspective on alignment structures by bundling customers to ensure the deliverability of the proposed value. The connecting factors were found to have a small but not substantial difference in effect between the applications of regional-, construction-, and waste collecting transports.

<p>The world transportation sector has been relying on the oil industry for more than a hundred years, accounting for the largest oil consumption and one third of the greenhouse gas emissions. However, with the boosting demand, escalating national energy security concerns and emerging environmental issues, reducing and displacing petroleum fuel in transportation sector has become an urging global target. As a result, hybrid electric vehicles evolve as one solution to displace petroleum fuel by utilizing vehicle onboard electrical systems, achieving higher fuel economy and less emissions by vehicle electrification and hybridization.</p> <p>However, since hybrid electric vehicles add additional electrical components and systems to realize better fuel economy, the system complexity increases and thus the cost increases. Hence, it is an objective of this thesis research to focus on the integrations and optimizations, aiming to simplify and optimize the hybrid power-trains in both system level and component level.</p> <p>This thesis contributes to a novel integrated electro-mechanical hybrid transmission that is potentially more compact and more operational flexible with fewer components compared to the GM Allison Two-Mode hybrid transmission. Comprehensive commercialized power-train transmissions are reviewed and analyzed to serve as background information for comparison. It also contributes to a family of double-rotor switched reluctance machines that are more integrated and suitable for hybrid electric vehicle applications. A prototype double-rotor switched reluctance machine has been built and tested for concept proving. Detailed machine design process is reported with the emphasis on design novelties. Finite element analysis and optimization techniques are applied and the accuracy is confirmed by the experiments. In addition, methods of machine loss analysis, thermal analysis and drive analysis are established; manufacturing and testing procedures are documented in detail that can be used for future machine designs guidance.</p> / Doctor of Philosophy (PhD)

Concerns with emissions, CO2 in particular, and energy resource associated with conventional internal combustion engine (ICE) vehicles is motivating a shift towards more electrified power-trains for road transportation, as well as other transportation applications. The modelling, characterization and design of electrified power-trains, including energy storage technologies, traction machine technologies and their associated power electronics, are discussed in this thesis. Port cranes are a special case of land transportation encompassing many of the power-train objectives found common with road based hybrid electric vehicles; here a port crane system is studied. The power flow for a typical crane loading cycle is analyzed and the value of the energy consumption and saving potential is calculated. Then alternative energy storage applications are considered for hybrid power-train configurations employing diesel engine generators, battery packs, supercapacitors (SCs), and flywheels. A hybrid rubber tyred gantry crane (RTGC) power-train model with power management is developed and the battery-SC hybrid energy storage systems are designed for both short- and long-period operation. The Induction machine (IM) is a popular technology for traction applications. Although many publications discuss IM design to realize a traction torque-speed characteristic, the IM model is studied to determine the main parameters impacting on the machine performance capability at constant torque and extended speed. Based on the model analysis, an IM design procedure for traction applications is proposed which improves machine performance capability. The machine design parameters are normalized in per unit form and hence the proposed design procedure is applicable across different ratings. In the specification and definition of vehicle power-trains, it is common (in industry) to quote data at specific operating conditions, for example, full or fixed battery terminal voltage and system temperature. The interactive influence between energy storage devices and the vehicle system is investigated. Using the all-electric Nissan Leaf power-train as a reference example, the Nissan Leaf traction system is evaluated and performance assessed by considering DC-link voltage variation from battery full state of charge (SoC) to zero SoC and temperature variations typical of an automotive application, showing that the system stated performance is reduced as battery SoC decreases. An alternative traction machine design is proposed to satisfy the vehicle target performance requirements over the complete variation of SoC. The vehicle power-train is then modified with the inclusion of a DC/DC converter between the vehicle battery and DC-link to maintain the traction system DC-link voltage near constant. A supercapacitor system is also considered for improved system voltage management. The trade-offs between the actual Nissan Leaf power-train and the redesigned systems are discussed in terms of electronic and machine packaging, and mitigation of faulted operation at high speeds. Using the Nissan Leaf interior permanent magnet (IPM) machine as the benchmark machine, an example surface permanent magnet (SPM) machine, with same design constraints, is designed and compared with the benchmark IPM machine. The phase voltage distortion of IPM and SPM machines are compared and the mechanisms are revealed. An alternative machine topology with pole shoe rotor is proposed for reduction of machine peak current rating and voltage distortion. The pole shoe topology is common in industrial variable speed drives employing constant torque regimes, but not for traction. Here, the machine with pole shoe rotor is designed to achieve traction performance. The pole shoe concept for vehicle traction is significantly different from existing practice in the electric and hybrid electric automotive industry and thus departure in standard design is a contribution of this thesis. / Thesis / Doctor of Philosophy (PhD)

