Temperature is one of the most important parameters for the performance, safety, and aging of lithium-ion batteries and has been linked to all main barriers for widespread commercial success of electric vehicles. The aim of this thesis is to highlight the importance of temperature effects, as well as to provide engineering tools to study these. The mass transport phenomena of the electrolyte with LiPF6  in EC:DEC was fully characterized in between 10 and 40 °C and 0.5 and 1.5 M, and all mass transport properties were found to vary strongly with temperature. A superconcentrated electrolyte with LiTFSI in ACN was also fully characterized at 25 °C, and was found to have very different properties and interactions compared to LiPF6  in EC:DEC. The benefit of using the benchmarking method termed electrolyte masstransport resistivity (EMTR) compared to using only ionic conductivity was illustrated for several systems, including organic liquids, ionic liquids, solid polymers, gelled polymers, and electrolytes containing flame-retardant additives. TPP, a flame-retardant electrolyte additive, was evaluated using a HEV load cycle and was found to be unsuitable for high-power applications such as HEVs. A large-format commercial battery cell with a thermal management system was characterized using both experiments and a coupled electrochemical and thermal model during a PHEV load cycle. Different thermal management strategies were evaluated using the model, but were found to have only minor effects since the limitations lie in the heat transfer of the jellyroll. / Temperatur är en av de viktigaste parametrarna gällande ett litiumjonbatteris prestanda, säkerhet och åldring och har länkats till de främsta barriärerna för en storskalig kommersiell framgång för elbilar. Syftet med den här avhandlingen är att belysa vikten av temperatureffekter, samt att bidra med ingenjörsverktyg att studera dessa. Masstransporten för elektrolyten LiPF6  i EC:DEC karakteriserades fullständigt i temperaturintervallet 10 till 40 °C för LiPF6-koncentrationer på 0.5 till 1.5 M. Alla masstransport-egenskaper fanns variera kraftigt med temperaturen. Den superkoncentrerade elektrolyten med LiTFSI i ACN karakteriserades även den fullständigt vid 25 °C. Dess egenskaper och interaktioner fanns vara väldigt annorlunda jämfört med LiPF6  i EC:DEC. Fördelen med att använda utvärderingsmetoden elektrolytmasstransportresistivitet (EMTR) jämfört med att endast mäta konduktivitet illustrerades för flertalet system, däribland organiska vätskor, jonvätskor, fasta polymerer, gellade polymerer, och elektrolyter med flamskyddsadditiv. Flamskyddsadditivet TPP utvärderades med en hybridbils-lastcykel och fanns vara olämplig för högeffektsapplikationer, som hybridbilar. Ett kommersiellt storformatsbatteri med ett temperatur-kontrollsystem karakteriserades med b.de experiment och en kopplad termisk och elektrokemisk modell under en lastcykel utvecklad för plug-inhybridbilar. Olika strategier för kontroll av temperaturen utvärderades, men fanns bara ha liten inverkan på batteriets temperatur då begränsningarna för värmetransport ligger i elektrodrullen, och inte i batteriets metalliska ytterhölje. / <p>QC 20150522</p> / Swedish Hybrid Vehicle Center

