Jarod C. Kelly

The significance of Li-ion batteries in electric vehicle life-cycle energy and emissions and recycling's role in its reduction

Three key questions have driven recent discussions of the energy and environmental impacts of automotive lithium-ion batteries. We address each of them, beginning with whether the energy intensity of producing all materials used in batteries or that of battery assembly is greater. Notably, battery assembly energy intensity depends on assembly facility throughput because energy consumption of equipment, especially the dry room, is mainly throughput-independent. Low-throughput facilities therefore will have higher energy intensities than near-capacity facilities. In our analysis, adopting an assembly energy intensity reflective of a low-throughput plant caused the assembly stage to dominate cradle-to-gate battery energy and environmental impact results. Results generated with an at-capacity assembly plant energy intensity, however, indicated cathode material production and aluminium use as a structural material were the drivers. Estimates of cradle-to-gate battery energy and environmental impacts must therefore be interpreted in light of assumptions made about assembly facility throughput. The second key question is whether battery recycling is worthwhile if battery assembly dominates battery cradle-to-gate impacts. In this case, even if recycled cathode materials are less energy and emissions intensive than virgin cathode materials, little energy and environmental benefit is obtained from their use because the energy consumed in assembly is so high. We reviewed the local impacts of metals recovery for cathode materials and concluded that avoiding or reducing these impacts, including SOx emissions and water contamination, is a key motivator of battery recycling regardless of the energy intensity of assembly. Finally, we address whether electric vehicles (EV) offer improved energy and environmental performance compared to internal combustion-engine vehicles (ICV). This analysis illustrated that, even if a battery assembly energy reflective of a low-throughput facility is adopted, EVs consume less petroleum and emit fewer greenhouse gases (GHG) than an ICV on a life-cycle basis. The only scenario in which an EV emitted more GHGs than an ICV was when it used solely coal-derived electricity as a fuel source. SOx emissions, however, were up to four times greater for EVs than ICVs. These emissions could be reduced through battery recycling.

A Framework for the Integrated Optimization of Charging and Power Management in Plug-in Hybrid Electric Vehicles

This paper develops a dynamic programming (DP)-based framework for simultaneously optimizing the charging and power management of a plug-in hybrid electric vehicle (PHEV). These two optimal control problems relate to activities of the PHEV on the electric grid (i.e., charging) and on the road (i.e., power management). The proposed framework solves these two problems simultaneously to avoid loss of optimality resulting from solving them separately. The framework furnishes optimal trajectories of PHEV states and control inputs over a 24-h period. We demonstrate the framework for 24-h scenarios with two driving trips and different power grid generation mixes. The results show that addressing the aforementioned optimization problems simultaneously can elucidate valuable insights. For example, for the chosen daily driving scenario, grid generation mixes, and optimization objective, it is shown that it is not always optimal to completely charge a battery before each driving trip. In addition, reduction in CO2 resulting from the synergistic interaction of PHEVs with an electric grid containing a significant amount of wind power is studied. The main contribution of this paper to the literature is a framework that makes it possible to evaluate tradeoffs between charging and on-road power management decisions.

Incorporating user shape preference in engineering design optimisation

Form versus function is a classic design debate. In this article, a practical approach to combine shape preference (form) and engineering performance (function) under a design optimisation paradigm is proposed and implemented. This synthesis allows form and function to be considered in quantitative terms during the design process to identify shapes that can benefit overall product design. Two methods of preference modelling, PREFMAP analysis and conjoint analysis, are used to model user preference as a mathematical function of design variables. Physics-based models express engineering performance as functions of the same design variables. The models are combined in an optimisation formulation to capture the design trade-offs involved. A simple illustrative study of bottle design is presented. A divergence is found between the most preferred shape and the technically optimal shape; a Pareto frontier provides insight into the trade-off between these two goals.