Yes / This paper presents a robust framework for active and reactive power management in distribution networks using electric vehicles (EVs). The method simultaneously minimizes the energy cost and the voltage deviation subject to network and EVs constraints. The uncertainties related to active and reactive loads, required energy to charge EV batteries, charge rate of batteries and charger capacity of EVs are modeled using deterministic uncertainty sets. Firstly, based on duality theory, the max min form of the model is converted to a max form. Secondly, Benders decomposition is employed to solve the problem. The effectiveness of the proposed method is demonstrated with a 33-bus distribution network.

Formula SAE (FSAE) vehicle systems are very complex. Understanding how subsystems effect the overall vehicle is essential for making design trade-offs. FSAE is a competitive environment. Teams need to have reliable and high performing vehicles to do well in competition. The Virginia Tech (VT) FSAE team has produced a prototype electric powertrain (EPT) vehicle, VTM16e, and will take their first EPT vehicle, VTM17e, to competition in 2017. The use of model-based design (MBD) for an EPT FSAE vehicle is investigated through this thesis. The goal of the research is to build the framework of a full vehicle simulation to take knowledge gained from the VTM16e prototype vehicle, and apply it to the VTM17e competition vehicle. A top-down, bottom-up approach is taken to build a full vehicle model of an EPT FSAE vehicle. A full vehicle simulation is built with subsystems to establish an overall structure and subsystem interactions. Individual subsystems are then focused on for testing and validation. Breaking the vehicle down into subsystems allows the overall model to be incrementally improved. The battery subsystem is focused on in this thesis. Extensive testing is performed on the batteries to characterize their performance. Computer models are generated from empirical data through parameter estimation techniques. Validation of the battery models is performed and the resulting model is incorporated into the overall vehicle model. Performance limits of the vehicle are determined through model exploration, and design modifications to increase the reliability and performance for the VTM17e vehicle are proposed. / Master of Science

Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired for providing quick, high-level estimations for a BEV without requiring extensive vehicle and computational resources. Therefore, the goal of this paper is to create a simple, yet reliable vehicle model, that can estimate the energy consumption of most, if not all, electric vehicles on the market by using parameter normalization techniques. These vehicle parameters include the vehicle test weight and performance to obtain a unified net Willans line to describe the input/output power through a linear relationship. A base model and three normalized models are developed by fitting the UDDS and HWFET energy consumption test data published by the EPA for all BEVs in the U.S. market. Out of the models analyzed, the normalization with weight performs best with the lowest RMSE values at 0.384 kW, 0.747 kW, and 0.988 kW for predicting the UDDS, HWY, and US06 data points, respectively, and 0.653 kW for all three data sets combined. Consideration of accessory loads at 0.5 kW improves the model normalized by weight and performance by a reduction of over 20% in RMSE for predictions with all data sets combined. Removing outliers in addition to consideration of accessory loads improves the model normalized by weight and performance by a reduction of over 36% in RMSE for predictions with all data sets combined. Overall, results suggest that a unified net Willans line is largely achievable with accessible energy consumption data on U.S. regulatory cycles. / Master of Science / Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from conventional internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired to support quick, high-level analyses for BEVs without requiring expensive resources. Therefore, the goal of this paper is to create a simple vehicle model that can estimate the energy consumption of most, if not all, electric vehicles by scaling the data using vehicle parameters. These parameters include the vehicle test weight and performance to obtain a unified net Willans line model describing the input/output power through a linear relationship. The UDDS (city) and HWFET (highway) energy consumption data points used to develop the model are easily accessible from published EPA data. Out of the models analyzed, the normalization with test weight performs best with the lowest error values at 0.384 kW, 0.747 kW, and 0.988 kW for predicting the UDDS, HWFET, and US06 (aggressive city/highway cycle) data points, respectively, and 0.653 kW for all three data sets combined. Consideration of accessory loads at 0.5 kW improves the model normalized by weight and performance by a reduction of over 20% in error for predictions with all data sets combined. Removing outliers in addition to consideration of accessory loads improves the model normalized by weight and performance by a reduction of over 36% in error for predictions with all data sets combined. Overall, results suggest that a unified net Willans line is largely achievable with accessible energy consumption data on U.S. regulatory cycles.