Renewable and green energy development is vigorously supported by most countries to suppress the continuously increasing greenhouse gas (GHG) emissions. However, as the total renewable capacity expands, the growth rate of emissions is not effectively restrained. An unforeseen factor contributing to this growth is the regulation service, which aims to mitigate power frequency deviations caused by the intermittent renewable power generation and unbalanced power supply and demand. Regulation services, normally issued by supply-side balancing authorities, leads to inefficient operations of regulating generators, thus directly contributing to the emissions growth. Therefore, it is urged to find solutions that can stabilize the power frequency with an increased energy using efficiency. Demand response (DR) is an ideal candidate to solve this problem. The current smart grid infrastructure enables a high penetration of smart residential electric loads, including heating, ventilation, and air conditioning systems (HVACs), air conditioners (A/Cs), electric water heaters (EWHs), and plug-in hybrid electric vehicles (PHEVs). Beyond simply drawing power from the grid for local electric demand, those loads can also adjust their power consumption patterns by responding to the control signals sent to them. It has been proved that, if appropriately aggregated and controlled, power consumption of demand-side residential loads possesses a huge potential for providing regulation services. The research of DR is pivotal from the the application perspective due to the efficient usage of renewable energy generation and the high power quality. However, many problems remain open in this area due to the load heterogeneity, device physical constraints, and computational and communication restrictions. In order to move one step further toward industry applications, this PhD thesis is concerned with two cruxes in DR program design: Aggregation Modeling and Control; it deals with two main types of terminal loads: Thermostatically Controlled Appliances (TCAs) (Chapters 2-4) and PHEVs (Chapter 5). This thesis proceeds with Chapter 1 by reviewing the state-of-the-art of DR. Then in Chapter 2, the focus is put on modeling and control of TCAs for secondary frequency control. In order to explicitly describe local TCA dynamics and to provide the aggregator a clear global view, TCAs are aggregated by directly stacking their individual dynamics. Terminal TCAs are assumed in a general case that an arbitrary number of TCAs are equipped with varying frequency drives (VFDs). A centralized model predictive control (MPC) scheme is firstly constructed. In the design, to tackle the TCA lockout effect and to facilitate the MPC scheme, a novel approach for converting time-integrated interdependent logic constraints into inequality constraints are proposed. Since a centralized MPC scheme may introduce non-trivial computational load by using this aggregation model, especially when the number of TCAs increases, a distributed MPC (DMPC) scheme is proposed. This DMPC scheme is validated through a more practical case study that all TCAs are subject to pure ON/OFF control. Chapter 3 targets on aggregation modeling and control of TCAs for the provision of primary frequency control. To efficiently reduce the computational load to facilitate the primary frequency control, the explicit monitoring of terminal TCAs must be compromised. To this end, a 2-D population-based model is proposed, in which TCAs are clustered into state bins according to their temperature information and running status. Within the proposed aggregation framework, individual TCA dynamics' evolutions develop into TCA population migration probabilities, thus the computational load of the centralized controller is dramatically reduced. Based on this model, a centralized MPC scheme is proposed for the primary frequency control. The previously proposed population-based model provides a promising direction for the centralized control. However, in traditional population-based model, TCA lockout effect can only be considered when implementing the control signals. This will cause a mismatch between the nominal control signals and the actually implemented ones. To conquer this, in Chapter 4, an improved population-based model is studied to explicitly formulate the TCA lockout effect in the aggregation model. A DMPC scheme is firstly constructed based on this model. Furthermore, since the predictions of regulation signals may not be available or they may include severe disturbances, a control scheme that does not require future regulation signals is urged. To this end, an optimal control scheme, in which a novel penalty is included to maximize the regulation capability, is proposed to facilitate the most practical scenario. Another type of terminal loads that has a huge potential in providing grid services is PHEV. At this point, Chapter 5 presents the aggregation and charging control of heterogeneous PHEVs for the provision of DR. In contrast to using battery state-of-charge (SOC) solely as the system state, a new aggregation model is proposed by introducing a novel concept, i.e., charging requirement index. This index combines the SOC with drivers' specified charging requirements, thus inherently providing the aggregation model with richer information. A centralized MPC scheme is proposed based on this novel model. Both of the model and controller are validated through an overnight valley-filling case study. Finally, the conclusions of the thesis are summarized and future research topics are presented. / Graduate / 0537 / 0544 / 0548 / mingxiliu419@gmail.com

This thesis evaluates emerging electric vehicle technology and estimates what effect it might have on how an oil company decides on its gas station network. It is conducted using data from South Korea, a country poised for a fast adoption of electric vehicles. The study first reviews the literature to gather reasonable cases of electric vehicle penetration. Also, after researching technology-diffusion theories, the study selects a model that can well explain the literature review data. The scenarios induced by this function are utilized as the main uncertainties confronting an oil company’s network decision model. Based on a probabilistic simulation, the study finds that the effects of technology diffusion alter the priority order of an oil company’s network decision alternatives. Namely, after the overall uncertainty level rises, directly owning gas station, with its heavy initial investment, is not preferred for an oil company’s network strategy. From the result, the study also estimates the scale of the new technology’s effect. Such effect is found to be significant enough to alter a part of an oil company’s retail strategy. Nevertheless, such effect cannot be shown to be so great as to change the current retail oil market structures. / text

