(398b) Designing a Li-Ion Battery Supply Chain Under Uncertainty for Nature-Positive Decisions | AIChE

(398b) Designing a Li-Ion Battery Supply Chain Under Uncertainty for Nature-Positive Decisions


Xue, Y. - Presenter, The Ohio State University
Bakshi, B., Ohio State University
The last decade has seen the highest levels of net anthropogenic greenhouse gas emissions, resulting in extreme events such as wildfires, heat waves, and floods. To address this, global goals such as "Nature-positive by 2030" and "Net-zero CO2 by 2050" have been proposed [1] . The transportation sector is responsible for the fastest-growing emissions, and electric vehicles (EVs) have emerged as a promising solution [2]. The urgent need to combat global warming and climate change also requires the development of sustainable supply chains that reduce greenhouse gas (GHG) emissions. In this study, from environmental, economic, and social aspects, we designed a Li-ion batteries supply chain which are essential components of EVs, integrating life cycle assessment (LCA), ecosystem services (ESs), and multiobjective stochastic optimization. Minimizing the transgression level of ecological and social thresholds, total cost of the supply chain are three objectives. The environmental objective function is defined by absolute environmental sustainability (AES) metric which compares environmental impacts against ecological thresholds. The environmental impact is quantified by a cradle-to-grave LCA of the Li-ion battery, from raw material extraction to cell assembly to end-of-life. We integrate a multiscale AES assessment method based on ecosystem services (ESs) to assess ecological thresholds for each process along the Li-ion battery supply chain. For social threshold, also referring to socially just space, we calculate a minimum threshold of necessary goods and services to meet basic food, energy, and water needs for each process. A two-stage stochastic model is proposed to account for uncertainties [3-5] in demand of Li-ion batteries and dynamic nature of ecosystem services. Bender’s decomposition method is applied to solve this problem. The final pareto-optimal plane reveals the tradeoff between the economic, environmental, and social dimensions.

Previous studies on environmentally sustainable or green supply chain design have primarily focused on minimizing environmental impacts or defining environmental constraints [6]. Sustainability assessment methods, such as LCA and ecological footprint, have been integrated into supply chain design models. However, from an absolute sustainability point of view, this is not sufficient, as nature's capacity to absorb emissions or provide resources is limited and is ignored in most studies [7]. Therefore, sustainable development and global goals require that human impact does not exceed ecological thresholds which require AES metrics. These metrics compare environmental impacts versus ecological thresholds. Our environmental focus is on CO2 emission versus carbon sequestration amount which is defined as the objective function. To quantify the ecological thresholds for each process along the Li-ion battery supply chain, a multiscale method based on ecosystem services (ESs) is implemented. This method quantifies the capacity of ecosystems from different spatial scales as ecological thresholds using biophysical models. It also considers public and private ownership of ESs separately, avoiding issues such as subjectiveness during downscaling and ignoring the heterogeneity of ecological thresholds that are present in current Planetary Boundary-based (PB) methods [8].

The study incorporates the life cycle objective into a country-level supply chain design that includes four major steps in manufacturing Li-ion batteries: cathode production, cell production, non-cell materials production, and battery pack assembly. A total of 13 processes are included in the system boundary. The functional unit is defined as the annual demand of Li-ion batteries for medium-sized EVs, with reference to Tesla. The overall CO2 emission consists of manufacturing emissions and transportation emissions. The objective function is defined by comparing the overall CO2 emission of the supply chain to its ecological threshold. The location of suppliers and transportation modes are the major decision variables, while life cycle flow balance, production capacity, and mass balance are the main constraints. Uncertainties in this supply chain are captured by a two-stage stochastic optimization model. One part of uncertainties comes from the market demand of Li-ion batteries modeled by three scenarios: high, low, and normal. Another major source of uncertainty stems from ecological thresholds which are values of ecosystem services. Ecosystems composed of living organisms are dynamic systems that are constantly changing overtime. In this study, carbon sequestration rates of ecosystems change seasonally, and the carbon sequestration abilities of ecosystems differ across different species. Similarly, the social threshold is defined as the required emissions from food production and electricity generation to meet the energy and caloric intake thresholds for the entire population. A stochastic model is applied to capture the demand variations and ecosystem services availability in different scenarios. Bender's decomposition method is used to solve the problem, decomposing it into master and subproblems, with additional constraints added at each iteration. The multiobjective optimization problem is solved with a ε-constraint method to show tradeoffs among environmental, social, and economic objectives.

The optimal absolutely sustainable and socially safe supply chain is compared with current practical supply chains, and the results are analyzed to find hotspots and identify potential improvements towards sustainable global goals. The contribution of this work includes modeling, methodology, and application. The two-stage stochastic model is incorporated with LCA and multiscale AES framework, designing a supply chain that meets the requirements of transitioning to a "nature-positive" planet.


1. Global Goal for nature. https://www.naturepositive.org/. Accessed: 2022-12-13.

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