Impacts of Vehicle Weight Reduction via Material Substitution on Life-Cycle Greenhouse Gas Emissions

This study examines the vehicle-cycle and vehicle total life-cycle impacts of substituting lightweight materials into vehicles. We determine part-based greenhouse gas (GHG) emission ratios by collecting material substitution data and evaluating that alongside known mass-based GHG ratios (using and updating Argonne National Laboratorys GREET model) associated with material pair substitutions. Several vehicle parts are lightweighted via material substitution, using substitution ratios from a U.S. Department of Energy report, to determine GHG emissions. We then examine fuel-cycle GHG reductions from lightweighting. The fuel reduction value methodology is applied using FRV estimates of 0.15-0.25, and 0.25-0.5 L/(100km·100 kg), with and without powertrain adjustments, respectively. GHG breakeven values are derived for both driving distance and material substitution ratio. While material substitution can reduce vehicle weight, it often increases vehicle-cycle GHGs. It is likely that replacing steel (the dominant vehicle material) with wrought aluminum, carbon fiber reinforced plastic (CRFP), or magnesium will increase vehicle-cycle GHGs. However, lifetime fuel economy benefits often outweigh the vehicle-cycle, resulting in a net total life-cycle GHG benefit. This is the case for steel replaced by wrought aluminum in all assumed cases, and for CFRP and magnesium except for high substitution ratio and low FRV.

Fuel Economy and Greenhouse Gas Emissions Labeling for Plug‐In Hybrid Vehicles from a Life Cycle Perspective

Fuel economy has been an effective indicator of vehicle greenhouse gas (GHG) emissions for conventional gasoline‐powered vehicles due to the strong relationship between fuel economy and vehicle life cycle emissions. However, fuel economy is not as accurate an indicator of vehicle GHG emissions for plug‐in hybrid (PHEVs) and pure battery electric vehicles (EVs). Current vehicle labeling efforts by the U.S. Environmental Protection Agency (EPA) and Department of Transportation have been focused on providing energy and environmental information to consumers based on U.S. national average data. This article explores the effects of variations in regional grids and regional daily vehicle miles traveled (VMT) on the total vehicle life cycle energy and GHG emissions of electrified vehicles and compare these results with information reported on the label and on the EPAs fuel economy Web site. The model results suggest that only 25% of the life cycle emissions from a representative PHEV are reflected on current vehicle labeling. The results show great variation in total vehicle life cycle emissions due to regional grid differences, including an approximately 100 gram per mile life cycle GHG emissions difference between the lowest and highest electric grid regions and up to a 100% difference between the state‐specific emission values within the same electric grid regions. Unexpectedly, for two regional grids the life cycle GHG emissions were higher in electric mode than in gasoline mode. We recommend that labels include stronger language on their deficiencies and provide ranges for GHG emissions from vehicle charging in regional electricity grids to better inform consumers.

Evaluating the life cycle greenhouse gas emissions from a lightweight plug-in hybrid electric vehicle in a regional context

Life cycle assessment provides a comprehensive framework to evaluate the total greenhouse gas (GHG) emissions from electrified vehicles (EVs) and their potential for GHG reduction as they gain market share. The magnitude of EVs¿ contribution will depend on the specific combinations of fueling strategies and the other vehicle technologies adopted. For instance, the GHG emissions from plug-in electric vehicles (PHEVs) could increase life cycle emissions if the vehicle is driven in a region with a high carbon grid. Also, vehicle lightweighting with lighter, high strength materials decreases use phase emissions but can increase emissions throughout the material production process. This research develops a method to evaluate life cycle emissions from a lightweight PHEV for use in diverse electric fueling regions. A life cycle model is constructed using: 1) Autonomie, a vehicle simulation software, 2) GREET, a vehicle and fuel cycle model, and 3) eGrid, a database with regional information about the US electric power sector. The life cycle analysis demonstrates the importance of considering vehicle production emissions when using energy intensive materials to reduce mass from a vehicle, since life cycle GHGs for the 10% lightweight carbon fiber vehicle are higher than the baseline steel vehicle. However, as a higher percentage of steel is replaced with carbon fiber, total life cycle GHGs decrease. Regional impacts of the electric grid are shown to be significant, with the potential to decrease life cycle emissions by more than four times the reductions possible with the best-case lightweight scenario.