Connected and Autonomous Vehicles (CAVs) benefit from both connectivity between vehicles and city infrastructures and automation of vehicles. In this respect, CAVs can improve safety and reduce traffic congestion and environmental impacts of daily commutes through making collaborative decisions. This dissertation studies how to reduce the energy consumption of vehicles and traffic congestion by making high-level routing decisions of CAVs. The first half of this dissertation considers the problem of eco-routing (finding the energy-optimal route) for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. Several algorithms are proposed that can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. The results show significant energy savings for PHEVs with a near real-time execution time for the algorithms. The second half of this dissertation tackles the problem of routing for fleets of CAVs in the presence of mixed traffic (coexistence of regular vehicles and CAVs). In this setting, all CAVs belong to the same fleet and can be routed using a centralized controller. The routing objective is to minimize a given overall fleet traveling cost (travel time or energy consumption). It is assumed that regular vehicles (non-CAVs) choose their routing decisions selfishly to minimize their traveling time. A framework is proposed that deals with the routing interaction between CAVs and regular uncontrolled vehicles under different penetration rates (fractions) of CAVs. The results suggest collaborative routing decisions of CAVs improve not only the cost of CAVs but also that of the non-CAVs. This framework is further extended to consider congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility under mixed traffic conditions. A network flow model is devised to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. The results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, while the combination of AMoD with walking or micromobility options can significantly improve the overall system performance.

During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range. / MS

Mississippi State University was selected for participation in the EcoCAR 3 Advance Vehicle Technology Competition. The team designed its architecture around the use of two UQM electric motors, and a Weber MPE 850cc turbocharged engine. To combine the three inputs into a singular output a custom gearbox was designed with seven helical gears. The gears were designed to handle the high torque and speeds the vehicle would experience. The use of this custom gearbox allows for a variety of control strategies. By optimizing the torque supplied by each motor, the overall energy consumption of the vehicle could be reduced. Additionally, studies were completed on the engine to understand the effects of injecting water into the engine’s intake manifold at 25% pedal request from 2000-3500 rpm. Overall, every speed showed an optimum at 20% water to fuel ratio, which obtained reductions in brake specific fuel consumption of up to 9.4%.

This work presents novel modeling and experimental techniques that provide insight into liquid-phase mass transport and electron transfer processes in lithium-ion batteries. These included liquid-phase ionic mass transport (conduction and diffusion), lithium diffuion in the solid phase and electronic transport in the solid phase. Fundamental understanding of these processes is necessary to efficiently design and optimize lithium-ion batteries for different applications. To understand the effect of electrode structure on the electronic resistance of the cathode, we tested power performance of cathodes with combinations of three different carbon conductivity additives: vapor-grown carbon fibers (CF), carbon black (CB) and graphite (GR). With all other factors held constant, cathodes with a mixture of CF+CB were found to have the best power-performance, followed by cells containing CF only and then by CB+GR. Thus, the use of carbon fibers as conductive additive was found to improve the power performance of cells compared to the baseline (CB+GR). The enhanced electrode performance due to the fibers also allows an increase in energy density while still meeting power goals. About one-third of the available energy was lost to irreversible processes when cells were pulse-charged or discharged at the maximum rate allowed by voltage-cutoff constraints. We developed modeling and experimental techniques to quantify tortuosity in electrolyte-filled porous battery structures (separator and active-material film). Tortuosities of separators were measured by two methods, AC impedance and polarization-interrupt, which produced consistent results. The polarization-interrupt experiment was used similarly to measure effective electrolyte transport in porous films of cathode materials, particularly films containing lithium iron phosphate. An empirical relationship between porosity and the tortuosity of the porous structures was developed. Our results demonstrate that the tortuosity-dependent mass transport resistance in porous separators and electrodes is significantly higher than that predicted by the oft-used Bruggeman relationship. To understand the dominant resistances in a lithium battery, we developed and validated a model for lithium iron phosphate cathode. In doing so we considered unique physical features of this active material. Our model is unusual in terms of the range of experimental conditions for which it is validated. Various submodel and experimental techniques were used to find physically realistic parameters. The model was tested with different discharge rates and thicknesses of cathodes, in all cases showing good agreement, which suggests that the model takes into account physical realities with different thicknesses. The model was then used to find the dominant resistance for the tested cathodes. The model suggests that the inter-particle contact resistance between carbon and the active-material particles was a dominant resistance for the tested cathodes.

