Designing for Manufacturing Scalability in Clean Energy Research

Huang, Kevin; Li, Liang; Olivetti, Elsa

doi:10.1016/j.joule.2018.07.020

Cited by 14 publications

(12 citation statements)

References 14 publications

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“…Given this range of options, how are promising pathways to large scale integration to be identified? The factors that can govern the manufacturability and scalability of materials dependent technologies, like batteries, are considerably varied 27 . This is further compounded during the transition from lab to production, as the challenges that confront battery scientists and engineers continue to evolve.…”

Section: Introductionmentioning

confidence: 99%

Manufacturing scalability implications of materials choice in inorganic solid-state batteries

Huang

Ceder

Olivetti

2021

Joule

Self Cite

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The pursuit of scalable and manufacturable all-solid-state batteries continues to intensify, motivated by the rapidly increasing demand for safe, dense electrical energy storage. In this Perspective, we describe the numerous, often conflicting, implications of materials choices that have been made in the search for effective mitigations to the interfacial instabilities plaguing solid-state batteries. Specifically, we show that the manufacturing scalability of solid-state batteries can be governed by at least three principal consequences of materials selection: (1) the availability, scaling capacity, and price volatility of the chosen materials' constituents, (2) the manufacturing processes needed to integrate the chosen materials into full cells, and (3) the cell performance that may be practically achieved with the chosen materials and processes. While each of these factors is, in isolation, a pivotal determinant of manufacturing scalability, we show that consideration and optimization of their collective effects and tradeoffs is necessary to more completely chart a scalable pathway to manufacturing. Context & Scale With examples pulled from recent developments in solid-state batteries, we illustrate the consequences of materials choice on materials availability, processing requirements and challenges, and resultant device performance. We demonstrate that while each of these factors is, by itself, essential to understanding manufacturing scalability, joint consideration of all three provides for a more comprehensive understanding of the specific factors that could impede the scale up to production. Much of the recent activity in solidstate battery research has been aimed at mitigating the various interfacial instabilities that currently prevent the fabrication of a low cost, high performance device. With such a wide breadth of options, it can be difficult for researchers to identify the most promising or scalable pathways forward. As such, we aim to empower researchers to make more informed decisions by providing them with insights into how their materials choices are likely to impact the manufacturing scalability of different interfacial mitigation alternatives. In doing so, we hope that generalizable lessons about scalability can be extracted and applied to subsequent challenges in solid-state battery development, thereby accelerating their scale up to manufacturing.

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Section: Introductionmentioning

confidence: 99%

Manufacturing scalability implications of materials choice in inorganic solid-state batteries

Huang

Ceder

Olivetti

2021

Joule

Self Cite

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“…To reduce the generation of waste and the consumption of resources was established a bi-level fuzzy algorithm for a particular case of nuclear power freshwater and the wastewater treatment (Aviso, Tan, Culaba, & Cruz, 2010;Tan, Aviso, Cruz, & Culaba, 2011). Further, it is compulsory implementing the manufacturing scalability of laboratory-derived clean energy technologies (Huang, Li, & Olivetti, 2018). For a sustainable development Cheng (Cheng, 2016) This survey efforts permits improving the manufacturing through quality when is used thermal drilling of galvanized steel sheets.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of green technology thermal drilling process using grey-fuzzy logic method

Kumar

Hynes²,

Pruncu

et al. 2019

Journal of Cleaner Production

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“…Expected changes in emissions of feedstocks and energy sources should naturally be integrated into projections of environmental impact. Importantly, projections should also account for disconnects between R&D-level designs and largescale, commercial processes, as doing otherwise risks neglecting design gaps that hinder scalability (Huang et al, 2018). For high-level and rapid projections, general learning rates that have been shown to cluster around 20% (Wene, 2008) could be applied to CCU, although this study details approaches that can allow for more precise calculations.…”

Section: Learning Curves: Brief Literature Review Current Approachesmentioning

confidence: 99%

Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

et al. 2022

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Comparisons of emerging carbon capture and utilization (CCU) technologies with equivalent incumbent technologies are necessary to support technology developers and to help policy-makers design appropriate long-term incentives to mitigate climate change through the deployment of CCU. In particular, early-stage CCU technologies must prove their economic viability and environmental reduction potential compared to already-deployed technologies. These comparisons can be misleading, as emerging technologies typically experience a drastic increase in performance and decrease in cost and greenhouse gas emissions as they develop from research to mass-market deployment due to various forms of learning. These changes complicate the interpretation of early techno-economic assessments (TEAs) and life cycle assessments (LCAs) of emerging CCU technologies. The effects of learning over time or cumulative production themselves can be quantitatively described using technology learning curves (TLCs). While learning curve approaches have been developed for various technologies, a harmonized methodology for using TLCs in TEA and LCA for CCU in particular is required. To address this, we describe a methodology that incorporates TLCs into TEA and LCA to forecast the environmental and economic performance of emerging CCU technologies. This methodology is based on both an evaluation of the state of the art of learning curve assessment and a literature review of TLC approaches developed in various manufacturing and energy generation sectors. Additionally, we demonstrate how to implement this methodology using a case study on a CO2 mineralization pathway. Finally, commentary is provided on how researchers, technology developers, and LCA and TEA practitioners can advance the use of TLCs to allow for consistent, high-resolution modeling of technological learning for CCU going forward and enable holistic assessments and fairer comparisons with other climate technologies.

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Designing for Manufacturing Scalability in Clean Energy Research

Cited by 14 publications

References 14 publications

Manufacturing scalability implications of materials choice in inorganic solid-state batteries

Manufacturing scalability implications of materials choice in inorganic solid-state batteries

Multi-objective optimization of green technology thermal drilling process using grey-fuzzy logic method

Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

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