Lithium-ion (Li-ion) batteries have experienced a massive rise in popularity since their initial commercial introduction in 1991. Their implementation into several economic sectors has been instrumental in achieving large-scale electrification, with the eventual goal of sector-wide decarbonization. However, there has been little consensus on how to report the impacts associated with Li-ion batteries, and the standard of modeling the use-phase of Li-ion technologies often relies on broad assumptions, particularly with the future prices of Li-ion batteries. Additionally, there is little consensus on whether the integration of Li-ion technologies provides net positive impacts in several sectors. My research aims to provide recommendations for life-cycle assessments (LCA) on Li-ion technologies with the intent of helping future studies be more interpretable, representative, and impactful, as well as critically examine the assumptions used to forecast Li-ion prices. I then employ LCA and technoeconomic analysis (TEA) to model the climate, human health, and economic impacts of Li-ion technologies serving in peaker replacement and heavy-duty long-haul freight roles. The results from these studies show that the relative net impact of using Li-ion batteries in these roles can be positive or negative depending on several factors. Greater details of these studies are provided below.
Life-cycle Assessment Consideration for Batteries and Battery MaterialsRechargeable batteries are necessary for the decarbonization of the energy systems, but life-cycle environmental impact assessments have not achieved consensus on the environmental impacts of producing these batteries. Nonetheless, life cycle assessment (LCA) is a powerful tool to inform the development of better-performing batteries with reduced environmental burden. This review explores common practices in lithium-ion battery LCAs and makes recommendations for how future studies can be more interpretable, representative, and impactful. First, LCAs should focus analyses of resource depletion on long-term trends toward more energy and resource-intensive material extraction and processing rather than treating known reserves as a fixed quantity being depleted. Second, future studies should account for extraction and processing operations that deviate from industry best-practices and may be responsible for an outsized share of sector-wide impacts, such as artisanal cobalt mining. Third, LCAs should explore at least 2–3 battery manufacturing facility scales to capture size- and throughput-dependent impacts such as dry room conditioning and solvent recovery. Finally, future LCAs must transition away from kg of battery mass as a functional unit and instead make use of kWh of storage capacity and kWh of lifetime energy throughput.
Temporal Variations in Learning Rates of Li-ion Technologies: Insights for Price Forecasting and Policy through Segmented Regression AnalysisSince their initial development in 1991, Li-ion cell prices have decreased by over 97%. However, decades of lithium-ion battery cost reductions are often represented by a single learning rate in an experience curve. Learning rates are not inherently constant, however, and changes in learning rates can have dramatic impacts on cost forecasts and subsequent policy and investment decisions. This analysis is the first study to employ segmented regression to describe how learning rates have historically changed for lithium-ion technologies in different periods of time. Additionally, the distinctions between cost and price data are highlighted to emphasize the value of allowing learning rates to vary over time when performing experience curves for lithium-ion batteries. This analysis identifies past changes in the learning rate of lithium-ion cells, modules, and installations: for lithium-ion cells, the learning rate was 4% through 1997, 34% through 2003, and 24.4% onward. This dynamic learning behavior is explained as periods of market development, shakeout, and stabilization respectively. By allowing greater flexibility in the experience curve, a secondary shakeout period emerges from 2013 onward, with a learning rate of 40.9%. While this secondary shakeout has less statistical significance, we find that it aligns well with the growth of Li-ion markets and may emerge as significant as more data becomes available. Modules and installed costs follow a similar trend, with low learning (6-8% for 4-6 years) followed by an acceleration to 31-37%. The importance of capturing these historical variances is highlighted by demonstrating the impact of varying learning rates on forecasted lithium-ion cell prices through scenario analysis. We observe that price forecasts are much more sensitive to the uncertainty in learning rate compared to the uncertainty in technology deployment. Utilizing multiple learning rates from a segmented experience curve can enhance future Li-ion technology price projections, improving both price forecasting and policy development.
Private and External Costs and Benefits of Replacing High-Emitting Peaker Plants with BatteriesFalling costs of Li-ion batteries have made them attractive for grid-scale energy storage applications. Energy storage will become increasingly important as intermittent renewable generation and more frequent extreme weather events put stress on the electricity grid. Environmental groups across the United States are advocating for the replacement of the highest-emitting power plants, which run only at times of peak demand, with Li-ion battery systems. We analyze the life-cycle cost, climate, and human health impacts of replacing the 19 highest-emitting peaker plants in California with Li-ion battery energy storage systems (BESS). Our results show that designing Li-ion BESS to replace peaker plants puts them at an economic disadvantage, even if facilities are only sized to meet 95% of the original plants’ load events and are free to engage in arbitrage. However, five of 19 potential replacements do achieve a positive net present value after including monetized climate and human health impacts. These BESS cycle far less than typical front-of-the-meter batteries and rely on the frequency regulation market for most of their revenue. All projects offer net air pollution benefits but increase net greenhouse gas emissions due to electricity demand during charging and upstream emissions from battery manufacturing.
Private and External Costs and Benefits of Electrifying Heavy-Duty Long-Haul Trucking with Li-ion BatteriesThe electrification of long-haul heavy-duty vehicles (HDVs) is necessary for the decarbonization of the transportation sector in the United States, but there is no clear technological pathway to replace the diesel internal combustion engine enabling this transport mode. Li-ion batteries have emerged as a popular candidate when exploring options to electrify HDVs, largely due to the rapidly growing popularity of Li-ion battery passenger electric vehicles and decreasing Li-ion battery prices. While many studies point to the climate and human health benefits that will arise from replacing diesel HDVs with Li-ion HDVs, other studies claim that technological limitations will make Li-ion HDVs economically inviable for long-haul freight. We use life-cycle assessment and technoeconomic analysis to model the total ownership cost, climate, and human health impacts associated with replacing a diesel Class 8 truck performing long-haul freight with a Li-ion Class 8 truck in the United States. Our results show that when including monetized contributions to global warming potential and human health burden, Li-ion Class 8 trucks in long-haul freight have greater lifetime costs per mile than diesel Class 8 trucks due to the high price and specific energy of Li-ion batteries, as well as high costs associated with the use of charging infrastructure. Additionally, the current use of Li-ion Class 8 trucks results in marginal improvements to social impacts relative to diesel Class 8 trucks under a high renewable energy cost scenario, but worse social impacts under a low renewable cost scenario. However, by 2035, the social impacts of Li-ion Class 8 trucks are substantially less than diesel Class 8 trucks under both renewable energy cost scenarios as more renewable energy is integrated into the electricity grid.