In today’s world of volatile fuel prices and climate concerns, there is little study on the relation between vehicle ownership patterns and attitudes toward potential policies and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s household-fleet evolution. Results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are vehicle purchase price, type/class, and fuel economy (with 30%, 21% and 19% of respondents placing these in their top three). Most (56%) respondents also indicated that they would seriously consider purchasing a Plug-In Hybrid Electric Vehicle (PHEV) if it were to cost $6,000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings. 25-year simulations suggest that 19% of Austin’s vehicle fleet could be comprised of Hybrid Electric Vehicles (HEVs) and PHEVs under adoption of a feebate policy (along with PHEV availability in Year 1 of the simulation, and current gas prices throughout). Under all scenarios vehicle usage levels (in total vehicle miles traveled [VMT]) are predicted to increase overall, along with average vehicle ownership levels (per household, and per capita); and a feebate policy is predicted to raise total regional VMT slightly (just 4.43 percent, by simulation year 25), relative to the trend scenario, while reducing CO2 emissions only slightly (by 3.8 percent, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 17% and CO2 emissions by 22% (relative to trend). Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. And HEVs, PHEVs and Smart Cars are estimated to represent a major share of the fleet’s VMT (25%) by year 25 under the feebate scenario. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet), yet feebate-policy receipts exceed rebates in each simulation year. A 15% reduction in the usage levels of SUVs, CUVs and minivans is observed in the $5/gallon scenario (relative to trend). Mean use levels per vehicle of HEVs and PHEVs are simulated to have a variation of 753 and 495 across scenarios. In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have even more significant effects on energy dependence and greenhouse gas emissions. / text

Transportation fleet emissions have a dominant role in air quality because of their significant contribution to ozone precursor and greenhouse gas emissions. Regulatory policies have emphasized improvements in vehicle fuel economy, alternative fuel use, and engine and vehicle technologies as approaches for obtaining transportation systems that support sustainable development. This study examined the air quality impacts of the partial electrification of the transportation fleet and the use of biofuels for the Austin Metropolitan Statistical Area under a 2030 vision of regional population growth and urban development using the Comprehensive Air Quality Model with extensions (CAMx). Different strategies were considered including the use of Plug-in Hybrid Electric Vehicles (PHEVs) with nighttime charging using excess capacity from electricity generation units and the replacement of conventional petroleum fuels with different percentages of the biofuels E85 and B100 along or in combination. Comparisons between a 2030 regional vision of growth assuming a continuation of current development trends (denoted as Envision Central Texas A or ECT A) in the Austin MSA and the electrification and biofuels scenarios were evaluated using different metrics, including changes in daily maximum 1-hour and 8-hour ozone concentrations, total area, time integrated area and total daily population exposure exceeding different 1-hour ozone concentration thresholds. Changes in ozone precursor emissions and predicted carbon monoxide and aldehyde concentrations were also determined for each scenario. Maximum changes in hourly ozone concentration from the use of PHEVs ranged from -8.5 to 2.2 ppb relative to ECT A. Replacement of petroleum based fuels with E85 had a lesser effect than PHEVs on maximum daily ozone concentrations. The maximum reduction due to replacement of 100% of gasoline fuel in light and heavy duty gasoline vehicles by E85 ranged from -2.1 to 2.8 ppb. The magnitude of the effect was sensitive to the biofuel penetration level. Unlike E85, B100 negatively impacted hourly ozone concentrations relative to the 2030 ECT A case. As the replacement level of petroleum-diesel fuel with B100 in diesel vehicles increased, hourly ozone concentrations increased as well. However, changes due to the penetration of B100 were relatively smaller than those due to E85 since the gasoline fraction of the fleet is larger than the diesel fraction. Because of the reductions in NOx emissions associated with E85, the results for the biofuels combination scenario were similar to those for the E85 scenario. Also, the results showed that as the threshold ozone concentration increased, so too did the percentage reductions in total daily population exposure for the PHEV, E85, and biofuel combination scenarios relative to ECT A. The greatest reductions in population exposure under higher threshold ozone concentrations were achieved with the E85 100% and 17% PHEV with EGU controls scenarios, while the B100 scenarios resulted in greater population exposure under higher threshold ozone concentrations. / text

Today, hybrid electric propulsion technology provides a promising and practical solution for improving vehicle performance, increasing energy efficiency, and reducing harmful emissions, due to the additional flexibility that the technology has provided in the optimal power control and energy management, which are the keys to its success. In this work, a systematic approach for real-time optimal energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) has been introduced and validated through two HEV/PHEV case studies. Firstly, a new analytical model of the optimal control problem for the Toyota Prius HEV with both offline and real-time solutions was presented and validated through Hardware-in-Loop (HIL) real-time simulation. Secondly, the new online or real-time optimal control algorithm was extended to a multi-regime PHEV by modifying the optimal control objective function and introducing a real-time implementable control algorithm with an adaptive coefficient tuning strategy. A number of practical issues in vehicle control, including drivability, controller integration, etc. are also investigated. The new algorithm was also validated on various driving cycles using both Model-in-Loop (MIL) and HIL environment. This research better utilizes the energy efficiency and emissions reduction potentials of hybrid electric powertrain systems, and forms the foundation for development of the next generation HEVs and PHEVs. / Graduate / laindeece@gmail.com