A framework for the integrated optimization of charging and power management in plug-in hybrid electric vehicles

This paper develops a dynamic programming (DP) based framework for simultaneously optimizing the charging and power management of a plug-in hybrid electric vehicle (PHEV). These two optimal control problems relate to activities of the PHEV on the electric grid (i.e., charging) and on the road (i.e., power management). The proposed framework solves these two problems simultaneously in order to avoid the loss of optimality resulting from solving them separately. The framework furnishes optimal trajectories of the PHEV states and control inputs over a 24-hour period. We demonstrate the framework for 24-hour scenarios with two driving trips and different power grid generation mixes. The results show that addressing the above optimization problems simultaneously can elucidate valuable insights for certain combinations of daily driving scenarios, grid generation mixes, and optimization objectives. For example, in one of the cases considered, the grid produces higher CO2 per unit energy between 3AM and 8AM. This causes the optimal PHEV state and control input trajectory to refrain from completely charging the PHEVs battery in the early morning, and judiciously combine electricity and gasoline while driving. The papers main contribution to the literature is a framework that makes it possible to evaluate tradeoffs such as this one.

Evidence for using Interactive Genetic Algorithms in shape preference assessment

Preferences for subjective design qualities, such as shape, are difficult to capture and relate to engineering specifications. The present paper uses Interactive Evolutionary Systems (IES) to locate a human users most preferred cola bottle shape among a set of parameterised bottle shapes. Several researchers have used IES to identify user preference, but have never independently confirmed that preference. In the present paper, participants used the IGA to select their favourite design from a small design space. The method of paired comparisons was used to characterise preference over the entire design space, showing a 91% agreement with IGA most preferred selections.

Determining the Effect of Users’ Mobile Phone on Design Preference via Interactive Genetic Algorithms

This study uses an Interactive Genetic Algorithm (IGA), a design space searching method, to determine the degree to which a user’s current mobile phone impacts their design preference, and how features in a product can change preference. IGAs mimic evolution by iteratively converging towards a design while exploring a design space through random mutations. 20 participants, 10 current Apple iPhone owners, and 10 non-iPhone owners were asked to use a web-based IGA tool to design touchscreen and non-touchscreen phones for dialing use only. Similar to other IGA mobile phone work (Nathan-Roberts & Liu 2010), the IGA varied screen size, button spacing, and phone radius independently. Results showed iPhone users, and non-iPhone users did have different design preferences, but that there was a bigger difference between touchscreen phone owners (iPhone and non-iPhone touchscreen phones), and non-touchscreen phone owners. Overall participants had significantly different preferences for touchscreen and non-touchscreen designs for all variables except for the vertical button spacing, and phone radius. This work is part of a larger research study of aesthetic ergonomics of mobile phones, specifically looking at usability, and the capacity of users to combine multiple goals in design. Future research needs are discussed, including further testing the effect of non-iPhone touchscreen phone ownership.

Life Cycle Assessment of Offshore Wind Farm Siting: Effects of Locational Factors, Lake Depth, and Distance from Shore

Summary According to previous studies, the life cycle energy intensity of an offshore wind farm (OWF) varies between 0.03 and 0.13 megawatt-hours (MWh) of primary energy for each MWh of electricity generated. The variation in these life cycle energy intensity studies, after normalizing for capacity factor and life span, is significantly affected by OWF location because of geographical properties, namely, wind speed and water depth. To improve OWF siting, this study investigates how an OWFs distance from shore and geographical location impacts its environmental benefit. A process-based life cycle assessment is conducted to compare 20 OWF siting scenarios in Michigans Great Lakes for their cumulative fossil energy demand, global warming potential, and acidification potential. Each scenario (four lake locations at five offshore distances) has unique foundation, transmission, installation, and operational requirements based on site characteristics. The results demonstrate that the cumulative environmental burden from an OWF is most significantly affected by (1) water depth, (2) distance from shore, and (3) distance to power grid, in descending order of importance, if all other site-relevant variables are held constant. The results also show that when OWFs are sited further offshore, the benefit of increased wind energy generation does not necessarily outweigh the increase in negative environmental impacts. This suggests that siting OWF nearer to shore may result in a better life cycle environmental performance. Finally, we demonstrate how much an OWFs environmental burdens can be reduced if the OWF system is either recycled, transported a shorter distance, or manufactured in a region with a high degree of renewable energy on the grid.